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+DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# TODO: Comment the next line if you want to checkin your web deploy settings +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# NuGet Packages +*.nupkg +# The packages folder can be ignored because of Package Restore +**/packages/* +# except build/, which is used as an MSBuild target. +!**/packages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/packages/repositories.config + +# Windows Azure Build Output +csx/ +*.build.csdef + +# Windows Store app package directory +AppPackages/ + +# Others +*.[Cc]ache +ClientBin/ +[Ss]tyle[Cc]op.* +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.pfx +*.publishsettings +node_modules/ +bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm + +# SQL Server files +*.mdf +*.ldf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings + +# Microsoft Fakes +FakesAssemblies/ + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# User-specific configuration that should not be place under source control +*.azurePubXml + +*-local.config +*.ConsoleHost.config +settings.config + +#Java + +*.class + +# Mobile Tools for Java (J2ME) +.mtj.tmp/ + +# Package Files # +*.jar +*.war +*.ear + +# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml +hs_err_pid* + +# Azure diagnostics +*.wadcfgx + +# docs +log/ +obj/ +_site/ +.optemp/ +_themes*/ + +.openpublishing.buildcore.ps1 \ No newline at end of file diff --git a/.local-web-server.json b/.local-web-server.json new file mode 100644 index 00000000000..10bb09416bf --- /dev/null +++ b/.local-web-server.json @@ -0,0 +1,15 @@ +{ + "port": 8000, + "directory": "_site", + "forbid": [], + "rewrite": [ + { + "from": "/azure/architecture/*", + "to": "/$1" + }, + { + "from": "/azure/architecture", + "to": "/azure/architecture/" + } + ] +} \ No newline at end of file diff --git a/.openpublishing.build.ps1 b/.openpublishing.build.ps1 new file mode 100644 index 00000000000..aadef762022 --- /dev/null +++ b/.openpublishing.build.ps1 @@ -0,0 +1,17 @@ +param( + [string]$buildCorePowershellUrl = "https://opbuildstorageprod.blob.core.windows.net/opps1container/.openpublishing.buildcore.ps1", + [string]$parameters +) +# Main +$errorActionPreference = 'Stop' + +# Step-1: Download buildcore script to local +echo "download build core script to local with source url: $buildCorePowershellUrl" +$repositoryRoot = Split-Path -Parent $MyInvocation.MyCommand.Definition +$buildCorePowershellDestination = "$repositoryRoot\.openpublishing.buildcore.ps1" +Invoke-WebRequest $buildCorePowershellUrl -OutFile "$buildCorePowershellDestination" + +# Step-2: Run build core +echo "run build core script with parameters: $parameters" +& "$buildCorePowershellDestination" "$parameters" +exit $LASTEXITCODE diff --git a/.openpublishing.publish.config.json b/.openpublishing.publish.config.json new file mode 100644 index 00000000000..f2b2ea29166 --- /dev/null +++ b/.openpublishing.publish.config.json @@ -0,0 +1,47 @@ +{ + "need_generate_pdf_url_template": false, + "need_generate_intellisense": false, + "branch_target_mapping": { + "docs": ["Publish", "PDF"] + }, + "docsets_to_publish": [ + { + "docset_name": "azure-architecture-center", + "build_source_folder": "docs", + "build_output_subfolder": "azure-architecture-center", + "locale": "en-us", + "version": 0, + "open_to_public_contributors": false, + "type_mapping": { + "Conceptual": "Content", + "ManagedReference": "Content", + "RestApi": "Content" + }, + "build_entry_point": "docs", + "template_folder": "_themes" + } + ], + "Targets": { + "Pdf": { + "template_folder": "_themes.pdf" + } + }, + "notification_subscribers": [], + "branches_to_filter": [], + "skip_source_output_uploading": false, + "need_preview_pull_request": false, + "enable_incremental_build": true, + "dependent_repositories": [ + { + "path_to_root": "_themes", + "url": "https://github.com/Microsoft/templates.docs.msft", + "branch": "master", + "branch_mapping": {} + }, + { + "path_to_root": "_themes.pdf", + "url": "https://github.com/Microsoft/templates.docs.msft.pdf", + "branch": "master" + } + ] +} \ No newline at end of file diff --git a/.openpublishing.redirection.json b/.openpublishing.redirection.json new file mode 100644 index 00000000000..224f9e9dddb --- /dev/null +++ b/.openpublishing.redirection.json @@ -0,0 +1,12 @@ +{ + "redirections": [ + { + "source_path": "docs/guidance.md", + "redirect_url": "/azure/architecture" + }, + { + "source_path": "docs/guidance-architecture.md", + "redirect_url": "/azure/architecture" + } + ] +} \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 00000000000..45cff81d8af --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,21 @@ +{ + // Use IntelliSense to learn about possible Node.js debug attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "type": "node", + "request": "launch", + "name": "Launch Program", + "program": "${workspaceRoot}\\build\\build.js", + "cwd": "${workspaceRoot}" + }, + { + "type": "node", + "request": "attach", + "name": "Attach to Process", + "port": 5858 + } + ] +} \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 00000000000..3421b95e9a2 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,8 @@ +// Place your settings in this file to overwrite default and user settings. +{ + "files.exclude": { + "**/.optemp": true, + "**/_themes": true, + "log": true + }, +} \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000000..e056e7c3ec5 --- /dev/null +++ b/LICENSE @@ -0,0 +1,395 @@ +Attribution 4.0 International + +======================================================================= + +Creative Commons Corporation ("Creative Commons") is not a law firm and +does not provide legal services or legal advice. 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If the provision + cannot be reformed, it shall be severed from this Public License + without affecting the enforceability of the remaining terms and + conditions. + + c. No term or condition of this Public License will be waived and no + failure to comply consented to unless expressly agreed to by the + Licensor. + + d. Nothing in this Public License constitutes or may be interpreted + as a limitation upon, or waiver of, any privileges and immunities + that apply to the Licensor or You, including from the legal + processes of any jurisdiction or authority. + + +======================================================================= + +Creative Commons is not a party to its public +licenses. Notwithstanding, Creative Commons may elect to apply one of +its public licenses to material it publishes and in those instances +will be considered the “Licensor.” The text of the Creative Commons +public licenses is dedicated to the public domain under the CC0 Public +Domain Dedication. Except for the limited purpose of indicating that +material is shared under a Creative Commons public license or as +otherwise permitted by the Creative Commons policies published at +creativecommons.org/policies, Creative Commons does not authorize the +use of the trademark "Creative Commons" or any other trademark or logo +of Creative Commons without its prior written consent including, +without limitation, in connection with any unauthorized modifications +to any of its public licenses or any other arrangements, +understandings, or agreements concerning use of licensed material. For +the avoidance of doubt, this paragraph does not form part of the +public licenses. + +Creative Commons may be contacted at creativecommons.org. \ No newline at end of file diff --git a/LICENSE-CODE b/LICENSE-CODE new file mode 100644 index 00000000000..b17b032a430 --- /dev/null +++ b/LICENSE-CODE @@ -0,0 +1,17 @@ +The MIT License (MIT) +Copyright (c) Microsoft Corporation + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +associated documentation files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, +and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, +subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial +portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, +WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 00000000000..32ff2ad4e12 --- /dev/null +++ b/README.md @@ -0,0 +1,41 @@ +# patterns & practices + +Some of the content is generated using a build script. +* `index.md` is completely generated. You should not edit it directly. Instead, edit `index.yml` and `index.liquid.md`, then run the build script. +* The metadata for series is generated and copied into the relevant docs. The metadata is derived from the corresponding toc.md. You should not directly editing any of the yml that starts with `pnp.series.`. + +In order to run the build script, first install the supporting packages: + +```bash +npm install +``` + +To generate the files, you can run the `start` task: +```bash +npm start +``` +Or run the script directly: +```bash +node .\build\build.js +``` + +For convenience, you can build, commit, and push with a single command: +```bash +npm run build +``` + +## Legal Notices +Microsoft and any contributors grant you a license to the Microsoft documentation and other content +in this repository under the [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/legalcode), +see the [LICENSE](LICENSE) file, and grant you a license to any code in the repository under the [MIT License](https://opensource.org/licenses/MIT), see the +[LICENSE-CODE](LICENSE-CODE) file. + +Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation +may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. +The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. +Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653. + +Privacy information can be found at https://privacy.microsoft.com/en-us/ + +Microsoft and any contributors reserve all others rights, whether under their respective copyrights, patents, +or trademarks, whether by implication, estoppel or otherwise. diff --git a/build/blueprints/build.js b/build/blueprints/build.js new file mode 100644 index 00000000000..6198a765344 --- /dev/null +++ b/build/blueprints/build.js @@ -0,0 +1,55 @@ +var path = require('path'); +var fs = require('fs'); +var series = require('./series'); + +var yamlFront = require('yaml-front-matter') +var Liquid = require('shopify-liquid'); + +var engine = Liquid({ + root: path.resolve(__dirname, 'templates'), + extname: '.liquid' +}); + +module.exports = function () { + var basePath = path.resolve(__dirname, `../../docs/blueprints/`); + var catalog = { series: [] }; + + // render series overviews + var seriesTemplate = engine.parse("{%- include 'series-overview' -%}"); + [ + 'virtual-machines-linux', + 'virtual-machines-windows', + 'app-service', + 'identity', + 'hybrid-networking', + 'dmz' + ].forEach(slug => { + var seriesPath = path.resolve(basePath, `${slug}`); + var model = series(slug, seriesPath); + + var ymlPath = path.resolve(seriesPath, `series.yml`); + var content = fs.readFileSync(ymlPath, 'utf8'); + var yml = yamlFront.loadFront(content); + + Object.assign(model, yml); + + model.next = model.articles[0].url; + model.path = slug; + catalog.series.push(model); + + var outputhPath = path.resolve(seriesPath, `index.md`); + engine.render(seriesTemplate, { series: model }) + .then(markdown => fs.writeFile(outputhPath, markdown)); + }); + + // render main index + ['index'].forEach(x => { + var templatePath = path.resolve(basePath, `${x}.liquid.md`); + var outputhPath = path.resolve(basePath, `${x}.md`); + var template = engine.parse(fs.readFileSync(templatePath, 'utf8')); + + console.log(x); + engine.render(template, catalog) + .then(markdown => fs.writeFile(outputhPath, markdown)); + }); +} \ No newline at end of file diff --git a/build/blueprints/createSeriesModel.js b/build/blueprints/createSeriesModel.js new file mode 100644 index 00000000000..d3c6940f80c --- /dev/null +++ b/build/blueprints/createSeriesModel.js @@ -0,0 +1,53 @@ +const toc = require('./toc'); + +function adorn(model) { + var articles = model.articles; + + articles.forEach((article, i) => { + var prevIndex = i - 1; + var nextIndex = i + 1; + if (prevIndex >= 0) { + article.prev = articles[prevIndex].url; + } else { + article.prev = './index'; + } + if (nextIndex < articles.length) { + article.next = articles[nextIndex].url; + } + }); +} + +function resolveUrl(url) { + return url.replace('.md', '').replace('./', ''); +} + +function getArticleByFile(file) { + return this.articles.find(x => (x.filePath.indexOf(file) >= 0)); +} + +module.exports = (slug, path) => { + + var contents = toc(path); + + var model = { + root: slug, + title: contents[0].title, + description: null, + articles: [], + getArticleByFile: getArticleByFile + }; + + contents.shift(); + + model.articles = contents.map(x => { + return { + title: x.title, + filePath: x.filePath, + url: resolveUrl(x.filePath) + }; + }); + + adorn(model); + + return model; +} \ No newline at end of file diff --git a/build/blueprints/series.js b/build/blueprints/series.js new file mode 100644 index 00000000000..3574ad6e004 --- /dev/null +++ b/build/blueprints/series.js @@ -0,0 +1,50 @@ +var createSeriesModel = require('./createSeriesModel'); +var lec = require('line-ending-corrector').LineEndingCorrector.correctSync; +var yamlFront = require('yaml-front-matter'); +var path = require('path'); +var fs = require('fs'); +var os = require('os'); + +module.exports = function (seriesSlug, seriesPath) { + console.log(`series: ${seriesSlug}`); + + var tocPath = path.resolve(seriesPath, `toc.md`); + var toc = fs.readFileSync(tocPath, 'utf8'); + + var series = createSeriesModel(seriesSlug, seriesPath); + + var files = fs.readdirSync(seriesPath); + files.forEach(file => { + if (file.substr(-3) != ".md") return; + if (['index.md', 'toc.md'].indexOf(file) > -1) return; + + var filePath = path.resolve(seriesPath, file); + var content = fs.readFileSync(filePath, 'utf8'); + + var yml = yamlFront.loadFront(content); + var original = yml.__content; + delete yml.__content; + + var article = series.getArticleByFile(file); + if (!article) return; + + var cardTitle = article.title; + + Object.assign(article, yml); + + + if (article.prev) { + yml['pnp.series.prev'] = article.prev; + } + if (article.next) { + yml['pnp.series.next'] = article.next; + } + yml['pnp.series.title'] = series.title; + yml['cardTitle'] = cardTitle; + + var updated = lec('---' + os.EOL + yamlFront.dump(yml) + '---' + original, { eolc: 'CRLF' }); + fs.writeFileSync(filePath, updated[1]); + }); + + return series; +} \ No newline at end of file diff --git a/build/blueprints/templates/series-overview.liquid b/build/blueprints/templates/series-overview.liquid new file mode 100644 index 00000000000..4d5bdf23ec3 --- /dev/null +++ b/build/blueprints/templates/series-overview.liquid @@ -0,0 +1,17 @@ +--- +title: {{ series.title }} | Architectural Blueprints +description: {{ series.summary }} +layout: LandingPage +pnp.series.title: {{ series.title }} +pnp.series.next: {{ series.next }} +--- + + +# Series overview +[!INCLUDE [header](../../_includes/header.md)] + +{{ series.description }} + +{% include 'series' %} + +{{ series.considerations }} \ No newline at end of file diff --git a/build/blueprints/templates/series.liquid b/build/blueprints/templates/series.liquid new file mode 100644 index 00000000000..fea81cf7121 --- /dev/null +++ b/build/blueprints/templates/series.liquid @@ -0,0 +1,21 @@ + \ No newline at end of file diff --git a/build/blueprints/toc.js b/build/blueprints/toc.js new file mode 100644 index 00000000000..30a4df2a9db --- /dev/null +++ b/build/blueprints/toc.js @@ -0,0 +1,25 @@ +const path = require('path'); +const fs = require('fs'); + +module.exports = folder => { + const articleRE = /^#+ \[([a-zA-Z-\s]*)\]\(([\.\/a-zA-Z-]*)\)/igm; + + const tocPath = path.resolve(folder, `toc.md`); + const content = fs.readFileSync(tocPath, 'utf8'); + + var output = []; + + // extract the main articles from the TOC + var result = articleRE.exec(content); + // first hit _should_ be the series overview + // so we'll ignore it and move on... + + while ((result = articleRE.exec(content)) !== null) { + output.push({ + title: result[1].trim(), + filePath: result[2] + }); + } + + return output; +} \ No newline at end of file diff --git a/build/build.js b/build/build.js new file mode 100644 index 00000000000..ffb5dbf5268 --- /dev/null +++ b/build/build.js @@ -0,0 +1,5 @@ +const blueprints = require('./blueprints/build'); +const main = require('./main/build'); + +main(); +blueprints(); \ No newline at end of file diff --git a/build/main/build.js b/build/main/build.js new file mode 100644 index 00000000000..70fabde412e --- /dev/null +++ b/build/main/build.js @@ -0,0 +1,32 @@ +var path = require('path'); +var fs = require('fs'); + +var yamlFront = require('yaml-front-matter'); +var Liquid = require('shopify-liquid'); + +var series = require('./series'); + +var engine = Liquid({ + root: path.resolve(__dirname, 'templates'), + extname: '.liquid' +}); + +module.exports = function () { + var dataPath = path.resolve(__dirname, '../../docs/index.yml'); + var content = fs.readFileSync(dataPath, 'utf8'); + var yml = yamlFront.loadFront(content); + + ['index'].forEach(x => { + console.log(`templated page: ${x}`); + var templatePath = path.resolve(__dirname, `../../docs/${x}.liquid.md`); + var outputhPath = path.resolve(__dirname, `../../docs/${x}.md`); + var template = engine.parse(fs.readFileSync(templatePath, 'utf8')); + + return engine.render(template, yml) + .then(function (markdown) { + fs.writeFile(outputhPath, markdown); + }); + }); + + ['multitenant-identity', 'elasticsearch'].forEach(slug => series(slug)); +} diff --git a/build/main/createSeriesModel.js b/build/main/createSeriesModel.js new file mode 100644 index 00000000000..eb3ba809348 --- /dev/null +++ b/build/main/createSeriesModel.js @@ -0,0 +1,86 @@ +var commonmark = require('commonmark'); + +function getHeadingText(node) { + var walker = node.walker(); + while ((event = walker.next())) { + node = event.node; + if (event.entering && node.type === 'text') { + return node._literal; + } + } + return null; +} + +function getHeadingLink(node) { + var walker = node.walker(); + while ((event = walker.next())) { + node = event.node; + if (event.entering && node._destination) { + return node._destination; + } + } + return null; +} + +function adorn(model) { + var articles = model.articles; + + articles.forEach((article, i) => { + var prevIndex = i - 1; + var nextIndex = i + 1; + if (prevIndex >= 0) { + article.prev = articles[prevIndex].url; + } + if (nextIndex < articles.length) { + article.next = articles[nextIndex].url; + } + }); +} + +function resolveUrl(url) { + return url.replace('.md', '').replace('./', ''); +} + +function getArticleByFile(file) { + var result = this.articles.find(x => (x.filePath.indexOf(file) >= 0)); + if (result) return result; + throw Error(`${file} was not found in articles for ${this.root}.`); +} + +module.exports = function (toc, slug) { + + var model = { + root: slug, + title: null, + articles: [], + getArticleByFile: getArticleByFile + }; + + var reader = new commonmark.Parser(); + var parsed = reader.parse(toc); + + var walker = parsed.walker(); + var event, node; + + while ((event = walker.next())) { + node = event.node; + if (event.entering && node.type === 'heading') { + if (node.level === 1) { + model.title = getHeadingText(node); + } + if (node.level === 2) { + var title = getHeadingText(node); + var filePath = getHeadingLink(node); + + var article = { + title: title, + filePath: filePath, + url: resolveUrl(filePath) + }; + model.articles.push(article); + } + } + } + adorn(model); + return model; +} \ No newline at end of file diff --git a/build/main/listFiles.js b/build/main/listFiles.js new file mode 100644 index 00000000000..0f8ada6facd --- /dev/null +++ b/build/main/listFiles.js @@ -0,0 +1,17 @@ +const Glob = require('glob').Glob; + +module.exports = ctx => { + + return new Promise((resolve, reject) => { + + var globbing = new Glob(ctx.pattern, { cwd: ctx.cwd }, (err, files) => { + + if (err) { + reject(err); + return; + } + + resolve({ files: files, ctx: ctx }); + }); + }); +}; \ No newline at end of file diff --git a/build/main/series.js b/build/main/series.js new file mode 100644 index 00000000000..6ad75d1a92f --- /dev/null +++ b/build/main/series.js @@ -0,0 +1,43 @@ +var createSeriesModel = require('./createSeriesModel'); +var lec = require('line-ending-corrector').LineEndingCorrector.correctSync; +var yamlFront = require('yaml-front-matter'); +var path = require('path'); +var fs = require('fs'); +var os = require('os'); + +module.exports = function (seriesSlug) { + console.log(`series: ${seriesSlug}`); + + var seriesPath = path.resolve(__dirname, `../../docs/${seriesSlug}`); + var tocPath = path.resolve(seriesPath, `toc.md`); + + var toc = fs.readFileSync(tocPath, 'utf8'); + + var series = createSeriesModel(toc, seriesSlug); + + var files = fs.readdirSync(seriesPath); + files.forEach(file => { + if (file.substr(-3) != ".md") return; + if (file === "toc.md") return; + + var filePath = path.resolve(seriesPath, file); + var content = fs.readFileSync(filePath, 'utf8'); + + var yml = yamlFront.loadFront(content); + var original = yml.__content; + delete yml.__content; + + var article = series.getArticleByFile(file); + + if (article.prev) { + yml['pnp.series.prev'] = article.prev; + } + if (article.next) { + yml['pnp.series.next'] = article.next; + } + yml['pnp.series.title'] = series.title; + + var updated = lec('---' + os.EOL + yamlFront.dump(yml) + '---' + original, { eolc: 'CRLF' }); + fs.writeFileSync(filePath, updated[1]); + }); +} \ No newline at end of file diff --git a/build/main/templates/featured-card.liquid b/build/main/templates/featured-card.liquid new file mode 100644 index 00000000000..089ebaf938d --- /dev/null +++ b/build/main/templates/featured-card.liquid @@ -0,0 +1,17 @@ + +
+
+
+
+
+ +
+
+
+

{{ item.title }}

+

{{ item.description }}

+
+
+
+
+
\ No newline at end of file diff --git a/build/main/templates/two-column-card.liquid b/build/main/templates/two-column-card.liquid new file mode 100644 index 00000000000..cf461d10557 --- /dev/null +++ b/build/main/templates/two-column-card.liquid @@ -0,0 +1,17 @@ + +
+
+
+
+
+ {{ item.title }} +
+
+
+

{{ item.title }}

+

{{ item.description }}

+
+
+
+
+
\ No newline at end of file diff --git a/build/patterns/build.js b/build/patterns/build.js new file mode 100644 index 00000000000..ac8b23af2fa --- /dev/null +++ b/build/patterns/build.js @@ -0,0 +1,39 @@ +const path = require('path'); +const fs = require('fs'); + +const yamlFront = require('yaml-front-matter'); +const Liquid = require('shopify-liquid'); + +const patterns = require('./patterns'); +const categories = require('./categories'); + +const engine = Liquid({ + root: path.resolve(__dirname, 'templates'), + extname: '.liquid' +}); + +var dataPath = path.resolve(__dirname, '../../docs/patterns/_categories.yml'); +var content = fs.readFileSync(dataPath, 'utf8'); +var yml = yamlFront.loadFront(content); +yml.patterns = patterns(); + +// render landing page +['index'].forEach(x => { + console.log(`templated page: ${x}`); + var templatePath = path.resolve(__dirname, `../../docs/patterns/${x}.liquid.md`); + var outputhPath = path.resolve(__dirname, `../../docs/patterns/${x}.md`); + var template = engine.parse(fs.readFileSync(templatePath, 'utf8')); + + engine.render(template, yml) + .then(markdown => { + fs.writeFile(outputhPath, markdown); + }); +}); + +// render category pages +var template = engine.parse("{%- include 'category-index' -%}"); +categories(yml).forEach(c => { + var outputhPath = path.resolve(__dirname, `../../docs/patterns/category/${c.url}.md`); + engine.render(template, c) + .then(markdown => { fs.writeFile(outputhPath, markdown); }); +}); \ No newline at end of file diff --git a/build/patterns/categories.js b/build/patterns/categories.js new file mode 100644 index 00000000000..897ac442487 --- /dev/null +++ b/build/patterns/categories.js @@ -0,0 +1,11 @@ +module.exports = function (data) { + var categories = data.categories; + var patterns = data.patterns; + + // associate individual patterns with categories using their metadata + categories.forEach(c => { + c.patterns = patterns.filter(p => p.categories.includes(c.url)); + }); + + return categories; +}; \ No newline at end of file diff --git a/build/patterns/patterns.js b/build/patterns/patterns.js new file mode 100644 index 00000000000..17e9cb385ae --- /dev/null +++ b/build/patterns/patterns.js @@ -0,0 +1,28 @@ +var yamlFront = require('yaml-front-matter'); +var path = require('path'); +var fs = require('fs'); + +module.exports = function () { + + var model = []; + var patternsPath = path.resolve(__dirname, `../../docs/patterns`); + + var files = fs.readdirSync(patternsPath); + files.forEach(file => { + if (file.substr(-3) != ".md") return; + if (['index.md','index.liquid.md','toc.md'].indexOf(file) > -1) return; + + var filePath = path.resolve(patternsPath, file); + var content = fs.readFileSync(filePath, 'utf8'); + var yml = yamlFront.loadFront(content); + + model.push({ + title: yml['title'], + description: yml['description'], + file: file, + categories: yml['pnp.pattern.categories'] + }); + }); + + return model; +}; \ No newline at end of file diff --git a/build/patterns/templates/category-card.liquid b/build/patterns/templates/category-card.liquid new file mode 100644 index 00000000000..1b45ee1571c --- /dev/null +++ b/build/patterns/templates/category-card.liquid @@ -0,0 +1,10 @@ + + + {{ category.title }} + +
+

{{ category.title }}

+

{{ category.description }}

+
+ +
\ No newline at end of file diff --git a/build/patterns/templates/category-index.liquid b/build/patterns/templates/category-index.liquid new file mode 100644 index 00000000000..4a730942631 --- /dev/null +++ b/build/patterns/templates/category-index.liquid @@ -0,0 +1,21 @@ +--- +title: {{ title }} patterns +description: {{ description }} +keywords: design pattern +author: dragon119 +manager: bennage + +pnp.series.title: Cloud Design Patterns +--- + +# {{ title }} patterns + +[!INCLUDE [header](../../_includes/header.md)] + +{{ description }} + +| Pattern | Summary | +| ------- | ------- | +{%- for pattern in patterns %} +| [{{ pattern.title }}](../{{ pattern.file }}) | {{ pattern.description }} | +{%- endfor %} \ No newline at end of file diff --git a/docs/_bread/toc.yml b/docs/_bread/toc.yml new file mode 100644 index 00000000000..9367c108f06 --- /dev/null +++ b/docs/_bread/toc.yml @@ -0,0 +1,32 @@ +- name: Azure + tocHref: /azure/ + topicHref: /azure + items: + - name: Architecture + tocHref: /azure/architecture/ + topicHref: /azure/architecture + items: + - name: Best Practices + tocHref: /azure/architecture/best-practices + topicHref: /azure/architecture/best-practices + - name: Azure for AWS Professionals + tocHref: /azure/architecture/aws-professional + topicHref: /azure/architecture/aws-professional + - name: Design for Resiliency + tocHref: /azure/architecture/resiliency + topicHref: /azure/architecture/resiliency + - name: Design Review Checklists + tocHref: /azure/architecture/checklist + topicHref: /azure/architecture/checklist + - name: Elasticsearch + tocHref: /azure/architecture/elasticsearch + topicHref: /azure/architecture/elasticsearch + - name: Manage Identity in Multitenant Applications + tocHref: /azure/architecture/multitenant-identity + tocHref: /azure/architecture/multitenant-identity + - name: Blueprints + tocHref: /azure/architecture/blueprints + topicHref: /azure/architecture/blueprints + - name: Cloud Design Patterns + tocHref: /azure/architecture/patterns + topicHref: /azure/architecture/patterns \ No newline at end of file diff --git a/docs/_css/hubCards.css b/docs/_css/hubCards.css new file mode 100644 index 00000000000..44107a5fcd8 --- /dev/null +++ b/docs/_css/hubCards.css @@ -0,0 +1,487 @@ +.card { + overflow: hidden; + position: relative; + border: 1px solid #dbdbdb; + padding: 6px 12px 6px 12px; + box-shadow: 0px 2px 5px #e8e8e8; +} + +html.theme_night .card { + border: 1px solid #666; + box-shadow: 0px 2px 5px #444; +} + +.card:hover, .card:active { + border: 1px solid #5493d1; +} + +.card a { + text-decoration: none; +} + +.card h3 { + font-size: 1rem; + margin: 0; + line-height: 1.125; + color: $text; +} + +html.theme_night .card h3{ + color:#ccc; +} + +a .card:hover h3, a .card:active h3, html.theme_night .card:hover h3, html.theme_night .card:active h3 { + color: $blue; +} + +a .card:hover h3:hover, a .card:active h3:active, .card h3 a:hover, .card h3 a:active { + text-decoration:underline; +} + +.card p { + font-size: 0.9375rem; + line-height: 1.33; + color: $text-subtle; + margin: 6px 0 0 0; + padding: 0; +} + +html.theme_night .card p{ + color:#ccc; +} + +.cardPadding { + padding: 0 12px 22px 12px; +} + +.cardSize { + width: 100%; + overflow: hidden; +} + +.cardScaleImage { + background-repeat:no-repeat; + background-size: contain; + background-position: center center; + height:100%; + width:100%; +} + +.cardText a, +.cardText a:visited { + color: $text-subtle; + text-decoration: none; +} + +.cardText h3 a, +.cardText h3 a:visited { + color: $text; + text-decoration: none; +} + +html.theme_night .cardText h3 a, +html.theme_night .cardText h3 a:visited { + color: #ccc; + text-decoration: none; +} + +a .card:hover .cardText, +a .card:active .cardText, +.cardText a:hover, +.cardText a:active, +html.theme_night .cardText h3 a:hover, +html.theme_night .cardText h3 a:active { + color: $blue; +} + +/* styleA */ +.cardsA .cardSize { + min-width: 260px; +} + +.cardsA .card { + min-height: 176px; +} + +.cardsA .cardImageOuter { + width:100%; +} +.cardsA .cardImage { + width:68px; + height:68px; + margin:0 auto 8px auto; +} + +/* styleB */ +.cardsB .cardSize { + min-width: 260px; +} + +.cardsB .card { + height: 176px; +} + +.cardsB .cardImageOuter { + float:left; +} + +.cardsB .cardImage { + width:68px; + height:68px; + margin:0 auto 8px auto; +} + +.cardsB .cardText { + padding-left: 80px; + padding-top:12px; +} + +/* styleC */ +.cardsC .cardSize { + min-width: 260px; +} + +.cardsC .card { + padding: 0 0 6px 0; + min-height: 250px; +} + +.cardsC .cardImageOuter { + width: 100%; + height: 140px; + overflow: hidden; +} + +.cardsC .cardImage img { + width: 100%; + max-width: 400px; +} + +.cardsC .cardText { + padding: 6px 12px 0 12px; +} + +/* styleD */ +.cardsD .cardSize { + min-width: 260px; +} + +.cardsD .card { + padding: 0 0 6px 0; + box-shadow: none; + border: 0; +} + +html.theme_night .cardsD .card { + box-shadow: none; + border: 0; +} + +.cardsD .cardImageOuter { + width: 100%; + height: 180px; +} + +.cardsD .cardText { + padding: 12px; +} + +/* style E */ +.cardsE .cardSize { + min-width: 260px; +} + +.cardsE .card { + height: 180px; + padding: 0 0 0 12px; + box-shadow: none; + border: 0; + border-left: 2px solid #dbdbdb; +} + +html.theme_night .cardsE .card { + box-shadow: none; + border: 0; + border-left: 2px solid #333; +} + +html[dir='rtl'] .cardsE .card { + border: 0; + border-right: 2px solid #dbdbdb; + padding: 0 12px 0 0; +} + +html[dir='rtl'].theme_night .cardsE .card { + border-right: 2px solid #333; +} + +.cardsE .cardImageOuter { + width:100%; +} + +.cardsE .cardImage { + width:68px; + height:68px; + margin:0 0 8px 0; +} + +/* style F */ +.cardsF .cardSize { + min-width: 260px; +} + +.cardsF .card { + padding: 0 0 6px 0; + box-shadow: none; + border: 0; +} + +html.theme_night .cardsF .card { + box-shadow: none; + border: 0; +} + +.cardsF .cardImageOuter { + float:left; +} + +.cardsF .cardImage { + width:80px; + height:80px; + overflow:hidden; + margin:0 auto; +} + +.cardsF .cardText { + padding-left: 98px; +} + +/* style F Title */ +.cardsFTitle .cardSize { + min-width: 260px; +} + +.cardsFTitle .card { + padding: 0 0 6px 0; + box-shadow: none; + border: 0; +} + +html.theme_night .cardsFTitle .card { + box-shadow: none; + border: 0; +} + +.cardsFTitle .cardImageOuter { + float:left; +} + +.cardsFTitle .cardImage { + width:50px; + height:50px; + overflow:hidden; + margin:0 auto; +} + +.cardsFTitle .cardText { + padding-left: 62px; +} + +.cardsFTitle .cardText h3 { + font-family: segoe-ui_normal, 'Segoe UI', Segoe, 'Segoe WP', 'Helvetica Neue', Helvetica, sans-serif; + font-size: 1rem; + line-height: 1.25; +} + +.cardsFTitle .cardText p{ + display:none; +} + +/* style G */ +.cardsG .cardSize { + min-width: 260px; +} + +.cardsG .card { + padding: 0 0 6px 0; + box-shadow: none; + border: 0; +} + +html.theme_night .cardsG .card { + box-shadow: none; + border: 0; +} + +.cardsG .cardImageOuter { + float:left; +} + +.cardsG .cardImage { + width:150px; + height:105px; + overflow:hidden; + margin:0 auto 8px auto; +} + +.cardsG .cardImage img { + width: 100%; +} + +.cardsG .cardText { + padding-left: 162px; +} + +@media only screen and (min-width: $desktop) { + .cardsG > li { + -webkit-flex: 0 1 50%; + -ms-flex: 0 1 50%; + flex: 0 1 50%; + } +} + +/* landing pages */ +.cardsW .card { + box-shadow: none; + border: 0; + padding:0; +} + +html.theme_night .cardsW .card { + box-shadow: none; + border: 0; +} + +.cardsW .card h3 { + font-size:1.188rem; +} + +.cardsW .card p { + font-size:1rem; + margin: 10px 0 0; +} + +/* site home */ +.cardsX .cardSize { + position: relative; +} + +.cardsX .card { + min-height: 92px; + padding:0; +} + +.noTouch .cardsX .card:hover, .noTouch .cardsX .card:active { + border-color: transparent; +} + +.cardsX .cardImageOuter { + margin:0; +} + +.cardsX .cardImage { + width:300px; + height:92px; + margin:auto; +} + +.cardsX .cardText { + display:none; +} + +.cardsX .card h3 { + display:none; +} + +@media only screen and (min-width: $tablet) { + .cardsX li { + margin-bottom: 32px; + } +} + +/* featured */ +.cardsY .card { + padding: 6px; +} + +.cardsY .cardImageOuter { + float:left; + margin:6px 0 6px 4px; +} + +.cardsY .cardImage { + width:50px; + height:50px; +} + +.cardsY .cardText { + padding-left: 64px; + margin-top:12px; +} + +.cardsY .card h3 { + font-size: 1rem; + font-family: segoe-ui_normal, 'Segoe UI', Segoe, 'Segoe WP', 'Helvetica Neue', Helvetica, sans-serif; + line-height:1.28; +} + +.cardsY .card p { + font-size: 0.875rem; + margin:0; +} + +/* no image */ +.cardsZ .cardImageOuter { + display:none; +} + +.cardsZ .card { + min-height: 116px; + padding-top: 24px; +} + +.directory > li { + float:left; + width: 50%; +} + +.directory > li > .group { + float:left; + min-width: 240px; + width: 50%; +} + +.directory h3 { + margin-top:4px; +} + +.directory > li ul { + list-style-type:none; + margin-bottom:32px; +} + +.directory > li ul img { + float:left; + height:24px; + width:24px; +} + +@media only screen and (min-width: $desktop) { + .panelItem > .directory > li { + flex: 0 1 50%; + } +} + +.directory > li ul a { + color: $text-subtle; +} + +.group li p { + margin-left:36px; + color: $text-subtle; +} + +.group li:hover p, .group li:active p { + color: $blue; + text-decoration:underline +} \ No newline at end of file diff --git a/docs/_css/pnp.css b/docs/_css/pnp.css new file mode 100644 index 00000000000..151f1784802 --- /dev/null +++ b/docs/_css/pnp.css @@ -0,0 +1,147 @@ +h1 { + background-color: #007bb8; 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Also learn key similarities and differences between the AWS and Azure platforms. +services: '' +documentationcenter: '' +keywords: AWS experts, Azure comparison, AWS comparison, difference between azure and aws, azure and aws +author: lbrader +manager: christb + +ms.assetid: +ms.service: multiple +ms.workload: na +ms.tgt_pltfrm: na +ms.devlang: na +ms.topic: article +ms.date: 11/21/2016 +ms.author: lbrader + +--- + +# Introduction to Microsoft Azure accounts, platform, and services for AWS experts + +This article helps Amazon Web Services (AWS) experts understand the basics of Microsoft Azure accounts, platform, and services. It also covers key similarities and differences between the AWS and Azure platforms. + +You'll learn: + +* How accounts and resources are organized in Azure. +* How available solutions are structured in Azure. +* How the major Azure services differ from AWS services. + + Azure and AWS built their capabilities independently over time so that each has important implementation and design differences. + +## Overview + +Like AWS, Microsoft Azure is built around a core set of compute, storage, +database, and networking services. In many cases, both platforms offer a basic +equivalence between the products and services they offer. Both AWS and Azure +allow you to build highly available solutions based on Windows or Linux hosts. So, if you're used to development using Linux and OSS technology, both platforms +can do the job. + +While the capabilities of both platforms are similar, the resources that provide +those capabilities are often organized differently. Exact one-to-one +relationships between the services required to build a solution are not always +clear. There are also cases where a particular service might be offered on one +platform, but not the other. See [charts of comparable Azure and AWS services](services.md). + +## Accounts and subscriptions + +Azure services can be purchased using several pricing options, depending on your +organization's size and needs. See the [pricing +overview](https://azure.microsoft.com/pricing/) page for details. + +[Azure +subscriptions](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-infrastructure-subscription-accounts-guidelines/) +are a grouping of resources with an assigned owner responsible for billing and +permissions management. Unlike AWS, where any resources created under the AWS +account are tied to that account, subscriptions exist independently of their +owner accounts, and can be reassigned to new owners as needed. + +![Comparison of structure and ownership of AWS accounts and Azure subscriptions](./images/azure-aws-account-compare.png "Comparison of structure and ownership of AWS accounts and Azure subscriptions") +
    *Comparison of structure and ownership of AWS accounts and Azure subscriptions* +

    + +Subscriptions are assigned three types of administrator accounts: + +- **Account Administrator** - The subscription owner and the + account billed for the resources used in the subscription. The account + administrator can only be changed by transferring ownership of the + subscription. + +- **Service Administrator** - This account has rights to create and manage + resources in the subscription, but is not responsible for billing. By + default, the account administrator and service administrator are assigned to + the same account. The account administrator can assign a separate user to + the service administrator account for managing the technical and operational + aspects of a subscription. There is only one service administrator per + subscription. + +- **Co-administrator** - There can be multiple co-administrator accounts + assigned to a subscription. Co-administrators cannot change the service + administrator, but otherwise have full control over subscription resources + and users. + +Below the subscription level user roles and individual permissions can also be assigned to specific resources, similarly to how permissions are granted to IAM users and groups in AWS. In Azure all user accounts are associated with either a Microsoft Account or Organizational Account (an account managed through an Azure Active Directory). + +Like AWS accounts, subscriptions have default service quotas and limits. For a +full list of these limits, see [Azure subscription and service limits, quotas, +and +constraints](https://azure.microsoft.com/documentation/articles/azure-subscription-service-limits/). +These limits can be increased up to the maximum by [filing a support request in +the management +portal](https://blogs.msdn.microsoft.com/girishp/2015/09/20/increasing-core-quota-limits-in-azure/). + +### See also + +- [How to add or change Azure administrator + roles](https://azure.microsoft.com/documentation/articles/billing-add-change-azure-subscription-administrator/) + +- [How to download your Azure billing invoice and daily usage + data](https://azure.microsoft.com/documentation/articles/billing-download-azure-invoice-daily-usage-date/) + +## Resource management + +The term "resource" in Azure is used in the same way as in AWS, meaning any +compute instance, storage object, networking device, or other entity you can +create or configure within the platform. + +Azure resources are deployed and managed using one of two models: [Azure +Resource Manager, or the older Azure [classic deployment model](/azure/azure-resource-manager/resource-manager-deployment-model). +Any new resources are created using the Resource Manager model. + +### Resource groups + +Both Azure and AWS have entities called "resource groups" that organize resources such as VMs, storage, and virtual networking devices. However, [Azure resource groups](https://azure.microsoft.com/documentation/articles/virtual-machines-windows-infrastructure-resource-groups-guidelines/) are not directly comparable to AWS resource groups. + +While AWS allows a resource to be tagged into multiple resource groups, an Azure +resource is always associated with one resource group. A resource created in one +resource group can be moved to another group, but can only be in one resource +group at a time. Resource groups are the fundamental grouping used by Azure +Resource Manager. + +Resources can also be organized using +[tags](https://azure.microsoft.com/documentation/articles/resource-group-using-tags/). +Tags are key-value pairs that allow you to group resources across your +subscription irrespective of resource group membership. + +### Management interfaces + +Azure offers several ways to manage your resources: + +- [Web + interface](https://azure.microsoft.com/documentation/articles/resource-group-portal/). + Like the AWS Dashboard, the Azure portal provides a full web-based + management interface for Azure resources. + +- [REST + API](https://azure.microsoft.com/documentation/articles/resource-manager-rest-api/). + The Azure Resource Manager REST API provides programmatic access to most of + the features available in the Azure portal. + +- [Command + Line](https://azure.microsoft.com/documentation/articles/xplat-cli-azure-resource-manager/). + The Azure CLI tool provides a command-line interface capable of creating and + managing Azure resources. Azure CLI is available for [Windows, Linux, and + Mac OS](https://github.com/azure/azure-xplat-cli). + +- [PowerShell](https://azure.microsoft.com/documentation/articles/powershell-azure-resource-manager/). + The Azure modules for PowerShell allow you to execute automated management + tasks using a script. PowerShell is available for [Windows, Linux, and Mac + OS](https://github.com/PowerShell/PowerShell). + +- [Templates](https://azure.microsoft.com/documentation/articles/resource-group-authoring-templates/). + Azure Resource Manager templates provide similar JSON template-based + resource management capabilities to the AWS CloudFormation service. + +In each of these interfaces, the resource group is central to how Azure +resources get created, deployed, or modified. This is similar to the role a +"stack" plays in grouping AWS resources during CloudFormation deployments. + +The syntax and structure of these interfaces are different from their AWS +equivalents, but they provide comparable capabilities. In addition, many third +party management tools used on AWS, like [Hashicorp's +Terraform](https://www.terraform.io/docs/providers/azurerm/) and [Netflix +Spinnaker](http://www.spinnaker.io/), are also available on Azure. + +### See also + +- [Azure resource group + guidelines](https://azure.microsoft.com/documentation/articles/virtual-machines-windows-infrastructure-resource-groups-guidelines/) + +## Regions and zones (high availability) + +In AWS, availability centers around the concept of Availability Zones. In Azure, +fault domains and availability sets are all involved in building highly +available solutions. Paired regions provide additional disaster recovery +capabilities. + +### Availability Zones, Azure fault domains, and availability sets + +In AWS, a region is divided into two or more Availability Zones. An Availability +Zone corresponds with a physically isolated datacenter in the geographic region. +If you deploy your application servers to separate Availability Zones, a +hardware or connectivity outage affecting one zone does not impact any servers +hosted in other zones. + +In Azure, a [fault +domain](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-manage-availability/) +defines a group of VMs that shares a physical power source and network switch. +You use [availability +sets](https://azure.microsoft.com/documentation/articles/virtual-machines-windows-manage-availability/) +to spread VMs across multiple fault domains. When instances are assigned to the +same availability set, Azure distributes them evenly across several fault +domains. If a power failure or network outage occurs in one fault domain, at +least some of the set's VMs are in another fault domain and unaffected by the +outage. + +![AWS Availability Zones comparison to Azure fault domains and availability sets](./images/zone-fault-domains.png "AWS Availability Zones compared with Azure fault domains and availability sets") +
    *AWS Availability Zones compared with Azure fault domains and availability sets* +

    + +Availability sets should be organized by the instance's role in your application +to ensure one instance in each role is operational. For example, in a standard +three-tier web application, you would want to create a separate availability set +for front-end, application, and data instances. + +![Azure availability sets for each application role](./images/three-tier-example.png "Availability sets for each application role") +
    *Azure availability sets for each application role* +

    + +When VM instances are added to availability sets, they are also assigned an +[update +domain](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-manage-availability/). +An update domain is a group of VMs that are set for planned maintenance events +at the same time. Distributing VMs across multiple update domains ensures that +planned update and patching events affect only a subset of these VMs at any +given time. + +### Paired regions + +In Azure, you use [paired +regions](https://azure.microsoft.com/documentation/articles/best-practices-availability-paired-regions/) +to support redundancy across two predefined geographic regions, ensuring that +even if an outage affects an entire Azure region, your solution is still +available. + +Unlike AWS Availability Zones, which are physically separate datacenters but may +be in relatively nearby geographic areas, paired regions are usually separated +by at least 300 miles. This is intended to ensure larger scale disasters only impact one of the regions in the pair. Neighboring pairs can be set to sync +database and storage service data, and are configured so that platform updates +are rolled out to only one region in the pair at a time. + +Azure [geo-redundant +storage](https://azure.microsoft.com/documentation/articles/storage-redundancy/#geo-redundant-storage) +is automatically backed up to the appropriate paired region. For all other +resources, creating a fully redundant solution using paired regions means +creating a full copy of your solution in both regions. + +### See also + +- [Regions and availability for virtual machines in + Azure](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-regions-and-availability/) + +- [Disaster recovery and high availability for applications built on Microsoft + Azure](https://azure.microsoft.com/documentation/articles/resiliency-disaster-recovery-high-availability-azure-applications/) + +- [Planned maintenance for Linux virtual machines in + Azure](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-planned-maintenance/) + +## Services + +Consult the [complete AWS and Azure service comparison matrix](https://aka.ms/azure4aws-services) for a full listing of how all services map between platforms. + +Not all Azure products and +services are available in all regions. Consult the [Products by +Region](https://azure.microsoft.com/regions/services/) page for details. You can find the uptime guarantees and downtime credit policies for each Azure +product or service on the [Service Level +Agreements](https://azure.microsoft.com/support/legal/sla/) page. + +The following sections provide a brief explanation of how commonly used features and services differ between the AWS and Azure platforms. + +### Compute services + +#### EC2 Instances and Azure virtual machines + +Although AWS instance types and Azure virtual machine sizes breakdown in a +similar way, there are differences in the RAM, CPU, and storage capabilities. + +- [Amazon EC2 Instance Types](https://aws.amazon.com/ec2/instance-types/) + +- [Sizes for virtual machines in Azure + (Windows)](https://azure.microsoft.com/documentation/articles/virtual-machines-windows-sizes/) + +- [Sizes for virtual machines in Azure + (Linux)](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-sizes/) + +Unlike AWS' hourly billing, Azure on-demand VMs are billed by the minute. + +Azure has no equivalent to EC2 Spot Instances, Reserved Instances, or Dedicated +Hosts. + +#### EBS and Azure Storage for VM disks + +Durable data storage for Azure VMs is provided by [data +disks](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-about-disks-vhds/) +residing in blob storage. This is similar to how EC2 instances store disk +volumes on Elastic Block Store (EBS). [Azure temporary +storage](https://blogs.msdn.microsoft.com/mast/2013/12/06/understanding-the-temporary-drive-on-windows-azure-virtual-machines/) +also provides VMs the same low-latency temporary read-write storage as EC2 +Instance Storage (also called ephemeral storage). + +Higher performance disk IO is supported using [Azure premium +storage](https://docs.microsoft.com/azure/storage/storage-premium-storage). +This is similar to the Provisioned IOPS storage options provided by AWS. + +#### Lambda, Azure Functions, Azure Web-Jobs, and Azure Logic Apps + +[Azure Functions](https://azure.microsoft.com/services/functions/) is the +primary equivalent of AWS Lambda in providing serverless, on-demand code. +However, Lambda functionality also overlaps with other Azure services: + +- [WebJobs](https://azure.microsoft.com/documentation/articles/web-sites-create-web-jobs/) - allow you to create scheduled or continuously running background tasks. + +- [Logic Apps](https://azure.microsoft.com/services/logic-apps/) - provides communications, integration, and business rule management services. + +#### Autoscaling, Azure VM scaling, and Azure App Service Autoscale + +Autoscaling in Azure is handled by two services: + +- [VM scale + sets](https://azure.microsoft.com/documentation/articles/virtual-machine-scale-sets-overview/) - allow you to deploy and manage an identical set of VMs. The number of + instances can autoscale based on performance needs. + +- [App Service + Autoscale](https://azure.microsoft.com/documentation/articles/web-sites-scale/) - provides the capability to autoscale Azure App Service solutions. + + +#### Container Service +The [Azure Container Service](https://docs.microsoft.com/azure/container-service/container-service-intro) supports Docker containers managed through Docker Swarm, Kubernetes, or DC/OS. + +#### Other compute services + + +Azure offers several compute services that do not have direct equivalents in +AWS: + +- [Azure + Batch](https://azure.microsoft.com/documentation/articles/batch-technical-overview/) - allows you to manage compute-intensive work across a scalable collection + of virtual machines. + +- [Service + Fabric](https://azure.microsoft.com/documentation/articles/service-fabric-overview/) - platform for developing and hosting scalable + [microservice](https://azure.microsoft.com/documentation/articles/service-fabric-overview-microservices/) + solutions. + +#### See also + +- [Create a Linux VM on Azure using the + Portal](https://azure.microsoft.com/documentation/articles/virtual-machines-linux-quick-create-portal/) + +- [Azure Reference Architecture: Running a Linux VM on + Azure](https://azure.microsoft.com/documentation/articles/guidance-compute-single-vm-linux/) + +- [Get started with Node.js web apps in Azure App + Service](https://azure.microsoft.com/documentation/articles/app-service-web-nodejs-get-started/) + +- [Azure Reference Architecture: Basic web + application](https://azure.microsoft.com/documentation/articles/guidance-web-apps-basic/) + +- [Create your first Azure + Function](https://azure.microsoft.com/documentation/articles/functions-create-first-azure-function/) + +### Storage + +#### S3/EBS/EFS and Azure Storage + +In the AWS platform, cloud storage is primarily broken down into three services: + +- **Simple Storage Service (S3)** - basic object storage. Makes data available + through an Internet accessible API. + +- **Elastic Block Storage (EBS)** - block level storage, intended for access + by a single VM. + +- **Elastic File System (EFS)** - file storage meant for use as shared storage + for up to thousands of EC2 instances. + +In Azure Storage, subscription-bound [storage +accounts](https://azure.microsoft.com/documentation/articles/storage-create-storage-account/) +allow you to create and manage the following storage services: + +- [Blob + storage](https://azure.microsoft.com/documentation/articles/storage-create-storage-account/) - stores any type of text or binary data, such as a document, media file, or + application installer. You can set Blob storage for private access or share + contents publicly to the Internet. Blob storage serves the same purpose as + both AWS S3 and EBS. + +- [Table + storage](https://azure.microsoft.com/documentation/articles/storage-nodejs-how-to-use-table-storage/) - stores structured datasets. Table storage is a NoSQL key-attribute data + store that allows for rapid development and fast access to large quantities + of data. Similar to AWS' SimpleDB and DynamoDB services. + +- [Queue + storage](https://azure.microsoft.com/documentation/articles/storage-nodejs-how-to-use-queues/) - provides messaging for workflow processing and for communication between + components of cloud services. + +- [File + storage](https://azure.microsoft.com/documentation/articles/storage-java-how-to-use-file-storage/) - offers shared storage for legacy applications using the standard server + message block (SMB) protocol. File storage is used in a similar manner to + EFS in the AWS platform. + +#### Glacier and Azure Storage + +Azure Storage does not offer a direct equivalent to AWS' long-term archival +Glacier storage. For data that is infrequently accessed and long-lived Azure +offers the [Azure cool blob storage +tier](https://azure.microsoft.com/documentation/articles/storage-blob-storage-tiers/). +Cool storage provides cheaper, lower performance storage than standard blob +storage and is comparable to AWS' S3 - Infrequent Access. + +#### See also + +- [Microsoft Azure Storage Performance and Scalability + Checklist](https://azure.microsoft.com/documentation/articles/storage-performance-checklist/) + +- [Azure Storage security + guide](https://azure.microsoft.com/documentation/articles/storage-security-guide/) + +- [Patterns & Practices: Content Delivery Network (CDN) + guidance](https://azure.microsoft.com/documentation/articles/best-practices-cdn/) + +### Networking + +#### Elastic Load Balancing, Azure Load Balancer, and Azure Application Gateway + +The Azure equivalents of the two Elastic Load Balancing services are: + +- [Load + Balancer](https://azure.microsoft.com/documentation/articles/load-balancer-overview/) - provides the same capabilities as the AWS Classic Load Balancer, allowing + you to distribute traffic for multiple VMs at the network level. It also + provides failover capability. + +- [Application + Gateway](https://azure.microsoft.com/documentation/articles/application-gateway-introduction/) - offers application-level rule-based routing comparable to the AWS + Application Load Balancer. + +#### Route 53, Azure DNS, and Azure Traffic Manager + +In AWS, Route 53 provides both DNS name management and DNS-level traffic routing +and failover services. In Azure this is handled through two services: + +- [Azure DNS](https://azure.microsoft.com/documentation/services/dns/) - provides domain and DNS management. + +- [Traffic + Manager](https://azure.microsoft.com/documentation/articles/traffic-manager-overview/) - provides DNS level traffic routing, load balancing, and failover + capabilities. + +#### Direct Connect and Azure ExpressRoute + +Azure provides similar site-to-site dedicated connections through its +[ExpressRoute](https://azure.microsoft.com/documentation/services/expressroute/) +service. ExpressRoute allows you to connect your local network directly to Azure +resources using a dedicated private network connection. Azure also offers more +conventional [site-to-site VPN +connections](https://azure.microsoft.com/documentation/articles/vpn-gateway-howto-site-to-site-resource-manager-portal/) +at a lower cost. + +#### See also + +- [Create a virtual network using the Azure + portal](https://azure.microsoft.com/documentation/articles/virtual-networks-create-vnet-arm-pportal/) + +- [Plan and design Azure Virtual + Networks](https://azure.microsoft.com/documentation/articles/virtual-network-vnet-plan-design-arm/) + +- [Azure Network Security Best + Practices](https://azure.microsoft.com/documentation/articles/azure-security-network-security-best-practices/) + +### Database services + +#### RDS and Azure SQL Database service + +AWS and Azure have different approaches on relational database offerings in the +cloud. AWS' Relational Database Service (RDS) supports creating instances using +several different database engines, such as Oracle and MySQL. + +[SQL +Database](https://azure.microsoft.com/documentation/articles/sql-database-technical-overview/) +is Azure's cloud database offering. It provides highly scalable relational data +storage, through a managed service. SQL Database uses its own engine, and does +not support the creation of other database types. Other database engines such as +[SQL +Server](https://azure.microsoft.com/services/virtual-machines/sql-server/), +[Oracle](https://azure.microsoft.com/campaigns/oracle/), or +[MySQL](https://azure.microsoft.com/documentation/articles/virtual-machines-windows-classic-mysql-2008r2/) +can be deployed using Azure VM Instances. + +Costs for AWS RDS are determined by the amount of hardware resources that your +instance uses, like CPU, RAM, storage, and network bandwidth. In the SQL +Database service, cost depends on your database size, concurrent connections, +and throughput levels. + +#### See also + +- [Azure SQL Database + Tutorials](https://azure.microsoft.com/documentation/articles/sql-database-explore-tutorials/) + +- [Configure geo-replication for Azure SQL Database with the Azure + portal](https://azure.microsoft.com/documentation/articles/sql-database-geo-replication-portal/) + +- [Introduction to DocumentDB: A NoSQL JSON + Database](https://azure.microsoft.com/documentation/articles/documentdb-introduction/) + +- [How to use Azure Table storage from + Node.js](https://azure.microsoft.com/documentation/articles/storage-nodejs-how-to-use-table-storage/) + +### Security and identity + +#### Directory service and Azure Active Directory + +Azure splits up directory services into the following offerings: + +- [Azure Active + Directory](https://azure.microsoft.com/documentation/articles/active-directory-whatis/) - cloud based directory and identity management service. + +- [Azure Active Directory + B2B](https://azure.microsoft.com/documentation/articles/active-directory-b2b-collaboration-overview/) - enables access to your corporate applications from partner-managed + identities. + +- [Azure Active Directory + B2C](https://azure.microsoft.com/documentation/articles/active-directory-b2c-overview/) - service offering support for single sign-on and user management for + consumer facing applications. + +- [Azure Active Directory Domain + Services](https://azure.microsoft.com/documentation/articles/active-directory-ds-overview/) - hosted domain controller service, allowing Active Directory compatible + domain join and user management functionality. + +#### Web application firewall + +In addition to the [Application Gateway Web Application +Firewall](https://azure.microsoft.com/documentation/articles/application-gateway-webapplicationfirewall-overview/), +you can also [use web application +firewalls](https://azure.microsoft.com/documentation/articles/application-gateway-webapplicationfirewall-overview/) +from third-party vendors like [Barracuda +Networks](https://azure.microsoft.com/marketplace/partners/barracudanetworks/waf/). + +#### See also + +- [Getting started with Microsoft Azure + security](https://azure.microsoft.com/documentation/articles/azure-security-getting-started/) + +- [Azure Identity Management and access control security best + practices](https://azure.microsoft.com/documentation/articles/azure-security-identity-management-best-practices/) + +### Application and messaging services + +#### Simple Email Service + +AWS provides the Simple Email Service (SES) for sending notification, +transactional, or marketing emails. In Azure, third-party solutions like +[Sendgrid](https://sendgrid.com/partners/azure/) provide email services. + +#### Simple Queueing Service + +AWS Simple Queueing Service (SQS) provides a messaging system for connecting +applications, services, and devices within the AWS platform. Azure has two +services that provide similar functionality: + +- [Queue + storage](https://azure.microsoft.com/documentation/articles/storage-nodejs-how-to-use-queues/) - a cloud messaging service that allows communication between application + components within the Azure platform. + +- [Service + Bus](https://azure.microsoft.com/en-us/services/service-bus/) - a more robust messaging system for connecting applications, services, and + devices. Using the related [Service Bus + relay](https://docs.microsoft.com/en-us/azure/service-bus-relay/relay-what-is-it), + Service Bus can also connect to remotely hosted applications and services. + +#### Device Farm + +The AWS Device Farm provides cross-device testing services. In Azure, [Xamarin +Test Cloud](https://www.xamarin.com/test-cloud) provides similar cross-device +front-end testing for mobile devices. + +In addition to front-end testing, the [Azure DevTest +Labs](https://azure.microsoft.com/services/devtest-lab/) provides back end +testing resources for Linux and Windows environments. + +#### See also + +- [How to use Queue storage from + Node.js](https://azure.microsoft.com/documentation/articles/storage-nodejs-how-to-use-queues/) + +- [How to use Service Bus + queues](https://azure.microsoft.com/documentation/articles/service-bus-nodejs-how-to-use-queues/) + +### Analytics and big data + +[The Cortana Intelligence +Suite](https://azure.microsoft.com/suites/cortana-intelligence-suite/) is +Azure's package of products and services designed to capture, organize, analyze, +and visualize large amounts of data. The Cortana suite consists of the following +services: + +- [HDInsight](https://azure.microsoft.com/documentation/services/hdinsight/) - managed Apache distribution that includes Hadoop, Spark, Storm, or HBase. + +- [Data + Factory](https://azure.microsoft.com/documentation/services/data-factory/) - provides data orchestration and data pipeline functionality. + +- [SQL Data + Warehouse](https://azure.microsoft.com/documentation/services/sql-data-warehouse/) - large-scale relational data storage. + +- [Data Lake + Store](https://azure.microsoft.com/documentation/services/data-lake-store/) - large-scale storage optimized for big data analytics workloads. + +- [Machine + Learning](https://azure.microsoft.com/documentation/services/machine-learning/) - used to build and apply predictive analytics on data. + +- [Stream + Analytics](https://azure.microsoft.com/documentation/services/stream-analytics/) - real-time data analysis. + +- [Data Lake + Analytics](https://azure.microsoft.com/documentation/articles/data-lake-analytics-overview/) - large-scale analytics service optimized to work with Data Lake Store + +- [PowerBI](https://powerbi.microsoft.com/) - used to power data + visualization. + +#### See also + +- [Cortana Intelligence Gallery](https://gallery.cortanaintelligence.com/) + +- [Understanding Microsoft big data + solutions](https://msdn.microsoft.com/library/dn749804.aspx) + +- [Azure Data Lake & Azure HDInsight + Blog](https://blogs.msdn.microsoft.com/azuredatalake/) + +### Internet of Things + +#### See also + +- [Get started with Azure IoT + Hub](https://azure.microsoft.com/documentation/articles/iot-hub-csharp-csharp-getstarted/) + +- [Comparison of IoT Hub and Event + Hubs](https://azure.microsoft.com/documentation/articles/iot-hub-compare-event-hubs/) + +### Mobile services + +#### Notifications + +Notification Hubs do not support sending SMS or email messages, so third-party +services are needed for those delivery types. + +#### See also + +- [Create an Android + app](https://azure.microsoft.com/documentation/articles/app-service-mobile-android-get-started/) + +- [Authentication and Authorization in Azure Mobile + Apps](https://azure.microsoft.com/documentation/articles/app-service-mobile-auth/) + +- [Sending push notifications with Azure Notification + Hubs](https://azure.microsoft.com/documentation/articles/notification-hubs-android-push-notification-google-fcm-get-started/) + +### Management and monitoring + +#### See also +- [Monitoring and diagnostics + guidance](https://azure.microsoft.com/documentation/articles/best-practices-monitoring/) + +- [Best practices for creating Azure Resource Manager + templates](https://azure.microsoft.com/documentation/articles/resource-manager-template-best-practices/) + +- [Azure Resource Manager Quickstart + templates](https://azure.microsoft.com/documentation/templates/) + + +## Next steps + +- [Complete AWS and Azure service comparison + matrix](https://aka.ms/azure4aws-services) + +- [Interactive Azure Platform Big + Picture](http://azureplatform.azurewebsites.net/) + +- [Get started with Azure](https://azure.microsoft.com/get-started/) + +- [Azure solution + architectures](https://azure.microsoft.com/solutions/architecture/) + +- [Azure Reference + Architectures](https://azure.microsoft.com/documentation/articles/guidance-architecture/) + +- [Patterns & Practices: Azure + Guidance](https://azure.microsoft.com/documentation/articles/guidance/) + +- [Free Online Course: Microsoft Azure for AWS + Experts](http://aka.ms/azureforaws) diff --git a/docs/aws-professional/services.md b/docs/aws-professional/services.md new file mode 100644 index 00000000000..b4741e6f3fb --- /dev/null +++ b/docs/aws-professional/services.md @@ -0,0 +1,160 @@ +--- +title: Azure and AWS services compared - multicloud +description: See how Microsoft Azure cloud services compare to Amazon Web Services (AWS) for multicloud solutions or migration to Azure. Learn the IT capabilities of each. +services: '' +documentationcenter: '' +keywords: cloud services comparison, cloud services compared, multicloud, compare azure aws, compare azure and aws, compare aws and azure, IT capabilities +author: lbrader +manager: christb + +pnp.series.title: Azure for AWS Professionals + +ms.assetid: 02488dea-711a-4618-8c51-667286008989 +ms.service: multiple +ms.workload: na +ms.tgt_pltfrm: na +ms.devlang: na +ms.topic: article +ms.date: 12/29/2016 +ms.author: lbrader + +--- +# Services comparison +[!INCLUDE [header](../_includes/header.md)] + +This article helps you understand how Microsoft Azure services compare to Amazon Web Services (AWS). Whether you are planning a multicloud solution with Azure and AWS, or migrating to Azure, you can compare the IT capabilities of Azure and AWS services in all categories. + +In the tables following, there are multiple Azure services listed for some AWS services. The Azure services are similar to one another, but depth and breadth of capabilities vary. + +## Azure and AWS for multicloud solutions + +As the leading public cloud platforms, Microsoft Azure and Amazon Web Services (AWS) each offer businesses a broad and deep set of capabilities with global coverage. Yet many organizations choose to use both platforms together for greater choice and flexibility, as well as to spread their risk and dependencies with a multicloud approach. Consulting companies and software vendors might also build on and use both Azure and AWS, as these platforms represent most of the cloud market demand. + +For an overview of Azure for AWS users, see [Introduction to Azure for AWS experts](index.md). + + +## Compute services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Virtual servers|EC2|[Virtual Machines](https://azure.microsoft.com/services/virtual-machines/)|Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes.| +|Container management|EC2 Container Service|[Container Service](http://azure.microsoft.com/services/container-service/)|A container management service that supports Docker containers and allows users to run applications on managed instance clusters. It eliminates the need to operate cluster management software or design fault-tolerant cluster architectures.| +|Web application|Elastic Beanstalk|- [Web Apps](https://azure.microsoft.com/services/app-service/web/)
    - [Cloud Services](https://azure.microsoft.com/services/cloud-services/)|A fully managed web infrastructure that provides the underlying web server instances and surrounding security, management, resilience, and shared storage capabilities.| +|Auto scale|Auto Scaling|- [VM Scale Sets](https://azure.microsoft.com/services/virtual-machine-scale-sets/)
    - [App Service AutoScaling](https://docs.microsoft.com/azure/app-service/app-service-environment-auto-scale)|Lets you automatically change the number of instances providing a particular compute workload. You set defined metric and thresholds that determine if the platform adds or removes instances.| +|Virtual server disk infrastructure|Elastic Block Store (EBS)|- [Page Blobs](https://docs.microsoft.com/azure/storage/storage-introduction#blob-storage)
    - [Premium Storage](https://docs.microsoft.com/azure/storage/storage-premium-storage)|Provides persistent, durable storage volumes for use with virtual machines, and offers the option to select different underlying physical storage types and performance characteristics.| +|Backend process logic|Lambda|- [Functions](https://azure.microsoft.com/services/functions/)
    - [Web Jobs](https://docs.microsoft.com/azure/app-service-web/web-sites-create-web-jobs)
    - [Logic Apps](https://azure.microsoft.com/services/logic-apps/) |Used to integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.| +|Job-based applications|** **|[Batch](https://azure.microsoft.com/services/batch/)|Orchestration of the tasks and interactions between compute resources that are needed when you require processing across hundreds or thousands of compute nodes.| +|Microservice-based applications|** **|[Service Fabric](https://azure.microsoft.com/services/service-fabric/)|A compute service that orchestrates and manages the execution, lifetime, and resilience of complex, inter-related code components that can be either stateless or stateful.| +|API-based application runtime|** **|[API Apps](https://azure.microsoft.com/services/app-service/api/)|Build, manage, and host APIs enabling a variety of languages and SDKs with built-in authentication and analytics.| +|Disaster recovery|** **|[Site recovery](https://azure.microsoft.com/services/site-recovery/)|Automates protection and replication of virtual machines. Offers health monitoring, recovery plans, and recovery plan testing.| +|Predefined templates|AWS Quick Start|[Azure Quickstart Templates](https://azure.microsoft.com/resources/templates/)|Community-led templates for creating and deploying virtual machine-based solutions.| +|Marketplace|AWS Marketplace|[Azure Marketplace](https://azure.microsoft.com/marketplace/)|Easy-to-deploy and automatically configured third-party applications, including single virtual machine or multiple virtual machine solutions.| + +## Storage and content delivery services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Object storage|S3|[Blob Storage](https://azure.microsoft.com/services/storage/blobs/)|Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.| +|Shared file storage|Elastic File System (Preview)|[File Storage](https://azure.microsoft.com/services/storage/files/)|Provides a simple interface to create and configure file systems quickly, and share common files. It’s shared file storage without the need for a supporting virtual machine, and can be used with traditional protocols that access files over a network.| +|Archiving and backup|N/A (software)
    Glacier and S3 (storage)|- [Backup (software)](https://azure.microsoft.com/services/backup/)
    - [Blob Storage (storage)](https://azure.microsoft.com/services/storage/blobs/)|Backup and archival solutions allow files and folders to be backed up and recovered from the cloud, and provides off-site protection against data loss. There are two components of backup software service that orchestrates backup/retrieval and the underlying backup storage infrastructure.| +|Hybrid storage|Storage Gateway|[StorSimple](https://azure.microsoft.com/services/storsimple/)|Integrates on-premises IT environments with cloud storage. Automates data management and storage, plus supports in disaster recovery.| +|Data transport|Import/Export Snowball|[Import/Export](https://azure.microsoft.com/pricing/details/storage-import-export/)|A data transport solution that uses secure disks and appliances to transfer large amounts of data. Also offers data protection during transit.| +|Content delivery|CloudFront|[Content Delivery Network](https://azure.microsoft.com/services/cdn/)|A global content delivery network that delivers audio, video, applications, images, and other files.| + +## Databases + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Relational database|RDS|[SQL Database](https://azure.microsoft.com/services/sql-database/)|Relational database-as-a-service (DBaaS) where the database resilience, scale, and maintenance are primarily handled by the platform.| +|NoSQL database|DynamoDB|[DocumentDB](https://azure.microsoft.com/services/documentdb/)|A globally distributed NoSQL database service that supports elastically scaling throughput and storage across multiple regions, supports multiple well-defined consistency models, and is capable of automatically indexing data to serve SQL and MongoDB APIs.| +|Data warehouse|Redshift|[SQL Data Warehouse](https://azure.microsoft.com/services/sql-data-warehouse/) |A fully managed data warehouse that analyzes data using business intelligence tools. It can transact SQL queries across relational and non-relational data.| +|Table storage|DynamoDB
    SimpleDB|[Table Storage](https://azure.microsoft.com/services/storage/tables/)|A non-relational data store for semi-structured data. Developers store and query data items via web services requests.| +|Caching|ElastiCache|[Azure Redis Cache](https://azure.microsoft.com/services/cache/)|An in-memory based, distributed caching service that provides a high-performance store typically used to offload non-transactional work from a database.| +|Database migration|Database Migration Service (Preview)|[SQL Database Migration Wizard](https://sqlazuremw.codeplex.com/)|Typically is focused on the migration of database schema and data from one database format to a specific database technology in the cloud.| + +## Networking services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Networking|Virtual Private Cloud|[Virtual Network](https://azure.microsoft.com/services/virtual-network/)|Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.| +|Domain name system (DNS)|Route 53|- [DNS](https://azure.microsoft.com/services/dns/)
    - [Traffic Manager](https://azure.microsoft.com/services/traffic-manager/)|A service that hosts domain names, plus routes users to Internet applications, connects user requests to datacenters, manages traffic to apps, and improves app availability with automatic failover.| +|Dedicated network|Direct Connect|[ExpressRoute](https://azure.microsoft.com/services/expressroute/)|Establishes a dedicated, private network connection from a location to the cloud provider (not over the Internet).| +|Load balancing|Elastic Load Balancing|- [Load Balancer](https://azure.microsoft.com/services/load-balancer/)
    - [Application Gateway](https://azure.microsoft.com/services/application-gateway/)|Automatically distributes incoming application traffic to add scale, handle failover, and route to a collection of resources.| + +## Developer tools + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +| Development tools |AWS Toolkit for Microsoft Visual Studio
    AWS Toolkit for Eclipse | [Visual Studio](https://www.visualstudio.com/vs/azure-tools/) |Development tools to help build, manage, and deploy cloud applications. | +| Dev-Test | Development and Test | [Development and Test](https://azure.microsoft.com/solutions/dev-test/)| Creates consistent development and test environments through a scalable, on-demand infrastructure. | + +## Management tools + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Deployment orchestration|OpsWorks
    CloudFormation|- [Resource Manager](https://docs.microsoft.com/azure/azure-resource-manager/resource-group-overview)
    - [Automation](https://azure.microsoft.com/services/automation/)
    - [VM extensions](https://docs.microsoft.com/azure/virtual-machines/virtual-machines-windows-extensions-features)|Configures and operates applications of all shapes and sizes, and provides templates to create and manage a collection of resources.| +|Management and monitoring|CloudWatch
    CloudTrail|- [Log Analytics](https://azure.microsoft.com/services/log-analytics/)
    - [Azure portal](https://azure.microsoft.com/features/azure-portal/)
    - [Application Insights](https://azure.microsoft.com/services/application-insights/)|Management and monitoring services for cloud resources and applications to collect, track, store, analyze, and deliver metrics and log files.| +|Optimization|Trusted Advisor|[Advisor (preview)](https://azure.microsoft.com/services/advisor)|Provides analysis of cloud resource configuration and security so subscribers can ensure they’re using best practices and optimum configurations.| +|Job scheduling|** **|[Scheduler](https://azure.microsoft.com/services/scheduler/)|Runs jobs on simple or complex recurring schedules—now, later, or recurring.| +|Catalog service|Service Catalog|** **|Creates and manages catalogs of approved IT services so users can quickly find and deploy them.| +|Administration|Config|[Azure portal (audit logs)](https://docs.microsoft.com/azure/azure-resource-manager/resource-group-audit)|Provides resource inventory, configuration history, and configuration change notifications for security and governance.| +|Programmatic access|Command Line Interface|- [Azure Command Line Interface (CLI)](https://docs.microsoft.com/azure/xplat-cli-install)
    - [Azure PowerShell](https://docs.microsoft.com/azure/powershell-install-configure)|Built on top of the native REST API across all cloud services, various programming language-specific wrappers provide easier ways to create solutions.| + +## Security and identity services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Authentication and authorization|Identity and Access Management
    Multi-Factor Authentication|- [Azure AD/Role-based access control](https://docs.microsoft.com/azure/active-directory/role-based-access-control-what-is)
    - [Multi-Factor Authentication](https://azure.microsoft.com/services/multi-factor-authentication/)|Lets users securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.| +|Encryption|Key Management Service
    CloudHSM|[Key Vault](https://azure.microsoft.com/services/key-vault/)|Creates, controls, and protects the encryption keys used to encrypt data. HSM provides hardware-based key storage.| +|Firewall|Web Application Firewall|[Web Application Firewall (preview)](https://docs.microsoft.com/azure/application-gateway/application-gateway-webapplicationfirewall-overview)|A firewall that protects web applications from common web exploits. Users can define customizable web security rules.| +|Security|Inspector (Preview)|[Security Center](https://azure.microsoft.com/services/security-center/) |An automated security assessment service that improves the security and compliance of applications. Automatically assess applications for vulnerabilities or deviations from best practices.| +|Directory|Directory Service|- [Azure Active Directory](https://azure.microsoft.com/services/active-directory/)
    - [Azure Active Directory B2C](https://azure.microsoft.com/services/active-directory-b2c/)
    - [Azure Active Directory Domain Services](https://azure.microsoft.com/services/active-directory-ds/)|Typically provides user/group properties that can be queried and used in applications. Also can provide capabilities to integrate to on-premises Active Directory services for single sign-on scenarios and SaaS management.| + +## Analytics services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Big data processing|Elastic MapReduce (EMR)|[HDInsight](https://azure.microsoft.com/services/hdinsight/)|Supports technologies that break up large data processing tasks into multiple jobs, and then combine the results together to enable massive parallelism.| +|Data orchestration|Data Pipeline|[Data Factory](https://azure.microsoft.com/services/data-factory/)|Processes and moves data between different compute and storage services, as well as on-premises data sources at specified intervals. Users can create, schedule, orchestrate, and manage data pipelines.| +|Analytics|Kinesis Analytics (Preview)|- [Stream Analytics](https://azure.microsoft.com/services/stream-analytics/)
    - [Data Lake Analytics](https://azure.microsoft.com/services/data-lake-analytics/)
    - [Data Lake Store](https://azure.microsoft.com/services/data-lake-store/) |Storage and analysis platforms that creates insights from large quantities of data, or data that originates from many sources.| +|Visualization|QuickSight (Preview)|[PowerBI](https://powerbi.microsoft.com/)|Business intelligence tools that build visualizations, perform ad-hoc analysis, and develop business insights from data.| +|Machine learning|Machine Learning|[Machine Learning](https://azure.microsoft.com/services/machine-learning/)|Produces an end-to-end workflow to create, process, refine, and publish predictive models that can be used to understand what might happen from complex data sets.| +|Search|Elasticsearch Service|[Search](https://azure.microsoft.com/services/search/)|Delivers full-text search and related search analytics and capabilities.| +|Data discovery|** **|[Data Catalog](https://azure.microsoft.com/services/data-catalog/) |Provides the ability to better register, enrich, discover, understand, and consume data sources.| + +## Mobile services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Pro app development|Mobile Hub (Beta)
    Cognito|[Mobile Apps](https://azure.microsoft.com/services/app-service/mobile/)|Backend mobile services for rapid development of mobile solutions, plus provide identity management, data synchronization, and storage and notifications across devices.| +|High-level app development|** **|[PowerApps](https://powerapps.microsoft.com/)|Model-driven application development for business applications with SaaS integration.| +|Analytics|Mobile Analytics|[Mobile Engagement](https://azure.microsoft.com/services/mobile-engagement/)|Provides real-time analytics from mobile apps data, highlights app users’ behavior, measures app usage, and tracks key trends.| +|Notification|Simple Notification Service|[Notification Hubs](https://azure.microsoft.com/services/notification-hubs/)|A push notification service that delivers messages instantly to applications or users. Messages can be sent to individual devices or can be broadcasted.| + +## Application services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Email|Simple Email Service | |Lets users send transactional email, marketing messages, or any other type of content to customers.| +|Messaging|Simple Queue Service|- [Queue Storage](https://azure.microsoft.com/services/storage/queues/)
    - [Service Bus queues](https://docs.microsoft.com/azure/service-bus-messaging/service-bus-dotnet-get-started-with-queues)
    - [Service Bus topics](https://docs.microsoft.com/azure/service-bus-messaging/service-bus-dotnet-how-to-use-topics-subscriptions)
    - [Service Bus relay](https://docs.microsoft.com/azure/service-bus-relay/relay-what-is-it)|Stores large numbers of messages that can be accessed from anywhere through authenticated calls using HTTP or HTTPS. A queue can contain millions of messages, up to the total capacity limit of a storage account, and may also support more complex topologies such as publish/subscribe.| +|Workflow|Simple Workflow Service|[Logic Apps](https://azure.microsoft.com/services/logic-apps/)|A state tracker and task coordinator service that allows developers to build, run, and scale background activities using a visual processes flow creation.| +|App testing|Device Farm (Front End)|- [Xamarin Test Cloud (Front End)](https://www.xamarin.com/test-cloud)
    - [Azure DevTest Labs (Back End)](https://azure.microsoft.com/services/devtest-lab/)|A range of services geared toward the orchestration of dev/test backend server and service application infrastructure, as well as front-end client device and software testing and simulation.| +|API management|API Gateway|[API Management](https://azure.microsoft.com/services/api-management/)|Allows developers to create, publish, maintain, monitor, and secure APIs. Handles processing concurrent API calls, including traffic management, authorization, access control, monitoring, and API version management.| +|Application streaming|AppStream|[RemoteApp](https://www.remoteapp.windowsazure.com/Default.aspx)|Streams and delivers existing applications from the cloud to reach more users on more devices—without any code modifications.| +|Search|CloudSearch|[Search](https://azure.microsoft.com/services/search/)|Sets up, manages, and scales a search solution for websites and applications.| +|Media transcoding|Elastic Transcoder|[Encoding](https://azure.microsoft.com/services/media-services/encoding/)|A media transcoding service in the cloud that transcodes media files from their source format into versions that play back on devices such as smartphones, tablets, and PCs.| +|Streaming|** **|[Live and on-demand streaming](https://azure.microsoft.com/services/media-services/live-on-demand/)|Delivers content to virtually any device. Offers scalable streaming.| +|Others|** **|- [Media Player](https://azure.microsoft.com/services/media-services/media-player/)
    - [Media Indexer](https://azure.microsoft.com/services/media-services/media-indexer/)
    - [Content Protection](https://azure.microsoft.com/services/media-services/content-protection/)|Additional services related to the playing, protection, and analysis of the content within the media service.| + +## Enterprise applications + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Productivity software|WorkSpaces
    WorkMail
    WorkDocs|[Office 365](https://products.office.com/)|Provides communication, collaboration, and document management services in the cloud.| + +## Internet of things (IoT) services + +|Subcategory|AWS Service|Azure Service|Description| +|--- |--- |--- |--- | +|Streaming data|Kinesis Firehose
    Kinesis Streams|[Event Hubs](https://azure.microsoft.com/services/event-hubs/)|Services that allow the mass ingestion of small data inputs, typically from devices and sensors, to process and route the data.| +|Internet of Things|IoT (Preview)|[IoT Hub](https://azure.microsoft.com/services/iot-hub/)|Lets connected devices interact with cloud applications and other devices to capture and analyze real-time data.| diff --git a/docs/aws-professional/toc.md b/docs/aws-professional/toc.md new file mode 100644 index 00000000000..884ff7a9b20 --- /dev/null +++ b/docs/aws-professional/toc.md @@ -0,0 +1,3 @@ +# Azure for AWS Professionals +## [Overview](./index.md) +## [Services comparison](./services.md) \ No newline at end of file diff --git a/docs/best-practices/api-design.md b/docs/best-practices/api-design.md new file mode 100644 index 00000000000..c1e72a10690 --- /dev/null +++ b/docs/best-practices/api-design.md @@ -0,0 +1,566 @@ +--- +title: API design guidance +description: Guidance upon how to create a well designed API. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 19514a32-923a-488c-85f5-b5beec2576de +ms.service: best-practice +ms.devlang: rest-api +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# API design +[!INCLUDE [header](../_includes/header.md)] + +Many modern web-based solutions make the use of web services, hosted by web servers, to provide functionality for remote client applications. The operations that a web service exposes constitute a web API. A well-designed web API should aim to support: + +* **Platform independence**. Client applications should be able to utilize the API that the web service provides without requiring how the data or operations that API exposes are physically implemented. This requires that the API abides by common standards that enable a client application and web service to agree on which data formats to use, and the structure of the data that is exchanged between client applications and the web service. +* **Service evolution**. The web service should be able to evolve and add (or remove) functionality independently from client applications. Existing client applications should be able to continue to operate unmodified as the features provided by the web service change. All functionality should also be discoverable, so that client applications can fully utilize it. + +The purpose of this guidance is to describe the issues that you should consider when designing a web API. + +## Introduction to Representational State Transfer (REST) +In his dissertation in 2000, Roy Fielding proposed an alternative architectural approach to structuring the operations exposed by web services; REST. REST is an architectural style for building distributed systems based on hypermedia. A primary advantage of the REST model is that it is based on open standards and does not bind the implementation of the model or the client applications that access it to any specific implementation. For example, a REST web service could be implemented by using the Microsoft ASP.NET Web API, and client applications could be developed by using any language and toolset that can generate HTTP requests and parse HTTP responses. + +> [!NOTE] +> REST is actually independent of any underlying protocol and is not necessarily tied to HTTP. However, most common implementations of systems that are based on REST utilize HTTP as the application protocol for sending and receiving requests. This document focuses on mapping REST principles to systems designed to operate using HTTP. +> +> + +The REST model uses a navigational scheme to represent objects and services over a network (referred to as *resources*). Many systems that implement REST typically use the HTTP protocol to transmit requests to access these resources. In these systems, a client application submits a request in the form of a URI that identifies a resource, and an HTTP method (the most common being GET, POST, PUT, or DELETE) that indicates the operation to be performed on that resource. The body of the HTTP request contains the data required to perform the operation. The important point to understand is that REST defines a stateless request model. HTTP requests should be independent and may occur in any order, so attempting to retain transient state information between requests is not feasible. The only place where information is stored is in the resources themselves, and each request should be an atomic operation. Effectively, a REST model implements a finite state machine where a request transitions a resource from one well-defined non-transient state to another. + +> [!NOTE] +> The stateless nature of individual requests in the REST model enables a system constructed by following these principles to be highly scalable. There is no need to retain any affinity between a client application making a series of requests and the specific web servers handling those requests. +> +> + +Another crucial point in implementing an effective REST model is to understand the relationships between the various resources to which the model provides access. These resources are typically organized as collections and relationships. For example, suppose that a quick analysis of an ecommerce system shows that there are two collections in which client applications are likely to be interested: orders and customers. Each order and customer should have its own unique key for identification purposes. The URI to access the collection of orders could be something as simple as */orders*, and similarly the URI for retrieving all customers could be */customers*. Issuing an HTTP GET request to the */orders* URI should return a list representing all orders in the collection encoded as an HTTP response: + +```HTTP +GET http://adventure-works.com/orders HTTP/1.1 +... +``` + +The response shown below encodes the orders as a JSON list structure: + +```HTTP +HTTP/1.1 200 OK +... +Date: Fri, 22 Aug 2014 08:49:02 GMT +Content-Length: ... +[{"orderId":1,"orderValue":99.90,"productId":1,"quantity":1},{"orderId":2,"orderValue":10.00,"productId":4,"quantity":2},{"orderId":3,"orderValue":16.60,"productId":2,"quantity":4},{"orderId":4,"orderValue":25.90,"productId":3,"quantity":1},{"orderId":5,"orderValue":99.90,"productId":1,"quantity":1}] +``` +To fetch an individual order requires specifying the identifier for the order from the *orders* resource, such as */orders/2*: + +```HTTP +GET http://adventure-works.com/orders/2 HTTP/1.1 +... +``` + +```HTTP +HTTP/1.1 200 OK +... +Date: Fri, 22 Aug 2014 08:49:02 GMT +Content-Length: ... +{"orderId":2,"orderValue":10.00,"productId":4,"quantity":2} +``` + +> [!NOTE] +> For simplicity, these examples show the information in responses being returned as JSON text data. However, there is no reason why resources should not contain any other type of data supported by HTTP, such as binary or encrypted information; the content-type in the HTTP response should specify the type. Also, a REST model may be able to return the same data in different formats, such as XML or JSON. In this case, the web service should be able to perform content negotiation with the client making the request. The request can include an *Accept* header which specifies the preferred format that the client would like to receive and the web service should attempt to honor this format if at all possible. +> +> + +Notice that the response from a REST request makes use of the standard HTTP status codes. For example, a request that returns valid data should include the HTTP response code 200 (OK), while a request that fails to find or delete a specified resource should return a response that includes the HTTP status code 404 (Not Found). + +## Design and structure of a RESTful web API +The keys to designing a successful web API are simplicity and consistency. A Web API that exhibits these two factors makes it easier to build client applications that need to consume the API. + +A RESTful web API is focused on exposing a set of connected resources, and providing the core operations that enable an application to manipulate these resources and easily navigate between them. For this reason, the URIs that constitute a typical RESTful web API should be oriented towards the data that it exposes, and use the facilities provided by HTTP to operate on this data. This approach requires a different mindset from that typically employed when designing a set of classes in an object-oriented API which tends to be more motivated by the behavior of objects and classes. Additionally, a RESTful web API should be stateless and not depend on operations being invoked in a particular sequence. The following sections summarize the points you should consider when designing a RESTful web API. + +### Organizing the web API around resources +> [!TIP] +> The URIs exposed by a REST web service should be based on nouns (the data to which the web API provides access) and not verbs (what an application can do with the data). +> +> + +Focus on the business entities that the web API exposes. For example, in a web API designed to support the ecommerce system described earlier, the primary entities are customers and orders. Processes such as the act of placing an order can be achieved by providing an HTTP POST operation that takes the order information and adds it to the list of orders for the customer. Internally, this POST operation can perform tasks such as checking stock levels, and billing the customer. The HTTP response can indicate whether the order was placed successfully or not. Also note that a resource does not have to be based on a single physical data item. As an example, an order resource might be implemented internally by using information aggregated from many rows spread across several tables in a relational database but presented to the client as a single entity. + +> [!TIP] +> Avoid designing a REST interface that mirrors or depends on the internal structure of the data that it exposes. REST is about more than implementing simple CRUD (Create, Retrieve, Update, Delete) operations over separate tables in a relational database. The purpose of REST is to map business entities and the operations that an application can perform on these entities to the physical implementation of these entities, but a client should not be exposed to these physical details. +> +> + +Individual business entities rarely exist in isolation (although some singleton objects may exist), but instead tend to be grouped together into collections. In REST terms, each entity and each collection are resources. In a RESTful web API, each collection has its own URI within the web service, and performing an HTTP GET request over a URI for a collection retrieves a list of items in that collection. Each individual item also has its own URI, and an application can submit another HTTP GET request using that URI to retrieve the details of that item. You should organize the URIs for collections and items in a hierarchical manner. In the ecommerce system, the URI */customers* denotes the customer’s collection, and */customers/5* retrieves the details for the single customer with the ID 5 from this collection. This approach helps to keep the web API intuitive. + +> [!TIP] +> Adopt a consistent naming convention in URIs; in general it helps to use plural nouns for URIs that reference collections. +> +> + +You also need to consider the relationships between different types of resources and how you might expose these associations. For example, customers may place zero or more orders. A natural way to represent this relationship would be through a URI such as */customers/5/orders* to find all the orders for customer 5. You might also consider representing the association from an order back to a specific customer through a URI such as */orders/99/customer* to find the customer for order 99, but extending this model too far can become cumbersome to implement. A better solution is to provide navigable links to associated resources, such as the customer, in the body of the HTTP response message returned when the order is queried. This mechanism is described in more detail in the section Using the HATEOAS Approach to Enable Navigation To Related Resources later in this guidance. + +In more complex systems there may be many more types of entity, and it can be tempting to provide URIs that enable a client application to navigate through several levels of relationships, such as */customers/1/orders/99/products* to obtain the list of products in order 99 placed by customer 1. However, this level of complexity can be difficult to maintain and is inflexible if the relationships between resources change in the future. Rather, you should seek to keep URIs relatively simple. Bear in mind that once an application has a reference to a resource, it should be possible to use this reference to find items related to that resource. The preceding query can be replaced with the URI */customers/1/orders* to find all the orders for customer 1, and then query the URI */orders/99/products* to find the products in this order (assuming order 99 was placed by customer 1). + +> [!TIP] +> Avoid requiring resource URIs more complex than *collection/item/collection*. +> +> + +Another point to consider is that all web requests impose a load on the web server, and the greater the number of requests the bigger the load. You should attempt to define your resources to avoid “chatty” web APIs that expose a large number of small resources. Such an API may require a client application to submit multiple requests to find all the data that it requires. It may be beneficial to denormalize data and combine related information together into bigger resources that can be retrieved by issuing a single request. However, you need to balance this approach against the overhead of fetching data that might not be frequently required by the client. Retrieving large objects can increase the latency of a request and incur additional bandwidth costs for little advantage if the additional data is not often used. + +Avoid introducing dependencies between the web API to the structure, type, or location of the underlying data sources. For example, if your data is located in a relational database, the web API does not need to expose each table as a collection of resources. Think of the web API as an abstraction of the database, and if necessary introduce a mapping layer between the database and the web API. In this way, if the design or implementation of the database changes (for example, you move from a relational database containing a collection of normalized tables to a denormalized NoSQL storage system such as a document database) client applications are insulated from these changes. + +> [!TIP] +> The source of the data that underpins a web API does not have to be a data store; it could be another service or line-of-business application or even a legacy application running on-premises within an organization. +> +> + +Finally, it might not be possible to map every operation implemented by a web API to a specific resource. You can handle such *non-resource* scenarios through HTTP GET requests that invoke a piece of functionality and return the results as an HTTP response message. A web API that implements simple calculator-style operations such as add and subtract could provide URIs that expose these operations as pseudo resources and utilize the query string to specify the parameters required. For example a GET request to the URI */add?operand1=99&operand2=1* could return a response message with the body containing the value 100, and GET request to the URI */subtract?operand1=50&operand2=20* could return a response message with the body containing the value 30. However, only use these forms of URIs sparingly. + +### Defining operations in terms of HTTP methods +The HTTP protocol defines a number of methods that assign semantic meaning to a request. The common HTTP methods used by most RESTful web APIs are: + +* **GET**, to retrieve a copy of the resource at the specified URI. The body of the response message contains the details of the requested resource. +* **POST**, to create a new resource at the specified URI. The body of the request message provides the details of the new resource. Note that POST can also be used to trigger operations that don't actually create resources. +* **PUT**, to replace or update the resource at the specified URI. The body of the request message specifies the resource to be modified and the values to be applied. +* **DELETE**, to remove the resource at the specified URI. + +> [!NOTE] +> The HTTP protocol also defines other less commonly-used methods, such as PATCH which is used to request selective updates to a resource, HEAD which is used to request a description of a resource, OPTIONS which enables a client information to obtain information about the communication options supported by the server, and TRACE which allows a client to request information that it can use for testing and diagnostics purposes. +> +> + +The effect of a specific request should depend on whether the resource to which it is applied is a collection or an individual item. The following table summarizes the common conventions adopted by most RESTful implementations using the ecommerce example. Note that not all of these requests might be implemented; it depends on the specific scenario. + +| **Resource** | **POST** | **GET** | **PUT** | **DELETE** | +| --- | --- | --- | --- | --- | +| /customers |Create a new customer |Retrieve all customers |Bulk update of customers (*if implemented*) |Remove all customers | +| /customers/1 |Error |Retrieve the details for customer 1 |Update the details of customer 1 if it exists, otherwise return an error |Remove customer 1 | +| /customers/1/orders |Create a new order for customer 1 |Retrieve all orders for customer 1 |Bulk update of orders for customer 1 (*if implemented*) |Remove all orders for customer 1(*if implemented*) | + +The purpose of GET and DELETE requests are relatively straightforward, but there is scope for confusion concerning the purpose and effects of POST and PUT requests. + +A POST request should create a new resource with data provided in the body of the request. In the REST model, you frequently apply POST requests to resources that are collections; the new resource is added to the collection. + +> [!NOTE] +> You can also define POST requests that trigger some functionality (and that don't necessarily return data), and these types of request can be applied to collections. For example you could use a POST request to pass a timesheet to a payroll processing service and get the calculated taxes back as a response. +> +> + +A PUT request is intended to modify an existing resource. If the specified resource does not exist, the PUT request could return an error (in some cases, it might actually create the resource). PUT requests are most frequently applied to resources that are individual items (such as a specific customer or order), although they can be applied to collections, although this is less-commonly implemented. Note that PUT requests are idempotent whereas POST requests are not; if an application submits the same PUT request multiple times the results should always be the same (the same resource will be modified with the same values), but if an application repeats the same POST request the result will be the creation of multiple resources. + +> [!NOTE] +> Strictly speaking, an HTTP PUT request replaces an existing resource with the resource specified in the body of the request. If the intention is to modify a selection of properties in a resource but leave other properties unchanged, then this should be implemented by using an HTTP PATCH request. However, many RESTful implementations relax this rule and use PUT for both situations. +> +> + +### Processing HTTP requests +The data included by a client application in many HTTP requests, and the corresponding response messages from the web server, could be presented in a variety of formats (or media types). For example, the data that specifies the details for a customer or order could be provided as XML, JSON, or some other encoded and compressed format. A RESTful web API should support different media types as requested by the client application that submits a request. + +When a client application sends a request that returns data in the body of a message, it can specify the media types it can handle in the Accept header of the request. The following code illustrates an HTTP GET request that retrieves the details of customer 1 and requests the result to be returned as JSON (the client should still examine the media type of the data in the response to verify the format of the data returned): + +```HTTP +GET http://adventure-works.com/orders/2 HTTP/1.1 +... +Accept: application/json +... +``` + +If the web server supports this media type, it can reply with a response that includes Content-Type header that specifies the format of the data in the body of the message: + +> [!NOTE] +> For maximum interoperability, the media types referenced in the Accept and Content-Type headers should be recognized MIME types rather than some custom media type. +> +> + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +Content-Length: ... +{"orderID":2,"productID":4,"quantity":2,"orderValue":10.00} +``` + +If the web server does not support the requested media type, it can send the data in a different format. IN all cases it must specify the media type (such as *application/json*) in the Content-Type header. It is the responsibility of the client application to parse the response message and interpret the results in the message body appropriately. + +Note that in this example, the web server successfully retrieves the requested data and indicates success by passing back a status code of 200 in the response header. If no matching data is found, it should instead return a status code of 404 (not found) and the body of the response message can contain additional information. The format of this information is specified by the Content-Type header, as shown in the following example: + +```HTTP +GET http://adventure-works.com/orders/222 HTTP/1.1 +... +Accept: application/json +... +``` + +Order 222 does not exist, so the response message looks like this: + +```HTTP +HTTP/1.1 404 Not Found +... +Content-Type: application/json; charset=utf-8 +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +Content-Length: ... +{"message":"No such order"} +``` + +When an application sends an HTTP PUT request to update a resource, it specifies the URI of the resource and provides the data to be modified in the body of the request message. It should also specify the format of this data by using the Content-Type header. A common format used for text-based information is *application/x-www-form-urlencoded*, which comprises a set of name/value pairs separated by the & character. The next example shows an HTTP PUT request that modifies the information in order 1: + +```HTTP +PUT http://adventure-works.com/orders/1 HTTP/1.1 +... +Content-Type: application/x-www-form-urlencoded +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +Content-Length: ... +ProductID=3&Quantity=5&OrderValue=250 +``` + +If the modification is successful, it should ideally respond with an HTTP 204 status code, indicating that the process has been successfully handled, but that the response body contains no further information. The Location header in the response contains the URI of the newly updated resource: + +```HTTP +HTTP/1.1 204 No Content +... +Location: http://adventure-works.com/orders/1 +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +``` + +> [!TIP] +> If the data in an HTTP PUT request message includes date and time information, make sure that your web service accepts dates and times formatted following the ISO 8601 standard. +> +> + +If the resource to be updated does not exist, the web server can respond with a Not Found response as described earlier. Alternatively, if the server actually creates the object itself it could return the status codes HTTP 200 (OK) or HTTP 201 (Created) and the response body could contain the data for the new resource. If the Content-Type header of the request specifies a data format that the web server cannot handle, it should respond with HTTP status code 415 (Unsupported Media Type). + +> [!TIP] +> Consider implementing bulk HTTP PUT operations that can batch updates to multiple resources in a collection. The PUT request should specify the URI of the collection, and the request body should specify the details of the resources to be modified. This approach can help to reduce chattiness and improve performance. +> +> + +The format of an HTTP POST requests that create new resources are similar to those of PUT requests; the message body contains the details of the new resource to be added. However, the URI typically specifies the collection to which the resource should be added. The following example creates a new order and adds it to the orders collection: + +```HTTP +POST http://adventure-works.com/orders HTTP/1.1 +... +Content-Type: application/x-www-form-urlencoded +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +Content-Length: ... +productID=5&quantity=15&orderValue=400 +``` + +If the request is successful, the web server should respond with a message code with HTTP status code 201 (Created). The Location header should contain the URI of the newly created resource, and the body of the response should contain a copy of the new resource; the Content-Type header specifies the format of this data: + +```HTTP +HTTP/1.1 201 Created +... +Content-Type: application/json; charset=utf-8 +Location: http://adventure-works.com/orders/99 +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +Content-Length: ... +{"orderID":99,"productID":5,"quantity":15,"orderValue":400} +``` + +> [!TIP] +> If the data provided by a PUT or POST request is invalid, the web server should respond with a message with HTTP status code 400 (Bad Request). The body of this message can contain additional information about the problem with the request and the formats expected, or it can contain a link to a URL that provides more details. +> +> + +To remove a resource, an HTTP DELETE request simply provides the URI of the resource to be deleted. The following example attempts to remove order 99: + +```HTTP +DELETE http://adventure-works.com/orders/99 HTTP/1.1 +... +``` + +If the delete operation is successful, the web server should respond with HTTP status code 204, indicating that the process has been successfully handled, but that the response body contains no further information (this is the same response returned by a successful PUT operation, but without a Location header as the resource no longer exists.) It is also possible for a DELETE request to return HTTP status code 200 (OK) or 202 (Accepted) if the deletion is performed asynchronously. + +```HTTP +HTTP/1.1 204 No Content +... +Date: Fri, 22 Aug 2014 09:18:37 GMT +``` + +If the resource is not found, the web server should return a 404 (Not Found) message instead. + +> [!TIP] +> If all the resources in a collection need to be deleted, enable an HTTP DELETE request to be specified for the URI of the collection rather than forcing an application to remove each resource in turn from the collection. +> +> + +### Filtering and paginating data +You should endeavor to keep the URIs simple and intuitive. Exposing a collection of resources through a single URI assists in this respect, but it can lead to applications fetching large amounts of data when only a subset of the information is required. Generating a large volume of traffic impacts not only the performance and scalability of the web server but also adversely affect the responsiveness of client applications requesting the data. + +For example, if orders contain the price paid for the order, a client application that needs to retrieve all orders that have a cost over a specific value might need to retrieve all orders from the */orders* URI and then filter these orders locally. Clearly this process is highly inefficient; it wastes network bandwidth and processing power on the server hosting the web API. + +One solution may be to provide a URI scheme such as */orders/ordervalue_greater_than_n* where *n* is the order price, but for all but a limited number of prices such an approach is impractical. Additionally, if you need to query orders based on other criteria, you can end up being faced with providing with a long list of URIs with possibly non-intuitive names. + +A better strategy to filtering data is to provide the filter criteria in the query string that is passed to the web API, such as */orders?ordervaluethreshold=n*. In this example, the corresponding operation in the web API is responsible for parsing and handling the `ordervaluethreshold` parameter in the query string and returning the filtered results in the HTTP response. + +Some simple HTTP GET requests over collection resources could potentially return a large number of items. To combat the possibility of this occurring you should design the web API to limit the amount of data returned by any single request. You can achieve this by supporting query strings that enable the user to specify the maximum number of items to be retrieved (which could itself be subject to an upperbound limit to help prevent Denial of Service attacks), and a starting offset into the collection. For example, the query string in the URI */orders?limit=25&offset=50* should retrieve 25 orders starting with the 50th order found in the orders collection. As with filtering data, the operation that implements the GET request in the web API is responsible for parsing and handling the `limit` and `offset` parameters in the query string. To assist client applications, GET requests that return paginated data should also include some form of metadata that indicate the total number of resources available in the collection. You might also consider other intelligent paging strategies; for more information, see [API Design Notes: Smart Paging](http://bizcoder.com/api-design-notes-smart-paging) + +You can follow a similar strategy for sorting data as it is fetched; you could provide a sort parameter that takes a field name as the value, such as */orders?sort=ProductID*. However, note that this approach can have a deleterious effect on caching (query string parameters form part of the resource identifier used by many cache implementations as the key to cached data). + +You can extend this approach to limit (project) the fields returned if a single resource item contains a large amount of data. For example, you could use a query string parameter that accepts a comma-delimited list of fields, such as */orders?fields=ProductID,Quantity*. + +> [!TIP] +> Give all optional parameters in query strings meaningful defaults. For example, set the `limit` parameter to 10 and the `offset` parameter to 0 if you implement pagination, set the sort parameter to the key of the resource if you implement ordering, and set the `fields` parameter to all fields in the resource if you support projections. +> +> + +### Handling large binary resources +A single resource may contain large binary fields, such as files or images. To overcome the transmission problems caused by unreliable and intermittent connections and to improve response times, consider providing operations that enable such resources to be retrieved in chunks by the client application. To do this, the web API should support the Accept-Ranges header for GET requests for large resources, and ideally implement HTTP HEAD requests for these resources. The Accept-Ranges header indicates that the GET operation supports partial results, and that a client application can submit GET requests that return a subset of a resource specified as a range of bytes. A HEAD request is similar to a GET request except that it only returns a header that describes the resource and an empty message body. A client application can issue a HEAD request to determine whether to fetch a resource by using partial GET requests. The following example shows a HEAD request that obtains information about a product image: + +```HTTP +HEAD http://adventure-works.com/products/10?fields=productImage HTTP/1.1 +... +``` + +The response message contains a header that includes the size of the resource (4580 bytes), and the Accept-Ranges header that the corresponding GET operation supports partial results: + +```HTTP +HTTP/1.1 200 OK +... +Accept-Ranges: bytes +Content-Type: image/jpeg +Content-Length: 4580 +... +``` + +The client application can use this information to construct a series of GET operations to retrieve the image in smaller chunks. The first request fetches the first 2500 bytes by using the Range header: + +```HTTP +GET http://adventure-works.com/products/10?fields=productImage HTTP/1.1 +Range: bytes=0-2499 +... +``` + +The response message indicates that this is a partial response by returning HTTP status code 206. The Content-Length header specifies the actual number of bytes returned in the message body (not the size of the resource), and the Content-Range header indicates which part of the resource this is (bytes 0-2499 out of 4580): + +```HTTP +HTTP/1.1 206 Partial Content +... +Accept-Ranges: bytes +Content-Type: image/jpeg +Content-Length: 2500 +Content-Range: bytes 0-2499/4580 +... +_{binary data not shown}_ +``` + +A subsequent request from the client application can retrieve the remainder of the resource by using an appropriate Range header: + +```HTTP +GET http://adventure-works.com/products/10?fields=productImage HTTP/1.1 +Range: bytes=2500- +... +``` + +The corresponding result message should look like this: + +```HTTP +HTTP/1.1 206 Partial Content +... +Accept-Ranges: bytes +Content-Type: image/jpeg +Content-Length: 2080 +Content-Range: bytes 2500-4580/4580 +... +``` + +## Using the HATEOAS approach to enable navigation to related resources +One of the primary motivations behind REST is that it should be possible to navigate the entire set of resources without requiring prior knowledge of the URI scheme. Each HTTP GET request should return the information necessary to find the resources related directly to the requested object through hyperlinks included in the response, and it should also be provided with information that describes the operations available on each of these resources. This principle is known as HATEOAS, or Hypertext as the Engine of Application State. The system is effectively a finite state machine, and the response to each request contains the information necessary to move from one state to another; no other information should be necessary. + +> [!NOTE] +> Currently there are no standards or specifications that define how to model the HATEOAS principle. The examples shown in this section illustrate one possible solution. +> +> + +As an example, to handle the relationship between customers and orders, the data returned in the response for a specific order should contain URIs in the form of a hyperlink identifying the customer that placed the order, and the operations that can be performed on that customer. + +```HTTP +GET http://adventure-works.com/orders/3 HTTP/1.1 +Accept: application/json +... +``` + +The body of the response message contains a `links` array (highlighted in the code example) that specifies the nature of the relationship (*Customer*), the URI of the customer (*http://adventure-works.com/customers/3*), how to retrieve the details of this customer (*GET*), and the MIME types that the web server supports for retrieving this information (*text/xml* and *application/json*). This is all the information that a client application needs to be able to fetch the details of the customer. Additionally, the Links array also includes links for the other operations that can be performed, such as PUT (to modify the customer, together with the format that the web server expects the client to provide), and DELETE. + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"orderID":3,"productID":2,"quantity":4,"orderValue":16.60,"links":[(some links omitted){"rel":"customer","href":" http://adventure-works.com/customers/3", "action":"GET","types":["text/xml","application/json"]},{"rel":" +customer","href":" http://adventure-works.com /customers/3", "action":"PUT","types":["application/x-www-form-urlencoded"]},{"rel":"customer","href":" http://adventure-works.com /customers/3","action":"DELETE","types":[]}]} +``` + +For completeness, the Links array should also include self-referencing information pertaining to the resource that has been retrieved. These links have been omitted from the previous example, but are highlighted in the following code. Notice that in these links, the relationship *self* has been used to indicate that this is a reference to the resource being returned by the operation: + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"orderID":3,"productID":2,"quantity":4,"orderValue":16.60,"links":[{"rel":"self","href":" http://adventure-works.com/orders/3", "action":"GET","types":["text/xml","application/json"]},{"rel":" self","href":" http://adventure-works.com /orders/3", "action":"PUT","types":["application/x-www-form-urlencoded"]},{"rel":"self","href":" http://adventure-works.com /orders/3", "action":"DELETE","types":[]},{"rel":"customer", +"href":" http://adventure-works.com /customers/3", "action":"GET","types":["text/xml","application/json"]},{"rel":" customer" (customer links omitted)}]} +``` + +For this approach to be effective, client applications must be prepared to retrieve and parse this additional information. + +## Versioning a RESTful web API +It is highly unlikely that in all but the simplest of situations that a web API will remain static. As business requirements change new collections of resources may be added, the relationships between resources might change, and the structure of the data in resources might be amended. While updating a web API to handle new or differing requirements is a relatively straightforward process, you must consider the effects that such changes will have on client applications consuming the web API. The issue is that although the developer designing and implementing a web API has full control over that API, the developer does not have the same degree of control over client applications which may be built by third party organizations operating remotely. The primary imperative is to enable existing client applications to continue functioning unchanged while allowing new client applications to take advantage of new features and resources. + +Versioning enables a web API to indicate the features and resources that it exposes, and a client application can submit requests that are directed to a specific version of a feature or resource. The following sections describe several different approaches, each of which has its own benefits and trade-offs. + +### No versioning +This is the simplest approach, and may be acceptable for some internal APIs. Big changes could be represented as new resources or new links. Adding content to existing resources might not present a breaking change as client applications that are not expecting to see this content will simply ignore it. + +For example, a request to the URI *http://adventure-works.com/customers/3* should return the details of a single customer containing `id`, `name`, and `address` fields expected by the client application: + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","address":"1 Microsoft Way Redmond WA 98053"} +``` + +> [!NOTE] +> For the purposes of simplicity and clarity, the example responses shown in this section do not include HATEOAS links. +> +> + +If the `DateCreated` field is added to the schema of the customer resource, then the response would look like this: + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","dateCreated":"2014-09-04T12:11:38.0376089Z","address":"1 Microsoft Way Redmond WA 98053"} +``` + +Existing client applications might continue functioning correctly if they are capable of ignoring unrecognized fields, while new client applications can be designed to handle this new field. However, if more radical changes to the schema of resources occur (such as removing or renaming fields) or the relationships between resources change then these may constitute breaking changes that prevent existing client applications from functioning correctly. In these situations you should consider one of the following approaches. + +### URI versioning +Each time you modify the web API or change the schema of resources, you add a version number to the URI for each resource. The previously existing URIs should continue to operate as before, returning resources that conform to their original schema. + +Extending the previous example, if the `address` field is restructured into sub-fields containing each constituent part of the address (such as `streetAddress`, `city`, `state`, and `zipCode`), this version of the resource could be exposed through a URI containing a version number, such as http://adventure-works.com/v2/customers/3: + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","dateCreated":"2014-09-04T12:11:38.0376089Z","address":{"streetAddress":"1 Microsoft Way","city":"Redmond","state":"WA","zipCode":98053}} +``` + +This versioning mechanism is very simple but depends on the server routing the request to the appropriate endpoint. However, it can become unwieldy as the web API matures through several iterations and the server has to support a number of different versions. Also, from a purist’s point of view, in all cases the client applications are fetching the same data (customer 3), so the URI should not really be different depending on the version. This scheme also complicates implementation of HATEOAS as all links will need to include the version number in their URIs. + +### Query string versioning +Rather than providing multiple URIs, you can specify the version of the resource by using a parameter within the query string appended to the HTTP request, such as *http://adventure-works.com/customers/3?version=2*. The version parameter should default to a meaningful value such as 1 if it is omitted by older client applications. + +This approach has the semantic advantage that the same resource is always retrieved from the same URI, but it depends on the code that handles the request to parse the query string and send back the appropriate HTTP response. This approach also suffers from the same complications for implementing HATEOAS as the URI versioning mechanism. + +> [!NOTE] +> Some older web browsers and web proxies will not cache responses for requests that include a query string in the URL. This can have an adverse impact on performance for web applications that use a web API and that run from within such a web browser. +> +> + +### Header versioning +Rather than appending the version number as a query string parameter, you could implement a custom header that indicates the version of the resource. This approach requires that the client application adds the appropriate header to any requests, although the code handling the client request could use a default value (version 1) if the version header is omitted. The following examples utilize a custom header named *Custom-Header*. The value of this header indicates the version of web API. + +Version 1: + +```HTTP +GET http://adventure-works.com/customers/3 HTTP/1.1 +... +Custom-Header: api-version=1 +... +``` + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","address":"1 Microsoft Way Redmond WA 98053"} +``` + +Version 2: + +```HTTP +GET http://adventure-works.com/customers/3 HTTP/1.1 +... +Custom-Header: api-version=2 +... +``` + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","dateCreated":"2014-09-04T12:11:38.0376089Z","address":{"streetAddress":"1 Microsoft Way","city":"Redmond","state":"WA","zipCode":98053}} +``` + +Note that as with the previous two approaches, implementing HATEOAS requires including the appropriate custom header in any links. + +### Media type versioning +When a client application sends an HTTP GET request to a web server it should stipulate the format of the content that it can handle by using an Accept header, as described earlier in this guidance. Frequently the purpose of the *Accept* header is to allow the client application to specify whether the body of the response should be XML, JSON, or some other common format that the client can parse. However, it is possible to define custom media types that include information enabling the client application to indicate which version of a resource it is expecting. The following example shows a request that specifies an *Accept* header with the value *application/vnd.adventure-works.v1+json*. The *vnd.adventure-works.v1* element indicates to the web server that it should return version 1 of the resource, while the *json* element specifies that the format of the response body should be JSON: + +```HTTP +GET http://adventure-works.com/customers/3 HTTP/1.1 +... +Accept: application/vnd.adventure-works.v1+json +... +``` + +The code handling the request is responsible for processing the *Accept* header and honoring it as far as possible (the client application may specify multiple formats in the *Accept* header, in which case the web server can choose the most appropriate format for the response body). The web server confirms the format of the data in the response body by using the Content-Type header: + +```HTTP +HTTP/1.1 200 OK +... +Content-Type: application/vnd.adventure-works.v1+json; charset=utf-8 +... +Content-Length: ... +{"id":3,"name":"Contoso LLC","address":"1 Microsoft Way Redmond WA 98053"} +``` + +If the Accept header does not specify any known media types, the web server could generate an HTTP 406 (Not Acceptable) response message or return a message with a default media type. + +This approach is arguably the purest of the versioning mechanisms and lends itself naturally to HATEOAS, which can include the MIME type of related data in resource links. + +> [!NOTE] +> When you select a versioning strategy, you should also consider the implications on performance, especially caching on the web server. The URI versioning and Query String versioning schemes are cache-friendly inasmuch as the same URI/query string combination refers to the same data each time. +> +> The Header versioning and Media Type versioning mechanisms typically require additional logic to examine the values in the custom header or the Accept header. In a large-scale environment, many clients using different versions of a web API can result in a significant amount of duplicated data in a server-side cache. This issue can become acute if a client application communicates with a web server through a proxy that implements caching, and that only forwards a request to the web server if it does not currently hold a copy of the requested data in its cache. +> +> + +## More information +* The [RESTful Cookbook](http://restcookbook.com/) contains an introduction to building RESTful APIs. +* The Web [API Checklist](https://mathieu.fenniak.net/the-api-checklist/) contains a useful list of items to consider when designing and implementing a Web API. diff --git a/docs/best-practices/api-implementation.md b/docs/best-practices/api-implementation.md new file mode 100644 index 00000000000..b3479bd7c17 --- /dev/null +++ b/docs/best-practices/api-implementation.md @@ -0,0 +1,1145 @@ +--- +title: API implementation guidance +description: Guidance upon how to implement an API. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 93d6a18b-de51-4b3c-9cb1-35eefb6c1747 +ms.service: best-practice +ms.devlang: rest-api +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# API implementation +[!INCLUDE [header](../_includes/header.md)] + +A carefully-designed RESTful web API defines the resources, relationships, and navigation schemes that are accessible to client applications. When you implement and deploy a web API, you should consider the physical requirements of the environment hosting the web API and the way in which the web API is constructed rather than the logical structure of the data. This guidance focusses on best practices for implementing a web API and publishing it to make it available to client applications. Security concerns are described separately in the API Security Guidance document. You can find detailed information about web API design in the API Design Guidance document. + +## Considerations for implementing a RESTful web API +The following sections illustrate best practice for using the ASP.NET Web API template to build a RESTful web API. For detailed information on using the Web API template, visit the [Learn About ASP.NET Web API](http://www.asp.net/web-api) page on the Microsoft website. + +## Considerations for implementing request routing +In a service implemented by using the ASP.NET Web API, each request is routed to a method in a *controller* class. The Web API framework provides two primary options for implementing routing; *convention-based* routing and *attribute-based* routing. Consider the following points when you determine the best way to route requests in your web API: + +* **Understand the limitations and requirements of convention-based routing**. + + By default, the Web API framework uses convention-based routing. The Web API framework creates an initial routing table that contains the following entry: + + ```C# + config.Routes.MapHttpRoute( + name: "DefaultApi", + routeTemplate: "api/{controller}/{id}", + defaults: new { id = RouteParameter.Optional } + ); + ``` + + Routes can be generic, comprising literals such as *api* and variables such as *{controller}* and *{id}*. Convention-based routing allows some elements of the route to be optional. The Web API framework determines which method to invoke in the controller by matching the HTTP method in the request to the initial part of the method name in the API, and then by matching any optional parameters. For example, if a controller named *orders* contains the methods *GetAllOrders()* or *GetOrderByInt(int id)* then the GET request *http://www.adventure-works.com/api/orders/* will be directed to the method *GetAlllOrders()* and the GET request *http://www.adventure-works.com/api/orders/99* will be routed to the method *GetOrderByInt(int id)*. If there is no matching method available that begins with the prefix Get in the controller, the Web API framework replies with an HTTP 405 (Method Not Allowed) message. Additionally, name of the parameter (id) specified in the routing table must be the same as the name of the parameter for the *GetOrderById* method, otherwise the Web API framework will reply with an HTTP 404 (Not Found) response. + + The same rules apply to POST, PUT, and DELETE HTTP requests; a PUT request that updates the details of order 101 would be directed to the URI *http://www.adventure-works.com/api/orders/101*, the body of the message will contain the new details of the order, and this information will be passed as a parameter to a method in the orders controller with a name that starts with the prefix *Put*, such as *PutOrder*. + + The default routing table will not match a request that references child resources in a RESTful web API, such as *http://www.adventure-works.com/api/customers/1/orders* (find the details of all orders placed by customer 1). To handle these cases, you can add custom routes to the routing table: + + ```C# + config.Routes.MapHttpRoute( + name: "CustomerOrdersRoute", + routeTemplate: "api/customers/{custId}/orders", + defaults: new { controller="Customers", action="GetOrdersForCustomer" }) + ); + ``` + + This route directs requests that match the URI to the *GetOrdersForCustomer* method in the *Customers* controller. This method must take a single parameter named *custI*: + + ```C# + public class CustomersController : ApiController + { + ... + public IEnumerable GetOrdersForCustomer(int custId) + { + // Find orders for the specified customer + var orders = ... + return orders; + } + ... + } + ``` + + > [!TIP] + > Utilize the default routing wherever possible and avoid defining many complicated custom routes as this can result in brittleness (it is very easy to add methods to a controller that result in ambiguous routes) and reduced performance (the bigger the routing table, the more work the Web API framework has to do to work out which route matches a given URI). Keep the API and routes simple. For more information, see the section Organizing the Web API Around Resources in the API Design Guidance. If you must define custom routes, a preferable approach is to use attribute-based routing described later in this section. + > + > + + For more information about convention-based routing, see the page [Routing in ASP.NET Web API](http://www.asp.net/web-api/overview/web-api-routing-and-actions/routing-in-aspnet-web-api) on the Microsoft website. +* **Avoid ambiguity in routing**. + + Convention-based routing can result in ambiguous pathways if multiple methods in a controller match the same route. In these situations, the Web API framework responds with an HTTP 500 (Internal Server Error) response message containing the text "Multiple actions were found that match the request". +* **Prefer attribute-based routing**. + + Attribute-based routing provides an alternative means for connecting routes to methods in a controller. Rather than relying on the pattern-matching features of convention-based routing, you can explicitly annotate methods in a controller with the details of the route to which they should be associated. This approach help to remove possible ambiguities. Furthermore, as explicit routes are defined at design time this approach is more efficient than convention-based routing at runtime. The following code shows how to apply the *Route* attribute to methods in the Customers controller. These methods also use the HttpGet attribute to indicate that they should respond to *HTTP GET* requests. This attribute enables you to name your methods using any convenient naming scheme rather than that expected by convention-based routing. You can also annotate methods with the *HttpPost*, *HttpPut*, and *HttpDelete* attributes to define methods that respond to other types of HTTP requests. + + ```C# + public class CustomersController : ApiController + { + ... + [Route("api/customers/{id}")] + [HttpGet] + public Customer FindCustomerByID(int id) + { + // Find the matching customer + var customer = ... + return customer; + } + ... + [Route("api/customers/{id}/orders")] + [HttpGet] + public IEnumerable FindOrdersForCustomer(int id) + { + // Find orders for the specified customer + var orders = ... + return orders; + } + ... + } + ``` + + Attribute-based routing also has the useful side-effect of acting as documentation for developers needing to maintain the code in the future; it is immediately clear which method belongs to which route, and the *HttpGet* attribute clarifies the type of HTTP request to which the method responds. + + Attribute-based routing enables you to define constraints which restrict how the parameters are matched. Constraints can specify the type of the parameter, and in some cases they can also indicate the acceptable range of parameter values. In the following example, the id parameter to the *FindCustomerByID* method must be a non-negative integer. If an application submits an HTTP GET request with a negative customer number, the Web API framework will respond with an HTTP 405 (Method Not Allowed) message: + + ```C# + public class CustomersController : ApiController + { + ... + [Route("api/customers/{id:int:min(0)}")] + [HttpGet] + public Customer FindCustomerByID(int id) + { + // Find the matching customer + var customer = ... + return customer; + } + ... + } + ``` + + For more information on attribute-based routing, see the page [Attribute Routing in Web API 2](http://www.asp.net/web-api/overview/web-api-routing-and-actions/attribute-routing-in-web-api-2) on the Microsoft website. +* **Support Unicode characters in routes**. + + The keys used to identify resources in GET requests could be strings. Therefore, in a global application, you may need to support URIs that contain non-English characters. +* **Distinguish methods that should not be routed**. + + If you are using convention-based routing, indicate methods that do not correspond to HTTP actions by decorating them with the *NonAction* attribute. This typically applies to helper methods defined for use by other methods within a controller, and this attribute will prevent these methods from being matched and invoked by an errant HTTP request. +* **Consider the benefits and tradeoffs of placing the API in a subdomain**. + + By default, the ASP.NET web API organizes APIs into the */api* directory in a domain, such as `http://www.adventure-works.com/api/orders`. This directory resides in the same domain as any other services exposed by the same host. It may be beneficial to split the web API out into its own subdomain running on a separate host, with URIs such as `http://api.adventure-works.com/orders`. This separation enables you to partition and scale the web API more effectively without affecting any other web applications or services running in the *www.adventure-works.com* domain. + + However, placing a web API in a different subdomain can also lead to security concerns. Any web applications or services hosted at *www.adventure-works.com* that invoke a web API running elsewhere may violate the same-origin policy of many web browsers. In this situation, it will be necessary to enable cross-origin resource sharing (CORS) between the hosts. For more information, see the API Security Guidance document. + +## Considerations for processing requests +Once a request from a client application has been successfully routed to a method in a web API, the request must be processed in as efficient manner as possible. Consider the following points when you implement the code to handle requests: + +* **GET, PUT, DELETE, HEAD, and PATCH actions should be idempotent**. + + The code that implements these requests should not impose any side-effects. The same request repeated over the same resource should result in the same state. For example, sending multiple DELETE requests to the same URI should have the same effect, although the HTTP status code in the response messages may be different (the first DELETE request might return status code 204 (No Content) while a subsequent DELETE request might return status code 404 (Not Found)). + +> [!NOTE] +> The article [Idempotency Patterns](http://blog.jonathanoliver.com/idempotency-patterns/) on Jonathan Oliver’s blog provides an overview of idempotency and how it relates to data management operations. +> +> + +* **POST actions that create new resources should do so without unrelated side-effects**. + + If a POST request is intended to create a new resource, the effects of the request should be limited to the new resource (and possibly any directly related resources if there is some sort of linkage involved) For example, in an ecommerce system, a POST request that creates a new order for a customer might also amend inventory levels and generate billing information, but it should not modify information not directly related to the order or have any other side-effects on the overall state of the system. +* **Avoid implementing chatty POST, PUT, and DELETE operations**. + + Support POST, PUT and DELETE requests over resource collections. A POST request can contain the details for multiple new resources and add them all to the same collection, a PUT request can replace the entire set of resources in a collection, and a DELETE request can remove an entire collection. + + Note that the OData support included in ASP.NET Web API 2 provides the ability to batch requests. A client application can package up several web API requests and send them to the server in a single HTTP request, and receive a single HTTP response that contains the replies to each request. For more information, see the page [Introducing Batch Support in Web API and Web API OData](http://blogs.msdn.com/b/webdev/archive/2013/11/01/introducing-batch-support-in-web-api-and-web-api-odata.aspx) on the Microsoft website. +* **Abide by the HTTP protocol when sending a response back to a client application**. + + A web API must return messages that contain the correct HTTP status code to enable the client to determine how to handle the result, the appropriate HTTP headers so that the client understands the nature of the result, and a suitably formatted body to enable the client to parse the result. If you are using the ASP.NET Web API template, the default strategy for implementing methods that respond to HTTP POST requests is simply to return a copy of the newly created resource, as illustrated by the following example: + + ```C# + public class CustomersController : ApiController + { + ... + [Route("api/customers")] + [HttpPost] + public Customer CreateNewCustomer(Customer customerDetails) + { + // Add the new customer to the repository + // This method returns a customer with a unique ID allocated + // by the repository + var newCust = repository.Add(customerDetails); + // Return the newly added customer + return newCust; + } + ... + } + ``` + + If the POST operation is successful, the Web API framework creates an HTTP response with status code 200 (OK) and the details of the customer as the message body. However, in this case, according to the HTTP protocol, a POST operation should return status code 201 (Created) and the response message should include the URI of the newly created resource in the Location header of the response message. + + To provide these features, return your own HTTP response message by using the `IHttpActionResult` interface. This approach gives you fine control over the HTTP status code, the headers in the response message, and even the format of the data in the response message body, as shown in the following code example. This version of the `CreateNewCustomer` method conforms more closely to the expectations of client following the HTTP protocol. The `Created` method of the `ApiController` class constructs the response message from the specified data, and adds the Location header to the results: + + ```C# + public class CustomersController : ApiController + { + ... + [Route("api/customers")] + [HttpPost] + public IHttpActionResult CreateNewCustomer(Customer customerDetails) + { + // Add the new customer to the repository + var newCust = repository.Add(customerDetails); + + // Create a value for the Location header to be returned in the response + // The URI should be the location of the customer including its ID, + // such as http://adventure-works.com/api/customers/99 + var location = new Uri(...); + + // Return the HTTP 201 response, + // including the location and the newly added customer + return Created(location, newCust); + } + ... + } + ``` +* **Support content negotiation**. + + The body of a response message may contain data in a variety of formats. For example, an HTTP GET request could return data in JSON, or XML format. When the client submits a request, it can include an Accept header that specifies the data formats that it can handle. These formats are specified as media types. For example, a client that issues a GET request that retrieves an image can specify an Accept header that lists the media types that the client can handle, such as "image/jpeg, image/gif, image/png". When the web API returns the result, it should format the data by using one of these media types and specify the format in the Content-Type header of the response. + + If the client does not specify an Accept header, then use a sensible default format for the response body. As an example, the ASP.NET Web API framework defaults to JSON for text-based data. + + > [!NOTE] + > The ASP.NET Web API framework performs some automatic detection of Accept headers and handles them itself based on the type of the data in the body of the response message. For example, if the body of a response message contains a CLR (common language runtime) object, the ASP.NET Web API automatically formats the response as JSON with the Content-Type header of the response set to "application/json" unless the client indicates that it requires the results as XML, in which case the ASP.NET Web API framework formats the response as XML and sets the Content-Type header of the response to "text/xml". However, it may be necessary to handle Accept headers that specify different media types explicitly in the implementation code for an operation. + > + > +* **Provide links to support HATEOAS-style navigation and discovery of resources**. + + The API Design Guidance describes how following the HATEOAS approach enables a client to navigate and discover resources from an initial starting point. This is achieved by using links containing URIs; when a client issues an HTTP GET request to obtain a resource, the response should contain URIs that enable a client application to quickly locate any directly related resources. For example, in a web API that supports an e-commerce solution, a customer may have placed many orders. When a client application retrieves the details for a customer, the response should include links that enable the client application to send HTTP GET requests that can retrieve these orders. Additionally, HATEOAS-style links should describe the other operations (POST, PUT, DELETE, and so on) that each linked resource supports together with the corresponding URI to perform each request. This approach is described in more detail in the API Design Guidance document. + + Currently there are no standards that govern the implementation of HATEOAS, but the following example illustrates one possible approach. In this example, an HTTP GET request that finds the details for a customer returns a response that include HATEOAS links that reference the orders for that customer: + + ```HTTP + GET http://adventure-works.com/customers/2 HTTP/1.1 + Accept: text/json + ... + ``` + + ```HTTP + HTTP/1.1 200 OK + ... + Content-Type: application/json; charset=utf-8 + ... + Content-Length: ... + {"CustomerID":2,"CustomerName":"Bert","Links":[ + {"rel":"self", + "href":"http://adventure-works.com/customers/2", + "action":"GET", + "types":["text/xml","application/json"]}, + {"rel":"self", + "href":"http://adventure-works.com/customers/2", + "action":"PUT", + "types":["application/x-www-form-urlencoded"]}, + {"rel":"self", + "href":"http://adventure-works.com/customers/2", + "action":"DELETE", + "types":[]}, + {"rel":"orders", + "href":"http://adventure-works.com/customers/2/orders", + "action":"GET", + "types":["text/xml","application/json"]}, + {"rel":"orders", + "href":"http://adventure-works.com/customers/2/orders", + "action":"POST", + "types":["application/x-www-form-urlencoded"]} + ]} + ``` + + In this example, the customer data is represented by the `Customer` class shown in the following code snippet. The HATEOAS links are held in the `Links` collection property: + + ```C# + public class Customer + { + public int CustomerID { get; set; } + public string CustomerName { get; set; } + public List Links { get; set; } + ... + } + + public class Link + { + public string Rel { get; set; } + public string Href { get; set; } + public string Action { get; set; } + public string [] Types { get; set; } + } + ``` + + The HTTP GET operation retrieves the customer data from storage and constructs a `Customer` object, and then populates the `Links` collection. The result is formatted as a JSON response message. Each link comprises the following fields: + + * The relationship between the object being returned and the object described by the link. In this case "self" indicates that the link is a reference back to the object itself (similar to a `this` pointer in many object-oriented languages), and "orders" is the name of a collection containing the related order information. + * The hyperlink (`Href`) for the object being described by the link in the form of a URI. + * The type of HTTP request (`Action`) that can be sent to this URI. + * The format of any data (`Types`) that should be provided in the HTTP request or that can be returned in the response, depending on the type of the request. + + The HATEOAS links shown in the example HTTP response indicate that a client application can perform the following operations: + * An HTTP GET request to the URI *http://adventure-works.com/customers/2* to fetch the details of the customer (again). The data can be returned as XML or JSON. + * An HTTP PUT request to the URI *http://adventure-works.com/customers/2* to modify the details of the customer. The new data must be provided in the request message in x-www-form-urlencoded format. + * An HTTP DELETE request to the URI *http://adventure-works.com/customers/2* to delete the customer. The request does not expect any additional information or return data in the response message body. + * An HTTP GET request to the URI *http://adventure-works.com/customers/2/orders* to find all the orders for the customer. The data can be returned as XML or JSON. + * An HTTP PUT request to the URI *http://adventure-works.com/customers/2/orders* to create a new order for this customer. The data must be provided in the request message in x-www-form-urlencoded format. + +## Considerations for handling exceptions +By default, in the ASP.NET Web API framework, if an operation throws an uncaught exception the framework returns a response message with HTTP status code 500 (Internal Server Error). In many cases, this simplistic approach is not useful in isolation, and makes determining the cause of the exception difficult. Therefore you should adopt a more comprehensive approach to handling exceptions, considering the following points: + +* **Capture exceptions and return a meaningful response to clients**. + + The code that implements an HTTP operation should provide comprehensive exception handling rather than letting uncaught exceptions propagate to the Web API framework. If an exception makes it impossible to complete the operation successfully, the exception can be passed back in the response message, but it should include a meaningful description of the error that caused the exception. The exception should also include the appropriate HTTP status code rather than simply returning status code 500 for every situation. For example, if a user request causes a database update that violates a constraint (such as attempting to delete a customer that has outstanding orders), you should return status code 409 (Conflict) and a message body indicating the reason for the conflict. If some other condition renders the request unachievable, you can return status code 400 (Bad Request). You can find a full list of HTTP status codes on the [Status Code Definitions](http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html) page on the W3C website. + + The following code shows an example that traps different conditions and returns an appropriate response. + + ```C# + [HttpDelete] + [Route("customers/{id:int}")] + public IHttpActionResult DeleteCustomer(int id) + { + try + { + // Find the customer to be deleted in the repository + var customerToDelete = repository.GetCustomer(id); + + // If there is no such customer, return an error response + // with status code 404 (Not Found) + if (customerToDelete == null) + { + return NotFound(); + } + + // Remove the customer from the repository + // The DeleteCustomer method returns true if the customer + // was successfully deleted + if (repository.DeleteCustomer(id)) + { + // Return a response message with status code 204 (No Content) + // To indicate that the operation was successful + return StatusCode(HttpStatusCode.NoContent); + } + else + { + // Otherwise return a 400 (Bad Request) error response + return BadRequest(Strings.CustomerNotDeleted); + } + } + catch + { + // If an uncaught exception occurs, return an error response + // with status code 500 (Internal Server Error) + return InternalServerError(); + } + } + ``` + + > [!TIP] + > Do not include information that could be useful to an attacker attempting to penetrate your web API.For further information, visit the [Exception Handling in ASP.NET Web API](http://www.asp.net/web-api/overview/error-handling/exception-handling) page on the Microsoft website. + > + > [!NOTE] + > Many web servers trap error conditions themselves before they reach the web API. For example, if you configure authentication for a web site and the user fails to provide the correct authentication information, the web server should respond with status code 401 (Unauthorized). Once a client has been authenticated, your code can perform its own checks to verify that the client should be able access the requested resource. If this authorization fails, you should return status code 403 (Forbidden). + > + > +* **Handle exceptions in a consistent manner and log information about errors**. + + To handle exceptions in a consistent manner, consider implementing a global error handling strategy across the entire web API. You can achieve part of this by creating an exception filter that runs whenever a controller throws any unhandled exception that is not an `HttpResponseException` exception. This approach is described on the [Exception Handling in ASP.NET Web API](http://www.asp.net/web-api/overview/error-handling/exception-handling) page on the Microsoft website. + + However, there are several situations where an exception filter will not catch an exception, including: + + * Exceptions thrown from controller constructors. + * Exceptions thrown from message handlers. + * Exceptions thrown during routing. + * Exceptions thrown while serializing the content for a response message. + + To handle these cases, you may need to implement a more customized approach. You should also incorporate error logging which captures the full details of each exception; this error log can contain detailed information as long as it is not made accessible over the web to clients. The article [Web API Global Error Handling](http://www.asp.net/web-api/overview/error-handling/web-api-global-error-handling) on the Microsoft website shows one way of performing this task. +* **Distinguish between client-side errors and server-side errors**. + + The HTTP protocol distinguishes between errors that occur due to the client application (the HTTP 4xx status codes), and errors that are caused by a mishap on the server (the HTTP 5xx status codes). Make sure that you respect this convention in any error response messages. + + + +## Considerations for optimizing client-side data access +In a distributed environment such as that involving a web server and client applications, one of the primary sources of concern is the network. This can act as a considerable bottleneck, especially if a client application is frequently sending requests or receiving data. Therefore you should aim to minimize the amount of traffic that flows across the network. Consider the following points when you implement the code to retrieve and maintain data: + +* **Support client-side caching**. + + The HTTP 1.1 protocol supports caching in clients and intermediate servers through which a request is routed by the use of the Cache-Control header. When a client application sends an HTTP GET request to the web API, the response can include a Cache-Control header that indicates whether the data in the body of the response can be safely cached by the client or an intermediate server through which the request has been routed, and for how long before it should expire and be considered out-of-date. The following example shows an HTTP GET request and the corresponding response that includes a Cache-Control header: + + ```HTTP + GET http://adventure-works.com/orders/2 HTTP/1.1 + ... + ``` + + ```HTTP + HTTP/1.1 200 OK + ... + Cache-Control: max-age=600, private + Content-Type: text/json; charset=utf-8 + Content-Length: ... + {"orderID":2,"productID":4,"quantity":2,"orderValue":10.00} + ``` + + In this example, the Cache-Control header specifies that the data returned should be expired after 600 seconds, and is only suitable for a single client and must not be stored in a shared cache used by other clients (it is *private*). The Cache-Control header could specify *public* rather than *private* in which case the data can be stored in a shared cache, or it could specify *no-store* in which case the data must **not** be cached by the client. The following code example shows how to construct a Cache-Control header in a response message: + + ```C# + public class OrdersController : ApiController + { + ... + [Route("api/orders/{id:int:min(0)}")] + [HttpGet] + public IHttpActionResult FindOrderByID(int id) + { + // Find the matching order + Order order = ...; + ... + // Create a Cache-Control header for the response + var cacheControlHeader = new CacheControlHeaderValue(); + cacheControlHeader.Private = true; + cacheControlHeader.MaxAge = new TimeSpan(0, 10, 0); + ... + + // Return a response message containing the order and the cache control header + OkResultWithCaching response = new OkResultWithCaching(order, this) + { + CacheControlHeader = cacheControlHeader + }; + return response; + } + ... + } + ``` + + This code makes use of a custom `IHttpActionResult` class named `OkResultWithCaching`. This class enables the controller to set the cache header contents: + + ```C# + public class OkResultWithCaching : OkNegotiatedContentResult + { + public OkResultWithCaching(T content, ApiController controller) + : base(content, controller) { } + + public OkResultWithCaching(T content, IContentNegotiator contentNegotiator, HttpRequestMessage request, IEnumerable formatters) + : base(content, contentNegotiator, request, formatters) { } + + public CacheControlHeaderValue CacheControlHeader { get; set; } + public EntityTagHeaderValue ETag { get; set; } + + public override async Task ExecuteAsync(CancellationToken cancellationToken) + { + HttpResponseMessage response = await base.ExecuteAsync(cancellationToken); + + response.Headers.CacheControl = this.CacheControlHeader; + response.Headers.ETag = ETag; + + return response; + } + } + ``` + + > [!NOTE] + > The HTTP protocol also defines the *no-cache* directive for the Cache-Control header. Rather confusingly, this directive does not mean "do not cache" but rather "revalidate the cached information with the server before returning it"; the data can still be cached, but it is checked each time it is used to ensure that it is still current. + > + > + + Cache management is the responsibility of the client application or intermediate server, but if properly implemented it can save bandwidth and improve performance by removing the need to fetch data that has already been recently retrieved. + + The *max-age* value in the Cache-Control header is only a guide and not a guarantee that the corresponding data won't change during the specified time. The web API should set the max-age to a suitable value depending on the expected volatility of the data. When this period expires, the client should discard the object from the cache. + + > [!NOTE] + > Most modern web browsers support client-side caching by adding the appropriate cache-control headers to requests and examining the headers of the results, as described. However, some older browsers will not cache the values returned from a URL that includes a query string. This is not usually an issue for custom client applications which implement their own cache management strategy based on the protocol discussed here. + > + > Some older proxies exhibit the same behavior and might not cache requests based on URLs with query strings. This could be an issue for custom client applications that connect to a web server through such a proxy. + > + > +* **Provide ETags to Optimize Query Processing**. + + When a client application retrieves an object, the response message can also include an *ETag* (Entity Tag). An ETag is an opaque string that indicates the version of a resource; each time a resource changes the Etag is also modified. This ETag should be cached as part of the data by the client application. The following code example shows how to add an ETag as part of the response to an HTTP GET request. This code uses the `GetHashCode` method of an object to generate a numeric value that identifies the object (you can override this method if necessary and generate your own hash using an algorithm such as MD5) : + + ```C# + public class OrdersController : ApiController + { + ... + public IHttpActionResult FindOrderByID(int id) + { + // Find the matching order + Order order = ...; + ... + + var hashedOrder = order.GetHashCode(); + string hashedOrderEtag = String.Format("\"{0}\"", hashedOrder); + var eTag = new EntityTagHeaderValue(hashedOrderEtag); + + // Return a response message containing the order and the cache control header + OkResultWithCaching response = new OkResultWithCaching(order, this) + { + ..., + ETag = eTag + }; + return response; + } + ... + } + ``` + + The response message posted by the web API looks like this: + + ```HTTP + HTTP/1.1 200 OK + ... + Cache-Control: max-age=600, private + Content-Type: text/json; charset=utf-8 + ETag: "2147483648" + Content-Length: ... + {"orderID":2,"productID":4,"quantity":2,"orderValue":10.00} + ``` + + > [!TIP] + > For security reasons, do not allow sensitive data or data returned over an authenticated (HTTPS) connection to be cached. + > + > + + A client application can issue a subsequent GET request to retrieve the same resource at any time, and if the resource has changed (it has a different ETag) the cached version should be discarded and the new version added to the cache. If a resource is large and requires a significant amount of bandwidth to transmit back to the client, repeated requests to fetch the same data can become inefficient. To combat this, the HTTP protocol defines the following process for optimizing GET requests that you should support in a web API: + + * The client constructs a GET request containing the ETag for the currently cached version of the resource referenced in an If-None-Match HTTP header: + + ```HTTP + GET http://adventure-works.com/orders/2 HTTP/1.1 + If-None-Match: "2147483648" + ... + ``` + * The GET operation in the web API obtains the current ETag for the requested data (order 2 in the above example), and compares it to the value in the If-None-Match header. + * If the current ETag for the requested data matches the ETag provided by the request, the resource has not changed and the web API should return an HTTP response with an empty message body and a status code of 304 (Not Modified). + * If the current ETag for the requested data does not match the ETag provided by the request, then the data has changed and the web API should return an HTTP response with the new data in the message body and a status code of 200 (OK). + * If the requested data no longer exists then the web API should return an HTTP response with the status code of 404 (Not Found). + * The client uses the status code to maintain the cache. If the data has not changed (status code 304) then the object can remain cached and the client application should continue to use this version of the object. If the data has changed (status code 200) then the cached object should be discarded and the new one inserted. If the data is no longer available (status code 404) then the object should be removed from the cache. + + > [!NOTE] + > If the response header contains the Cache-Control header no-store then the object should always be removed from the cache regardless of the HTTP status code. + > + > + + The code below shows the `FindOrderByID` method extended to support the If-None-Match header. Notice that if the If-None-Match header is omitted, the specified order is always retrieved: + + ```C# + public class OrdersController : ApiController + { + ... + [Route("api/orders/{id:int:min(0)}")] + [HttpGet] + public IHttpActionResult FindOrderById(int id) + { + try + { + // Find the matching order + Order order = ...; + + // If there is no such order then return NotFound + if (order == null) + { + return NotFound(); + } + + // Generate the ETag for the order + var hashedOrder = order.GetHashCode(); + string hashedOrderEtag = String.Format("\"{0}\"", hashedOrder); + + // Create the Cache-Control and ETag headers for the response + IHttpActionResult response = null; + var cacheControlHeader = new CacheControlHeaderValue(); + cacheControlHeader.Public = true; + cacheControlHeader.MaxAge = new TimeSpan(0, 10, 0); + var eTag = new EntityTagHeaderValue(hashedOrderEtag); + + // Retrieve the If-None-Match header from the request (if it exists) + var nonMatchEtags = Request.Headers.IfNoneMatch; + + // If there is an ETag in the If-None-Match header and + // this ETag matches that of the order just retrieved, + // then create a Not Modified response message + if (nonMatchEtags.Count > 0 && + String.Compare(nonMatchEtags.First().Tag, hashedOrderEtag) == 0) + { + response = new EmptyResultWithCaching() + { + StatusCode = HttpStatusCode.NotModified, + CacheControlHeader = cacheControlHeader, + ETag = eTag + }; + } + // Otherwise create a response message that contains the order details + else + { + response = new OkResultWithCaching(order, this) + { + CacheControlHeader = cacheControlHeader, + ETag = eTag + }; + } + + return response; + } + catch + { + return InternalServerError(); + } + } + ... + } + ``` + + This example incorporates an additional custom `IHttpActionResult` class named `EmptyResultWithCaching`. This class simply acts as a wrapper around an `HttpResponseMessage` object that does not contain a response body: + + ```C# + public class EmptyResultWithCaching : IHttpActionResult + { + public CacheControlHeaderValue CacheControlHeader { get; set; } + public EntityTagHeaderValue ETag { get; set; } + public HttpStatusCode StatusCode { get; set; } + public Uri Location { get; set; } + + public async Task ExecuteAsync(CancellationToken cancellationToken) + { + HttpResponseMessage response = new HttpResponseMessage(StatusCode); + response.Headers.CacheControl = this.CacheControlHeader; + response.Headers.ETag = this.ETag; + response.Headers.Location = this.Location; + return response; + } + } + ``` + + > [!TIP] + > In this example, the ETag for the data is generated by hashing the data retrieved from the underlying data source. If the ETag can be computed in some other way, then the process can be optimized further and the data only needs to be fetched from the data source if it has changed. This approach is especially useful if the data is large or accessing the data source can result in significant latency (for example, if the data source is a remote database). + > + > +* **Use ETags to Support Optimistic Concurrency**. + + To enable updates over previously cached data, the HTTP protocol supports an optimistic concurrency strategy. If, after fetching and caching a resource, the client application subsequently sends a PUT or DELETE request to change or remove the resource, it should include in If-Match header that references the ETag. The web API can then use this information to determine whether the resource has already been changed by another user since it was retrieved and send an appropriate response back to the client application as follows: + + * The client constructs a PUT request containing the new details for the resource and the ETag for the currently cached version of the resource referenced in an If-Match HTTP header. The following example shows a PUT request that updates an order: + + ```HTTP + PUT http://adventure-works.com/orders/1 HTTP/1.1 + If-Match: "2282343857" + Content-Type: application/x-www-form-urlencoded + ... + Date: Fri, 12 Sep 2014 09:18:37 GMT + Content-Length: ... + productID=3&quantity=5&orderValue=250 + ``` + * The PUT operation in the web API obtains the current ETag for the requested data (order 1 in the above example), and compares it to the value in the If-Match header. + * If the current ETag for the requested data matches the ETag provided by the request, the resource has not changed and the web API should perform the update, returning a message with HTTP status code 204 (No Content) if it is successful. The response can include Cache-Control and ETag headers for the updated version of the resource. The response should always include the Location header that references the URI of the newly updated resource. + * If the current ETag for the requested data does not match the ETag provided by the request, then the data has been changed by another user since it was fetched and the web API should return an HTTP response with an empty message body and a status code of 412 (Precondition Failed). + * If the resource to be updated no longer exists then the web API should return an HTTP response with the status code of 404 (Not Found). + * The client uses the status code and response headers to maintain the cache. If the data has been updated (status code 204) then the object can remain cached (as long as the Cache-Control header does not specify no-store) but the ETag should be updated. If the data was changed by another user changed (status code 412) or not found (status code 404) then the cached object should be discarded. + + The next code example shows an implementation of the PUT operation for the Orders controller: + + ```C# + public class OrdersController : ApiController + { + ... + [HttpPut] + [Route("api/orders/{id:int}")] + public IHttpActionResult UpdateExistingOrder(int id, DTOOrder order) + { + try + { + var baseUri = Constants.GetUriFromConfig(); + var orderToUpdate = this.ordersRepository.GetOrder(id); + if (orderToUpdate == null) + { + return NotFound(); + } + + var hashedOrder = orderToUpdate.GetHashCode(); + string hashedOrderEtag = String.Format("\"{0}\"", hashedOrder); + + // Retrieve the If-Match header from the request (if it exists) + var matchEtags = Request.Headers.IfMatch; + + // If there is an Etag in the If-Match header and + // this etag matches that of the order just retrieved, + // or if there is no etag, then update the Order + if (((matchEtags.Count > 0 && + String.Compare(matchEtags.First().Tag, hashedOrderEtag) == 0)) || + matchEtags.Count == 0) + { + // Modify the order + orderToUpdate.OrderValue = order.OrderValue; + orderToUpdate.ProductID = order.ProductID; + orderToUpdate.Quantity = order.Quantity; + + // Save the order back to the data store + // ... + + // Create the No Content response with Cache-Control, ETag, and Location headers + var cacheControlHeader = new CacheControlHeaderValue(); + cacheControlHeader.Private = true; + cacheControlHeader.MaxAge = new TimeSpan(0, 10, 0); + + hashedOrder = order.GetHashCode(); + hashedOrderEtag = String.Format("\"{0}\"", hashedOrder); + var eTag = new EntityTagHeaderValue(hashedOrderEtag); + + var location = new Uri(string.Format("{0}/{1}/{2}", baseUri, Constants.ORDERS, id)); + var response = new EmptyResultWithCaching() + { + StatusCode = HttpStatusCode.NoContent, + CacheControlHeader = cacheControlHeader, + ETag = eTag, + Location = location + }; + + return response; + } + + // Otherwise return a Precondition Failed response + return StatusCode(HttpStatusCode.PreconditionFailed); + } + catch + { + return InternalServerError(); + } + } + ... + } + ``` + + > [!TIP] + > Use of the If-Match header is entirely optional, and if it is omitted the web API will always attempt to update the specified order, possibly blindly overwriting an update made by another user. To avoid problems due to lost updates, always provide an If-Match header. + > + > + + + +## Considerations for handling large requests and responses +There may be occasions when a client application needs to issue requests that send or receive data that may be several megabytes (or bigger) in size. Waiting while this amount of data is transmitted could cause the client application to become unresponsive. Consider the following points when you need to handle requests that include significant amounts of data: + +* **Optimize requests and responses that involve large objects**. + + Some resources may be large objects or include large fields, such as graphics images or other types of binary data. A web API should support streaming to enable optimized uploading and downloading of these resources. + + The HTTP protocol provides the chunked transfer encoding mechanism to stream large data objects back to a client. When the client sends an HTTP GET request for a large object, the web API can send the reply back in piecemeal *chunks* over an HTTP connection. The length of the data in the reply may not be known initially (it might be generated), so the server hosting the web API should send a response message with each chunk that specifies the Transfer-Encoding: Chunked header rather than a Content-Length header. The client application can receive each chunk in turn to build up the complete response. The data transfer completes when the server sends back a final chunk with zero size. You can implement chunking in the ASP.NET Web API by using the `PushStreamContent` class. + + The following example shows an operation that responds to HTTP GET requests for product images: + + ```C# + public class ProductImagesController : ApiController + { + ... + [HttpGet] + [Route("productimages/{id:int}")] + public IHttpActionResult Get(int id) + { + try + { + var container = ConnectToBlobContainer(Constants.PRODUCTIMAGESCONTAINERNAME); + + if (!BlobExists(container, string.Format("image{0}.jpg", id))) + { + return NotFound(); + } + else + { + return new FileDownloadResult() + { + Container = container, + ImageId = id + }; + } + } + catch + { + return InternalServerError(); + } + } + ... + } + ``` + + In this example, `ConnectBlobToContainer` is a helper method that connects to a specified container (name not shown) in Azure Blob storage. `BlobExists` is another helper method that returns a Boolean value that indicates whether a blob with the specified name exists in the blob storage container. + + Each product has its own image held in blob storage. The `FileDownloadResult` class is a custom `IHttpActionResult` class that uses a `PushStreamContent` object to read the image data from appropriate blob and transmit it asynchronously as the content of the response message: + + ```C# + public class FileDownloadResult : IHttpActionResult + { + public CloudBlobContainer Container { get; set; } + public int ImageId { get; set; } + + public async Task ExecuteAsync(CancellationToken cancellationToken) + { + var response = new HttpResponseMessage(); + response.Content = new PushStreamContent(async (outputStream, _, __) => + { + try + { + CloudBlockBlob blockBlob = Container.GetBlockBlobReference(String.Format("image{0}.jpg", ImageId)); + await blockBlob.DownloadToStreamAsync(outputStream); + } + finally + { + outputStream.Close(); + } + }); + + response.StatusCode = HttpStatusCode.OK; + response.Content.Headers.ContentType = new MediaTypeHeaderValue("image/jpeg"); + return response; + } + } + ``` + + You can also apply streaming to upload operations if a client needs to POST a new resource that includes a large object. The next example shows the Post method for the `ProductImages` controller. This method enables the client to upload a new product image: + + ```C# + public class ProductImagesController : ApiController + { + ... + [HttpPost] + [Route("productimages")] + public async Task Post() + { + try + { + if (!Request.Content.Headers.ContentType.MediaType.Equals("image/jpeg")) + { + return StatusCode(HttpStatusCode.UnsupportedMediaType); + } + else + { + var id = new Random().Next(); // Use a random int as the key for the new resource. Should probably check that this key has not already been used + var container = ConnectToBlobContainer(Constants.PRODUCTIMAGESCONTAINERNAME); + return new FileUploadResult() + { + Container = container, + ImageId = id, + Request = Request + }; + } + } + catch + { + return InternalServerError(); + } + } + ... + } + ``` + + This code uses another custom `IHttpActionResult` class called `FileUploadResult`. This class contains the logic for uploading the data asynchronously: + + ```C# + public class FileUploadResult : IHttpActionResult + { + public CloudBlobContainer Container { get; set; } + public int ImageId { get; set; } + public HttpRequestMessage Request { get; set; } + + public async Task ExecuteAsync(CancellationToken cancellationToken) + { + var response = new HttpResponseMessage(); + CloudBlockBlob blockBlob = Container.GetBlockBlobReference(String.Format("image{0}.jpg", ImageId)); + await blockBlob.UploadFromStreamAsync(await Request.Content.ReadAsStreamAsync()); + var baseUri = string.Format("{0}://{1}:{2}", Request.RequestUri.Scheme, Request.RequestUri.Host, Request.RequestUri.Port); + response.Headers.Location = new Uri(string.Format("{0}/productimages/{1}", baseUri, ImageId)); + response.StatusCode = HttpStatusCode.OK; + return response; + } + } + ``` + + > [!TIP] + > The volume of data that you can upload to a web service is not constrained by streaming, and a single request could conceivably result in a massive object that consumes considerable resources. If, during the streaming process, the web API determines that the amount of data in a request has exceeded some acceptable bounds, it can abort the operation and return a response message with status code 413 (Request Entity Too Large). + > + > + + You can minimize the size of large objects transmitted over the network by using HTTP compression. This approach helps to reduce the amount of network traffic and the associated network latency, but at the cost of requiring additional processing at the client and the server hosting the web API. For example, a client application that expects to receive compressed data can include an Accept-Encoding: gzip request header (other data compression algorithms can also be specified). If the server supports compression it should respond with the content held in gzip format in the message body and the Content-Encoding: gzip response header. + + > [!TIP] + > You can combine encoded compression with streaming; compress the data first before streaming it, and specify the gzip content encoding and chunked transfer encoding in the message headers. Also note that some web servers (such as Internet Information Server) can be configured to automatically compress HTTP responses regardless of whether the web API compresses the data or not. + > + > +* **Implement partial responses for clients that do not support asynchronous operations**. + + As an alternative to asynchronous streaming, a client application can explicitly request data for large objects in chunks, known as partial responses. The client application sends an HTTP HEAD request to obtain information about the object. If the web API supports partial responses if should respond to the HEAD request with a response message that contains an Accept-Ranges header and a Content-Length header that indicates the total size of the object, but the body of the message should be empty. The client application can use this information to construct a series of GET requests that specify a range of bytes to receive. The web API should return a response message with HTTP status 206 (Partial Content), a Content-Length header that specifies the actual amount of data included in the body of the response message, and a Content-Range header that indicates which part (such as bytes 4000 to 8000) of the object this data represents. + + HTTP HEAD requests and partial responses are described in more detail in the API Design Guidance document. +* **Avoid sending unnecessary Continue status messages in client applications**. + + A client application that is about to send a large amount of data to a server may determine first whether the server is actually willing to accept the request. Prior to sending the data, the client application can submit an HTTP request with an Expect: 100-Continue header, a Content-Length header that indicates the size of the data, but an empty message body. If the server is willing to handle the request, it should respond with a message that specifies the HTTP status 100 (Continue). The client application can then proceed and send the complete request including the data in the message body. + + If you are hosting a service by using IIS, the HTTP.sys driver automatically detects and handles Expect: 100-Continue headers before passing requests to your web application. This means that you are unlikely to see these headers in your application code, and you can assume that IIS has already filtered any messages that it deems to be unfit or too large. + + If you are building client applications by using the .NET Framework, then all POST and PUT messages will first send messages with Expect: 100-Continue headers by default. As with the server-side, the process is handled transparently by the .NET Framework. However, this process results in each POST and PUT request causing 2 round-trips to the server, even for small requests. If your application is not sending requests with large amounts of data, you can disable this feature by using the `ServicePointManager` class to create `ServicePoint` objects in the client application. A `ServicePoint` object handles the connections that the client makes to a server based on the scheme and host fragments of URIs that identify resources on the server. You can then set the `Expect100Continue` property of the `ServicePoint` object to false. All subsequent POST and PUT requests made by the client through a URI that matches the scheme and host fragments of the `ServicePoint` object will be sent without Expect: 100-Continue headers. The following code shows how to configure a `ServicePoint` object that configures all requests sent to URIs with a scheme of `http` and a host of `www.contoso.com`. + + ```C# + Uri uri = new Uri("http://www.contoso.com/"); + ServicePoint sp = ServicePointManager.FindServicePoint(uri); + sp.Expect100Continue = false; + ``` + + You can also set the static `Expect100Continue` property of the `ServicePointManager` class to specify the default value of this property for all subsequently created `ServicePoint` objects. For more information, see the [ServicePoint Class](https://msdn.microsoft.com/library/system.net.servicepoint.aspx) page on the Microsoft website. +* **Support pagination for requests that may return large numbers of objects**. + + If a collection contains a large number of resources, issuing a GET request to the corresponding URI could result in significant processing on the server hosting the web API affecting performance, and generate a significant amount of network traffic resulting in increased latency. + + To handle these cases, the web API should support query strings that enable the client application to refine requests or fetch data in more manageable, discrete blocks (or pages). The ASP.NET Web API framework parses query strings and splits them up into a series of parameter/value pairs which are passed to the appropriate method, following the routing rules described earlier. The method should be implemented to accept these parameters using the same names specified in the query string. Additionally, these parameters should be optional (in case the client omits the query string from a request) and have meaningful default values. The code below shows the `GetAllOrders` method in the `Orders` controller. This method retrieves the details of orders. If this method was unconstrained, it could conceivably return a large amount of data. The `limit` and `offset` parameters are intended to reduce the volume of data to a smaller subset, in this case only the first 10 orders by default: + + ```C# + public class OrdersController : ApiController + { + ... + [Route("api/orders")] + [HttpGet] + public IEnumerable GetAllOrders(int limit=10, int offset=0) + { + // Find the number of orders specified by the limit parameter + // starting with the order specified by the offset parameter + var orders = ... + return orders; + } + ... + } + ``` + + A client application can issue a request to retrieve 30 orders starting at offset 50 by using the URI *http://www.adventure-works.com/api/orders?limit=30&offset=50*. + + > [!TIP] + > Avoid enabling client applications to specify query strings that result in a URI that is more than 2000 characters long. Many web clients and servers cannot handle URIs that are this long. + > + > + + + +## Considerations for maintaining responsiveness, scalability, and availability +The same web API might be utilized by many client applications running anywhere in the world. It is important to ensure that the web API is implemented to maintain responsiveness under a heavy load, to be scalable to support a highly varying workload, and to guarantee availability for clients that perform business-critical operations. Consider the following points when determining how to meet these requirements: + +* **Provide Asynchronous Support for Long-Running Requests**. + + A request that might take a long time to process should be performed without blocking the client that submitted the request. The web API can perform some initial checking to validate the request, initiate a separate task to perform the work, and then return a response message with HTTP code 202 (Accepted). The task could run asynchronously as part of the web API processing, or it could be offloaded to an Azure WebJob (if the web API is hosted by an Azure Website) or a worker role (if the web API is implemented as an Azure cloud service). + + > [!NOTE] + > For more information about using WebJobs with Azure Website, visit the page [Use WebJobs to run background tasks in Microsoft Azure Websites](/azure/app-service-web/web-sites-create-web-jobs/) on the Microsoft website. + > + > + + The web API should also provide a mechanism to return the results of the processing to the client application. You can achieve this by providing a polling mechanism for client applications to periodically query whether the processing has finished and obtain the result, or enabling the web API to send a notification when the operation has completed. + + You can implement a simple polling mechanism by providing a *polling* URI that acts as a virtual resource using the following approach: + + 1. The client application sends the initial request to the web API. + 2. The web API stores information about the request in a table held in table storage or Microsoft Azure Cache, and generates a unique key for this entry, possibly in the form of a GUID. + 3. The web API initiates the processing as a separate task. The web API records the state of the task in the table as *Running*. + 4. The web API returns a response message with HTTP status code 202 (Accepted), and the GUID of the table entry in the body of the message. + 5. When the task has completed, the web API stores the results in the table, and sets the state of the task to *Complete*. Note that if the task fails, the web API could also store information about the failure and set the status to *Failed*. + 6. While the task is running, the client can continue performing its own processing. It can periodically send a request to the URI */polling/{guid}* where *{guid}* is the GUID returned in the 202 response message by the web API. + 7. The web API at the */polling/{guid}* URI queries the state of the corresponding task in the table and returns a response message with HTTP status code 200 (OK) containing this state (*Running*, *Complete*, or *Failed*). If the task has completed or failed, the response message can also include the results of the processing or any information available about the reason for the failure. + + If you prefer to implement notifications, the options available include: + 8. Using an Azure Notification Hub to push asynchronous responses to client applications. The page [Azure Notification Hubs Notify Users](/azure/notification-hubs/notification-hubs-aspnet-backend-windows-dotnet-wns-notification/) on the Microsoft website provides further details. + 9. Using the Comet model to retain a persistent network connection between the client and the server hosting the web API, and using this connection to push messages from the server back to the client. The MSDN magazine article [Building a Simple Comet Application in the Microsoft .NET Framework](https://msdn.microsoft.com/magazine/jj891053.aspx) describes an example solution. + 10. Using SignalR to push data in real-time from the web server to the client over a persistent network connection. SignalR is available for ASP.NET web applications as a NuGet package. You can find more information on the [ASP.NET SignalR](http://signalr.net/) website. + + > [!NOTE] + > Comet and SignalR both utilize persistent network connections between the web server and the client application. This can affect scalability as a large number of clients may require an equally large number of concurrent connections. + > + > +* **Ensure that each request is stateless**. + + Each request should be considered atomic. There should be no dependencies between one request made by a client application and any subsequent requests submitted by the same client. This approach assists in scalability; instances of the web service can be deployed on a number of servers. Client requests can be directed at any of these instances and the results should always be the same. It also improves availability for a similar reason; if a web server fails requests can be routed to another instance (by using Azure Traffic Manager) while the server is restarted with no ill effects on client applications. +* **Track clients and implement throttling to reduce the chances of DOS attacks**. + + If a specific client makes a large number of requests within a given period of time it might monopolize the service and affect the performance of other clients. To mitigate this issue, a web API can monitor calls from client applications either by tracking the IP address of all incoming requests or by logging each authenticated access. You can use this information to limit resource access. If a client exceeds a defined limit, the web API can return a response message with status 503 (Service Unavailable) and include a Retry-After header that specifies when the client can send the next request without it being declined. This strategy can help to reduce the chances of a Denial Of Service (DOS) attack from a set of clients stalling the system. +* **Manage persistent HTTP connections carefully**. + + The HTTP protocol supports persistent HTTP connections where they are available. The HTTP 1.0 specificiation added the Connection:Keep-Alive header that enables a client application to indicate to the server that it can use the same connection to send subsequent requests rather than opening new ones. The connection closes automatically if the client does not reuse the connection within a period defined by the host. This behavior is the default in HTTP 1.1 as used by Azure services, so there is no need to include Keep-Alive headers in messages. + + Keeping a connection open can help to improve responsiveness by reducing latency and network congestion, but it can be detrimental to scalability by keeping unnecessary connections open for longer than required, limiting the ability of other concurrent clients to connect. It can also affect battery life if the client application is running on a mobile device; if the application only makes occasional requests to the server, maintaining an open connection can cause the battery to drain more quickly. To ensure that a connection is not made persistent with HTTP 1.1, the client can include a Connection:Close header with messages to override the default behavior. Similarly, if a server is handling a very large number of clients it can include a Connection:Close header in response messages which should close the connection and save server resources. + + > [!NOTE] + > Persistent HTTP connections are a purely optional feature to reduce the network overhead associated with repeatedly establishing a communications channel. Neither the web API nor the client application should depend on a persistent HTTP connection being available. Do not use persistent HTTP connections to implement Comet-style notification systems; instead you should utilize sockets (or websockets if available) at the TCP layer. Finally, note Keep-Alive headers are of limited use if a client application communicates with a server via a proxy; only the connection with the client and the proxy will be persistent. + > + > + +## Considerations for publishing and managing a web API +To make a web API available for client applications, the web API must be deployed to a host environment. This environment is typically a web server, although it may be some other type of host process. You should consider the following points when publishing a web API: + +* All requests must be authenticated and authorized, and the appropriate level of access control must be enforced. +* A commercial web API might be subject to various quality guarantees concerning response times. It is important to ensure that host environment is scalable if the load can vary significantly over time. +* If may be necessary to meter requests for monetization purposes. +* It might be necessary to regulate the flow of traffic to the web API, and implement throttling for specific clients that have exhausted their quotas. +* Regulatory requirements might mandate logging and auditing of all requests and responses. +* To ensure availability, it may be necessary to monitor the health of the server hosting the web API and restart it if necessary. + +It is useful to be able to decouple these issues from the technical issues concerning the implementation of the web API. For this reason, consider creating a [façade](http://en.wikipedia.org/wiki/Facade_pattern), running as a separate process and that routes requests to the web API. The façade can provide the management operations and forward validated requests to the web API. Using a façade can also bring many functional advantages, including: + +* Acting as an integration point for multiple web APIs. +* Transforming messages and translating communications protocols for clients built by using varying technologies. +* Caching requests and responses to reduce load on the server hosting the web API. + +## Considerations for testing a web API +A web API should be tested as thoroughly as any other piece of software. You should consider creating unit tests to validate the functionality of each operation, as you would with any other type of application. For more information, see the page [Verifying Code by Using Unit Tests](https://msdn.microsoft.com/library/dd264975.aspx) on the Microsoft website. + +> [!NOTE] +> The sample web API available with this guidance includes a test project that shows how to perform unit testing over selected operations. +> +> + +The nature of a web API brings its own additional requirements to verify that it operates correctly. You should pay particular attention to the following aspects: + +* Test all routes to verify that they invoke the correct operations. Be especially aware of HTTP status code 405 (Method Not Allowed) being returned unexpectedly as this can indicate a mismatch between a route and the HTTP methods (GET, POST, PUT, DELETE) that can be dispatched to that route. + + Send HTTP requests to routes that do not support them, such as submitting a POST request to a specific resource (POST requests should only be sent to resource collections). In these cases, the only valid response *should* be status code 405 (Not Allowed). +* Verify that all routes are protected properly and are subject to the appropriate authentication and authorization checks. + + > [!NOTE] + > Some aspects of security such as user authentication are most likely to be the responsibility of the host environment rather than the web API, but it is still necessary to include security tests as part of the deployment process. + > + > +* Test the exception handling performed by each operation and verify that an appropriate and meaningful HTTP response is passed back to the client application. +* Verify that request and response messages are well-formed. For example, if an HTTP POST request contains the data for a new resource in x-www-form-urlencoded format, confirm that the corresponding operation correctly parses the data, creates the resources, and returns a response containing the details of the new resource, including the correct Location header. +* Verify all links and URIs in response messages. For example, an HTTP POST message should return the URI of the newly-created resource. All HATEOAS links should be valid. + + > [!IMPORTANT] + > If you publish the web API through an API Management Service, then these URIs should reflect the URL of the management service and not that of the web server hosting the web API. + > + > +* Ensure that each operation returns the correct status codes for different combinations of input. For example: + + * If a query is successful, it should return status code 200 (OK) + * If a resource is not found, the operation should return HTTP status code 404 (Not Found). + * If the client sends a request that successfully deletes a resource, the status code should be 204 (No Content). + * If the client sends a request that creates a new resource, the status code should be 201 (Created) + +Watch out for unexpected response status codes in the 5xx range. These messages are usually reported by the host server to indicate that it was unable to fulfill a valid request. + +* Test the different request header combinations that a client application can specify and ensure that the web API returns the expected information in response messages. +* Test query strings. If an operation can take optional parameters (such as pagination requests), test the different combinations and order of parameters. +* Verify that asynchronous operations complete successfully. If the web API supports streaming for requests that return large binary objects (such as video or audio), ensure that client requests are not blocked while the data is streamed. If the web API implements polling for long-running data modification operations, verify that that the operations report their status correctly as they proceed. + +You should also create and run performance tests to check that the web API operates satisfactorily under duress. You can build a web performance and load test project by using Visual Studio Ultimate. For more information, see the page [Run performance tests on an application before a release](https://msdn.microsoft.com/library/dn250793.aspx) on the Microsoft website. + +## Publishing and managing a web API by using the Azure API Management Service +Azure provides the [API Management Service](https://azure.microsoft.com/documentation/services/api-management/) which you can use to publish and manage a web API. Using this facility, you can generate a service that acts a façade for one or more web APIs. The service is itself a scalable web service that you can create and configure by using the Azure Management portal. You can use this service to publish and manage a web API as follows: + +1. Deploy the web API to a website, Azure cloud service, or Azure virtual machine. +2. Connect the API management service to the web API. Requests sent to the URL of the management API are mapped to URIs in the web API. The same API management service can route requests to more than one web API. This enables you to aggregate multiple web APIs into a single management service. Similarly, the same web API can be referenced from more than one API management service if you need to restrict or partition the functionality available to different applications. + + > [!NOTE] + > The URIs in HATEOAS links generated as part of the response for HTTP GET requests should reference the URL of the API management service and not the web server hosting the web API. + > + > +3. For each web API, specify the HTTP operations that the web API exposes together with any optional parameters that an operation can take as input. You can also configure whether the API management service should cache the response received from the web API to optimize repeated requests for the same data. Record the details of the HTTP responses that each operation can generate. This information is used to generate documentation for developers, so it is important that it is accurate and complete. + + You can either define operations manually using the wizards provided by the Azure Management portal, or you can import them from a file containing the definitions in WADL or Swagger format. +4. Configure the security settings for communications between the API management service and the web server hosting the web API. The API management service currently supports Basic authentication and mutual authentication using certificates, and OAuth 2.0 user authorization. +5. Create a product. A product is the unit of publication; you add the web APIs that you previously connected to the management service to the product. When the product is published, the web APIs become available to developers. + + > [!NOTE] + > Prior to publishing a product, you can also define user-groups that can access the product and add users to these groups. This gives you control over the developers and applications that can use the web API. If a web API is subject to approval, prior to being able to access it a developer must send a request to the product administrator. The administrator can grant or deny access to the developer. Existing developers can also be blocked if circumstances change. + > + > +6. Configure policies for each web API. Policies govern aspects such as whether cross-domain calls should be allowed, how to authenticate clients, whether to convert between XML and JSON data formats transparently, whether to restrict calls from a given IP range, usage quotas, and whether to limit the call rate. Policies can be applied globally across the entire product, for a single web API in a product, or for individual operations in a web API. + +You can find full details describing how to perform these tasks on the [API Management](https://azure.microsoft.com/services/api-management/) page on the Microsoft website. The Azure API Management Service also provides its own REST interface, enabling you to build a custom interface for simplifying the process of configuring a web API. For more information, visit the [Azure API Management REST API Reference](https://msdn.microsoft.com/library/azure/dn776326.aspx) page on the Microsoft website. + +> [!TIP] +> Azure provides the Azure Traffic Manager which enables you to implement failover and load-balancing, and reduce latency across multiple instances of a web site hosted in different geographic locations. You can use Azure Traffic Manager in conjunction with the API Management Service; the API Management Service can route requests to instances of a web site through Azure Traffic Manager. For more information, visit the [Traffic Manager routing Methods](/azure/traffic-manager/traffic-manager-routing-methods/) page on the Microsoft website. +> +> In this structure, if you are using custom DNS names for your web sites, you should configure the appropriate CNAME record for each web site to point to the DNS name of the Azure Traffic Manager web site. +> +> + +## Supporting developers building client applications +Developers constructing client applications typically require information on how to access the web API, and documentation concerning the parameters, data types, return types, and return codes that describe the different requests and responses between the web service and the client application. + +### Documenting the REST operations for a web API +The Azure API Management Service includes a developer portal that describes the REST operations exposed by a web API. When a product has been published it appears on this portal. Developers can use this portal to sign up for access; the administrator can then approve or deny the request. If the developer is approved, they are assigned a subscription key that is used to authenticate calls from the client applications that they develop. This key must be provided with each web API call otherwise it will be rejected. + +This portal also provides: + +* Documentation for the product, listing the operations that it exposes, the parameters required, and the different responses that can be returned. Note that this information is generated from the details provided in step 3 in the list in the Publishing a web API by using the Microsoft Azure API Management Service section. +* Code snippets that show how to invoke operations from several languages, including JavaScript, C#, Java, Ruby, Python, and PHP. +* A developers' console that enables a developer to send an HTTP request to test each operation in the product and view the results. +* A page where the developer can report any issues or problems found. + +The Azure Management portal enables you to customize the developer portal to change the styling and layout to match the branding of your organization. + +### Implementing a client SDK +Building a client application that invokes REST requests to access a web API requires writing a significant amount of code to construct each request and format it appropriately, send the request to the server hosting the web service, and parse the response to work out whether the request succeeded or failed and extract any data returned. To insulate the client application from these concerns, you can provide an SDK that wraps the REST interface and abstracts these low-level details inside a more functional set of methods. A client application uses these methods, which transparently convert calls into REST requests and then convert the responses back into method return values. This is a common technique that is implemented by many services, including the Azure SDK. + +Creating a client-side SDK is a considerable undertaking as it has to be implemented consistently and tested carefully. However, much of this process can be made mechanical, and many vendors supply tools that can automate many of these tasks. + +## Monitoring a web API +Depending on how you have published and deployed your web API you can monitor the web API directly, or you can gather usage and health information by analyzing the traffic that passes through the API Management service. + +### Monitoring a web API directly +If you have implemented your web API by using the ASP.NET Web API template (either as a Web API project or as a Web role in an Azure cloud service) and Visual Studio 2013, you can gather availability, performance, and usage data by using ASP.NET Application Insights. Application Insights is a package that transparently tracks and records information about requests and responses when the web API is deployed to the cloud; once the package is installed and configured, you don't need to amend any code in your web API to use it. When you deploy the web API to an Azure web site, all traffic is examined and the following statistics are gathered: + +* Server response time. +* Number of server requests and the details of each request. +* The top slowest requests in terms of average response time. +* The details of any failed requests. +* The number of sessions initiated by different browsers and user agents. +* The most frequently viewed pages (primarily useful for web applications rather than web APIs). +* The different user roles accessing the web API. + +You can view this data in real time from the Azure Management portal. You can also create webtests that monitor the health of the web API. A webtest sends a periodic request to a specified URI in the web API and captures the response. You can specify the definition of a successful response (such as HTTP status code 200), and if the request does not return this response you can arrange for an alert to be sent to an administrator. If necessary, the administrator can restart the server hosting the web API if it has failed. + +The [Application Insights - Get started with ASP.NET](/azure/application-insights/app-insights-asp-net/) page on the Microsoft website provides more information. + +### Monitoring a web API through the API Management Service +If you have published your web API by using the API Management service, the API Management page on the Azure Management portal contains a dashboard that enables you to view the overall performance of the service. The Analytics page enables you to drill down into the details of how the product is being used. This page contains the following tabs: + +* **Usage**. This tab provides information about the number of API calls made and the bandwidth used to handle these calls over time. You can filter usage details by product, API, and operation. +* **Health**. This tab enables you view the outcome of API requests (the HTTP status codes returned), the effectiveness of the caching policy, the API response time, and the service response time. Again, you can filter health data by product, API, and operation. +* **Activity**. This tab provides a text summary of the numbers of successful calls, failed called, blocked calls, average response time, and response times for each product, web API, and operation. This page also lists the number of calls made by each developer. +* **At a glance**. This tab displays a summary of the performance data, including the developers responsible for making the most API calls, and the products, web APIs, and operations that received these calls. + +You can use this information to determine whether a particular web API or operation is causing a bottleneck, and if necessary scale the host environment and add more servers. You can also ascertain whether one or more applications are using a disproportionate volume of resources and apply the appropriate policies to set quotas and limit call rates. + +> [!NOTE] +> You can change the details for a published product, and the changes are applied immediately. For example, you can add or remove an operation from a web API without requiring that you republish the product that contains the web API. +> +> + +## Related patterns +* The [façade](http://en.wikipedia.org/wiki/Facade_pattern) pattern describes how to provide an interface to a web API. + +## More information +* The page [Learn About ASP.NET Web API](http://www.asp.net/web-api) on the Microsoft website provides a detailed introduction to building RESTful web services by using the Web API. +* The page [Routing in ASP.NET Web API](http://www.asp.net/web-api/overview/web-api-routing-and-actions/routing-in-aspnet-web-api) on the Microsoft website describes how convention-based routing works in the ASP.NET Web API framework. +* For more information on attribute-based routing, see the page [Attribute Routing in Web API 2](http://www.asp.net/web-api/overview/web-api-routing-and-actions/attribute-routing-in-web-api-2) on the Microsoft website. +* The [Basic Tutorial](http://www.odata.org/getting-started/basic-tutorial/) page on the OData website provides an introduction to the features of the OData protocol. +* The [ASP.NET Web API OData](http://www.asp.net/web-api/overview/odata-support-in-aspnet-web-api) page on the Microsoft website contains examples and further information on implementing an OData web API by using ASP.NET. +* The page [Introducing Batch Support in Web API and Web API OData](http://blogs.msdn.com/b/webdev/archive/2013/11/01/introducing-batch-support-in-web-api-and-web-api-odata.aspx) on the Microsoft website describes how to implement batch operations in a web API by using OData. +* The article [Idempotency Patterns](http://blog.jonathanoliver.com/idempotency-patterns/) on Jonathan Oliver’s blog provides an overview of idempotency and how it relates to data management operations. +* The [Status Code Definitions](http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html) page on the W3C website contains a full list of HTTP status codes and their descriptions. +* For detailed information on handling HTTP exceptions with the ASP.NET Web API, visit the [Exception Handling in ASP.NET Web API](http://www.asp.net/web-api/overview/error-handling/exception-handling) page on the Microsoft website. +* The article [Web API Global Error Handling](http://www.asp.net/web-api/overview/error-handling/web-api-global-error-handling) on the Microsoft website describes how to implement a global error handling and logging strategy for a web API. +* The page [Run background tasks with WebJobs](/azure/app-service-web/web-sites-create-web-jobs/) on the Microsoft website provides information and examples on using WebJobs to perform background operations on an Azure Website. +* The page [Azure Notification Hubs Notify Users](/azure/notification-hubs/notification-hubs-aspnet-backend-windows-dotnet-wns-notification/) on the Microsoft website shows how you can use an Azure Notification Hub to push asynchronous responses to client applications. +* The [API Management](https://azure.microsoft.com/services/api-management/) page on the Microsoft website describes how to publish a product that provides controlled and secure access to a web API. +* The [Azure API Management REST API Reference](https://msdn.microsoft.com/library/azure/dn776326.aspx) page on the Microsoft website describes how to use the API Management REST API to build custom management applications. +* The [Traffic Manager Routing Methods](/azure/traffic-manager/traffic-manager-routing-methods/) page on the Microsoft website summarizes how Azure Traffic Manager can be used to load-balance requests across multiple instances of a website hosting a web API. +* The [Application Insights - Get started with ASP.NET](/azure/application-insights/app-insights-asp-net/) page on the Microsoft website provides detailed information on installing and configuring Application Insights in an ASP.NET Web API project. +* The page [Verifying Code by Using Unit Tests](https://msdn.microsoft.com/library/dd264975.aspx) on the Microsoft website provides detailed information on creating and managing unit tests by using Visual Studio. +* The page [Run performance tests on an application before a release](https://msdn.microsoft.com/library/dn250793.aspx) on the Microsoft website describes how to use Visual Studio Ultimate to create a web performance and load test project. diff --git a/docs/best-practices/auto-scaling.md b/docs/best-practices/auto-scaling.md new file mode 100644 index 00000000000..2854424baa2 --- /dev/null +++ b/docs/best-practices/auto-scaling.md @@ -0,0 +1,116 @@ +--- +title: Autoscaling guidance +description: Guidance on how to autoscale to dynamically allocate resources required by an application. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 25bba4f9-9ca5-48d1-ac8b-08a3441ba696 +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# Autoscaling +[!INCLUDE [header](../_includes/header.md)] + +Autoscaling is the process of dynamically allocating the resources required by an application to match performance requirements and satisfy service-level agreements (SLAs), while minimizing runtime costs. As the volume of work grows, an application may require additional resources to enable it to perform its tasks in a timely manner. As demand slackens, resources can be de-allocated to minimize costs, while still maintaining adequate performance and meeting SLAs. +Autoscaling takes advantage of the elasticity of cloud-hosted environments while easing management overhead. It does so by reducing the need for an operator to continually monitor the performance of a system and make decisions about adding or removing resources. + +> [!NOTE] +> Autoscaling applies to all of the resources used by an application, not just the compute resources. For example, if your system uses message queues to send and receive information, it could create additional queues as it scales. +> +> + +## Types of scaling +Scaling typically takes one of the following two forms: + +* **Vertical** (often referred to as *scaling up and down*). This form requires that you modify the hardware (expand or reduce its capacity and performance), or redeploy the solution using alternative hardware that has the appropriate capacity and performance. In a cloud environment, the hardware platform is typically a virtualized environment. Unless the original hardware was substantially overprovisioned, with the consequent upfront capital expense, vertically scaling up in this environment involves provisioning more powerful resources, and then moving the system onto these new resources. Vertical scaling is often a disruptive process that requires making the system temporarily unavailable while it is being redeployed. It may be possible to keep the original system running while the new hardware is provisioned and brought online, but there will likely be some interruption while the processing transitions from the old environment to the new one. It is uncommon to use autoscaling to implement a vertical scaling strategy. +* **Horizontal** (often referred to as *scaling out and in*). This form requires deploying the solution on additional or fewer resources, which are typically commodity resources rather than high-powered systems. The solution can continue running without interruption while these resources are provisioned. When the provisioning process is complete, copies of the elements that comprise the solution can be deployed on these additional resources and made available. If demand drops, the additional resources can be reclaimed after the elements using them have been shut down cleanly. Many cloud-based systems, including Microsoft Azure, support automation of this form of scaling. + +## Implement an autoscaling strategy +Implementing an autoscaling strategy typically involves the following components and processes: + +* Instrumentation and monitoring systems at the application, service, and infrastructure levels. These systems capture key metrics, such as response times, queue lengths, CPU utilization, and memory usage. +* Decision-making logic that can evaluate the monitored scaling factors against predefined system thresholds or schedules, and make decisions regarding whether to scale or not. +* Components that are responsible for carrying out tasks associated with scaling the system, such as provisioning or de-provisioning resources. +* Testing, monitoring, and tuning of the autoscaling strategy to ensure that it functions as expected. + +Most cloud-based environments, such as Azure, provide built-in autoscaling mechanisms that address common scenarios. If the environment or service you use doesn't provide the necessary automated scaling functionality, or if you have extreme autoscaling requirements beyond its capabilities, a custom implementation may be necessary. Use this customized implementation to collect operational and system metrics, analyze them to identify relevant data, and then scale resources accordingly. + +## Configure autoscaling for an Azure solution +There are several options for configuring autoscaling for your Azure solutions: + +* **Azure Autoscale** supports the most common scaling scenarios based on a schedule and, optionally, triggered scaling operations based on runtime metrics (such as processor utilization, queue length, or built-in and custom counters). You can configure simple autoscaling policies for a solution quickly and easily by using the Azure portal. For more detailed control, you can make use of the [Azure Service Management REST API](https://msdn.microsoft.com/library/azure/ee460799.aspx) or the [Azure Resource Manager REST API](https://msdn.microsoft.com//library/azure/dn790568.aspx). The [Azure Monitoring Service Management Library](http://www.nuget.org/packages/Microsoft.WindowsAzure.Management.Monitoring) and the [Microsoft Insights Library](https://www.nuget.org/packages/Microsoft.Azure.Insights/) (in preview) are SDKs that allow collecting metrics from different resources, and perform autoscaling by making use of the REST APIs. For resources where Azure Resource Manager support isn't available, or if you are using Azure Cloud Services, the Service Management REST API can be used for autoscaling. In all other cases, use Azure Resource Manager. +* **A custom solution**, based on your instrumentation on the application, and management features of Azure, can be useful. For example, you could use Azure diagnostics or other methods of instrumentation in your application, along with custom code to continually monitor and export metrics of the application. You could have custom rules that work on these metrics, and make use of the Service Management or Resource Manager REST API's to trigger autoscaling. The metrics for triggering a scaling operation can be any built-in or custom counter, or other instrumentation you implement within the application. However, a custom solution is not simple to implement, and should be considered only if none of the previous approaches can fulfill your requirements. The [Autoscaling Application Block](http://msdn.microsoft.com/library/hh680892%28v=pandp.50%29.aspx) makes use of this approach. +* **Third-party services**, such as [Paraleap AzureWatch](http://www.paraleap.com/AzureWatch), enable you to scale a solution based on schedules, service load and system performance indicators, custom rules, and combinations of different types of rules. + +When choosing which autoscaling solution to adopt, consider the following points: + +* Use the built-in autoscaling features of the platform, if they can meet your requirements. If not, carefully consider whether you really do need more complex scaling features. Some examples of additional requirements may include more granularity of control, different ways to detect trigger events for scaling, scaling across subscriptions, and scaling other types of resources. +* Consider if you can predict the load on the application with sufficient accuracy to depend only on scheduled autoscaling (adding and removing instances to meet anticipated peaks in demand). Where this isn't possible, use reactive autoscaling based on metrics collected at runtime, to allow the application to handle unpredictable changes in demand. Typically, you can combine these approaches. For example, create a strategy that adds resources such as compute, storage, and queues, based on a schedule of the times when you know the application is most busy. This helps to ensure that capacity is available when required, without the delay encountered when starting new instances. In addition, for each scheduled rule, define metrics that allow reactive autoscaling during that period to ensure that the application can handle sustained but unpredictable peaks in demand. +* It's often difficult to understand the relationship between metrics and capacity requirements, especially when an application is initially deployed. Prefer to provision a little extra capacity at the beginning, and then monitor and tune the autoscaling rules to bring the capacity closer to the actual load. + +### Use Azure Autoscale +Autoscale enables you to configure scale out and scale in options for a solution. Autoscale can automatically add and remove instances of Azure Cloud Services web and worker roles, Azure Mobile Services, and Web Apps feature in Azure App Service. It can also enable automatic scaling by starting and stopping instances of Azure Virtual Machines. An Azure autoscaling strategy includes two sets of factors: + +* Schedule-based autoscaling that can ensure additional instances are available to coincide with an expected peak in usage, and can scale in once the peak time has passed. This enables you to ensure that you have sufficient instances already running, without waiting for the system to react to the load. +* Metrics-based autoscaling that reacts to factors such as average CPU utilization over the last hour, or the backlog of messages that the solution is processing in an Azure storage or Azure Service Bus queue. This allows the application to react separately from the scheduled autoscaling rules to accommodate unplanned or unforeseen changes in demand. + +Consider the following points when using Autoscale: + +* Your autoscaling strategy combines both scheduled and metrics-based scaling. You can specify both types of rules for a service. +* You should configure the autoscaling rules, and then monitor the performance of your application over time. Use the results of this monitoring to adjust the way in which the system scales if necessary. However, keep in mind that autoscaling is not an instantaneous process. It takes time to react to a metric such as average CPU utilization exceeding (or falling below) a specified threshold. +* Autoscaling rules that use a detection mechanism based on a measured trigger attribute (such as CPU usage or queue length) use an aggregated value over time, rather than instantaneous values, to trigger an autoscaling action. By default, the aggregate is an average of the values. This prevents the system from reacting too quickly, or causing rapid oscillation. It also allows time for new instances that are auto-started to settle into running mode, preventing additional autoscaling actions from occurring while the new instances are starting up. For Azure Cloud Services and Azure Virtual Machines, the default period for the aggregation is 45 minutes, so it can take up to this period of time for the metric to trigger autoscaling in response to spikes in demand. You can change the aggregation period by using the SDK, but be aware that periods of fewer than 25 minutes may cause unpredictable results (for more information, see [Auto Scaling Cloud Services on CPU Percentage with the Azure Monitoring Services Management Library](http://rickrainey.com/2013/12/15/auto-scaling-cloud-services-on-cpu-percentage-with-the-windows-azure-monitoring-services-management-library/)). For Web Apps, the averaging period is much shorter, allowing new instances to be available in about five minutes after a change to the average trigger measure. +* If you configure autoscaling using the SDK rather than the web portal, you can specify a more detailed schedule during which the rules are active. You can also create your own metrics and use them with or without any of the existing ones in your autoscaling rules. For example, you may wish to use alternative counters, such as the number of requests per second or the average memory availability, or use custom counters that measure specific business processes. +* When autoscaling Azure Virtual Machines, you must deploy a number of instances of the virtual machine that is equal to the maximum number you will allow autoscaling to start. These instances must be part of the same availability set. The Virtual Machines autoscaling mechanism does not create or delete instances of the virtual machine; instead, the autoscaling rules you configure will start and stop an appropriate number of these instances. For more information, see [Automatically scale an application running Web Roles, Worker Roles, or Virtual Machines](/azure/cloud-services/cloud-services-how-to-scale/). +* If new instances cannot be started, perhaps because the maximum for a subscription has been reached or an error occurs during startup, the portal may show that an autoscaling operation succeeded. However, subsequent **ChangeDeploymentConfiguration** events displayed in the portal will show only that a service startup was requested, and there will be no event to indicate it was successfully completed. +* You can use the web portal UI to link resources such as SQL Database instances and queues to a compute service instance. This allows you to more easily access the separate manual and automatic scaling configuration options for each of the linked resources. For more information, see "How to: Link a resource to a cloud service" in [How to Manage Cloud Services](/azure/cloud-services/cloud-services-how-to-manage) and [How to Scale an Application](/azure/cloud-services/cloud-services-how-to-scale/). +* When you configure multiple policies and rules, they could conflict with each other. Autoscale uses the following conflict resolution rules to ensure that there is always a sufficient number of instances running: + * Scale out operations always take precedence over scale in operations. + * When scale out operations conflict, the rule that initiates the largest increase in the number of instances takes precedence. + * When scale in operations conflict, the rule that initiates the smallest decrease in the number of instances takes precedence. + + + +## Application design considerations for implementing autoscaling +Autoscaling isn't an instant solution. Simply adding resources to a system or running more instances of a process doesn't guarantee that the performance of the system will improve. Consider the following points when designing an autoscaling strategy: + +* The system must be designed to be horizontally scalable. Avoid making assumptions about instance affinity; do not design solutions that require that the code is always running in a specific instance of a process. When scaling a cloud service or web site horizontally, don't assume that a series of requests from the same source will always be routed to the same instance. For the same reason, design services to be stateless to avoid requiring a series of requests from an application to always be routed to the same instance of a service. When designing a service that reads messages from a queue and processes them, don't make any assumptions about which instance of the service handles a specific message. Autoscaling could start additional instances of a service as the queue length grows. The [Competing Consumers Pattern](http://msdn.microsoft.com/library/dn568101.aspx) describes how to handle this scenario. +* If the solution implements a long-running task, design this task to support both scaling out and scaling in. Without due care, such a task could prevent an instance of a process from being shut down cleanly when the system scales in, or it could lose data if the process is forcibly terminated. Ideally, refactor a long-running task and break up the processing that it performs into smaller, discrete chunks. The [Pipes and Filters Pattern](http://msdn.microsoft.com/library/dn568100.aspx) provides an example of how you can achieve this. +* Alternatively, you can implement a checkpoint mechanism that records state information about the task at regular intervals, and save this state in durable storage that can be accessed by any instance of the process running the task. In this way, if the process is shutdown, the work that it was performing can be resumed from the last checkpoint by using another instance. +* When background tasks run on separate compute instances, such as in worker roles of a cloud services hosted application, you may need to scale different parts of the application using different scaling policies. For example, you may need to deploy additional user interface (UI) compute instances without increasing the number of background compute instances, or the opposite of this. If you offer different levels of service (such as basic and premium service packages), you may need to scale out the compute resources for premium service packages more aggressively than those for basic service packages in order to meet SLAs. +* Consider using the length of the queue over which UI and background compute instances communicate as a criterion for your autoscaling strategy. This is the best indicator of an imbalance or difference between the current load and the processing capacity of the background task. +* If you base your autoscaling strategy on counters that measure business processes, such as the number of orders placed per hour or the average execution time of a complex transaction, ensure that you fully understand the relationship between the results from these types of counters and the actual compute capacity requirements. It may be necessary to scale more than one component or compute unit in response to changes in business process counters. +* To prevent a system from attempting to scale out excessively, and to avoid the costs associated with running many thousands of instances, consider limiting the maximum number of instances that can be automatically added. Most autoscaling mechanisms allow you to specify the minimum and maximum number of instances for a rule. In addition, consider gracefully degrading the functionality that the system provides if the maximum number of instances have been deployed, and the system is still overloaded. +* Keep in mind that autoscaling might not be the most appropriate mechanism to handle a sudden burst in workload. It takes time to provision and start new instances of a service or add resources to a system, and the peak demand may have passed by the time these additional resources have been made available. In this scenario, it may be better to throttle the service. For more information, see the [Throttling Pattern](http://msdn.microsoft.com/library/dn589798.aspx). +* Conversely, if you do need the capacity to process all requests when the volume fluctuates rapidly, and cost isn't a major contributing factor, consider using an aggressive autoscaling strategy that starts additional instances more quickly. You can also use a scheduled policy that starts a sufficient number of instances to meet the maximum load before that load is expected. +* The autoscaling mechanism should monitor the autoscaling process, and log the details of each autoscaling event (what triggered it, what resources were added or removed, and when). If you create a custom autoscaling mechanism, ensure that it incorporates this capability. Analyze the information to help measure the effectiveness of the autoscaling strategy, and tune it if necessary. You can tune both in the short term, as the usage patterns become more obvious, and over the long term, as the business expands or the requirements of the application evolve. If an application reaches the upper limit defined for autoscaling, the mechanism might also alert an operator who could manually start additional resources if necessary. Note that, under these circumstances, the operator may also be responsible for manually removing these resources after the workload eases. + +## Related patterns and guidance +The following patterns and guidance may also be relevant to your scenario when implementing autoscaling: + +* [Throttling Pattern](http://msdn.microsoft.com/library/dn589798.aspx). This pattern describes how an application can continue to function and meet SLAs when an increase in demand places an extreme load on resources. Throttling can be used with autoscaling to prevent a system from being overwhelmed while the system scales out. +* [Competing Consumers Pattern](http://msdn.microsoft.com/library/dn568101.aspx). This pattern describes how to implement a pool of service instances that can handle messages from any application instance. Autoscaling can be used to start and stop service instances to match the anticipated workload. This approach enables a system to process multiple messages concurrently to optimize throughput, improve scalability and availability, and balance the workload. +* [Instrumentation and Telemetry Guidance](http://msdn.microsoft.com/library/dn589775.aspx). Instrumentation and telemetry are vital for gathering the information that can drive the autoscaling process. + +## More information +* [How to Scale an Application](/azure/cloud-services/cloud-services-how-to-scale/) +* [Automatically scale an application running Web Roles, Worker Roles, or Virtual Machines](/azure/cloud-services/cloud-services-how-to-manage) +* [How to: Link a resource to a cloud service](/azure/cloud-services/cloud-services-how-to-manage) +* [Scale linked resources](/azure/cloud-services/cloud-services-how-to-scale) +* [Azure Monitoring Services Management Library](http://www.nuget.org/packages/Microsoft.WindowsAzure.Management.Monitoring) +* [Azure Service Management REST API](http://msdn.microsoft.com/library/azure/ee460799.aspx) +* [Azure Resource Manager REST API](https://msdn.microsoft.com/library/azure/dn790568.aspx) +* [Microsoft Insights library](https://www.nuget.org/packages/Microsoft.Azure.Insights/) +* [Operations on Autoscaling](http://msdn.microsoft.com/library/azure/dn510374.aspx) +* [Microsoft.WindowsAzure.Management.Monitoring.Autoscale Namespace](http://msdn.microsoft.com/library/azure/microsoft.windowsazure.management.monitoring.autoscale.aspx) diff --git a/docs/best-practices/background-jobs.md b/docs/best-practices/background-jobs.md new file mode 100644 index 00000000000..937f9d77402 --- /dev/null +++ b/docs/best-practices/background-jobs.md @@ -0,0 +1,298 @@ +--- +title: Background jobs guidance +description: Guidance on background tasks that run independently of the user interface. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: f6040f8c-4cbb-4c21-a886-8d54a5868421 +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/21/2016 +ms.author: masashin + +--- +# Background jobs +[!INCLUDE [header](../_includes/header.md)] + +Many types of applications require background tasks that run independently of the user interface (UI). Examples include batch jobs, intensive processing tasks, and long-running processes such as workflows. Background jobs can be executed without requiring user interaction--the application can start the job and then continue to process interactive requests from users. This can help to minimize the load on the application UI, which can improve availability and reduce interactive response times. + +For example, if an application is required to generate thumbnails of images that are uploaded by users, it can do this as a background job and save the thumbnail to storage when it is complete--without the user needing to wait for the process to be completed. In the same way, a user placing an order can initiate a background workflow that processes the order, while the UI allows the user to continue browsing the web app. When the background job is complete, it can update the stored orders data and send an email to the user that confirms the order. + +When you consider whether to implement a task as a background job, the main criteria is whether the task can run without user interaction and without the UI needing to wait for the job to be completed. Tasks that require the user or the UI to wait while they are completed might not be appropriate as background jobs. + +## Types of background jobs +Background jobs typically include one or more of the following types of jobs: + +* CPU-intensive jobs, such as mathematical calculations or structural model analysis. +* I/O-intensive jobs, such as executing a series of storage transactions or indexing files. +* Batch jobs, such as nightly data updates or scheduled processing. +* Long-running workflows, such as order fulfillment, or provisioning services and systems. +* Sensitive-data processing where the task is handed off to a more secure location for processing. For example, you might not want to process sensitive data within a web app. Instead, you might use a pattern such as [Gatekeeper](http://msdn.microsoft.com/library/dn589793.aspx) to transfer the data to an isolated background process that has access to protected storage. + +## Triggers +Background jobs can be initiated in several different ways. They fall into one of the following categories: + +* [**Event-driven triggers**](#event-driven-triggers). The task is started in response to an event, typically an action taken by a user or a step in a workflow. +* [**Schedule-driven triggers**](#schedule-driven-triggers). The task is invoked on a schedule based on a timer. This might be a recurring schedule or a one-off invocation that is specified for a later time. + +### Event-driven triggers +Event-driven invocation uses a trigger to start the background task. Examples of using event-driven triggers include: + +* The UI or another job places a message in a queue. The message contains data about an action that has taken place, such as the user placing an order. The background task listens on this queue and detects the arrival of a new message. It reads the message and uses the data in it as the input to the background job. +* The UI or another job saves or updates a value in storage. The background task monitors the storage and detects changes. It reads the data and uses it as the input to the background job. +* The UI or another job makes a request to an endpoint, such as an HTTPS URI, or an API that is exposed as a web service. It passes the data that is required to complete the background task as part of the request. The endpoint or web service invokes the background task, which uses the data as its input. + +Typical examples of tasks that are suited to event-driven invocation include image processing, workflows, sending information to remote services, sending email messages, and provisioning new users in multitenant applications. + +### Schedule-driven triggers +Schedule-driven invocation uses a timer to start the background task. Examples of using schedule-driven triggers include: + +* A timer that is running locally within the application or as part of the application’s operating system invokes a background task on a regular basis. +* A timer that is running in a different application, or a timer service such as Azure Scheduler, sends a request to an API or web service on a regular basis. The API or web service invokes the background task. +* A separate process or application starts a timer that causes the background task to be invoked once after a specified time delay, or at a specific time. + +Typical examples of tasks that are suited to schedule-driven invocation include batch-processing routines (such as updating related-products lists for users based on their recent behavior), routine data processing tasks (such as updating indexes or generating accumulated results), data analysis for daily reports, data retention cleanup, and data consistency checks. + +If you use a schedule-driven task that must run as a single instance, be aware of the following: + +* If the compute instance that is running the scheduler (such as a virtual machine using Windows scheduled tasks) is scaled, you will have multiple instances of the scheduler running. These could start multiple instances of the task. +* If tasks run for longer than the period between scheduler events, the scheduler may start another instance of the task while the previous one is still running. + +## Returning results +Background jobs execute asynchronously in a separate process, or even in a separate location, from the UI or the process that invoked the background task. Ideally, background tasks are “fire and forget” operations, and their execution progress has no impact on the UI or the calling process. This means that the calling process does not wait for completion of the tasks. Therefore, it cannot automatically detect when the task ends. + +If you require a background task to communicate with the calling task to indicate progress or completion, you must implement a mechanism for this. Some examples are: + +* Write a status indicator value to storage that is accessible to the UI or caller task, which can monitor or check this value when required. Other data that the background task must return to the caller can be placed into the same storage. +* Establish a reply queue that the UI or caller listens on. The background task can send messages to the queue that indicate status and completion. Data that the background task must return to the caller can be placed into the messages. If you are using Azure Service Bus, you can use the **ReplyTo** and **CorrelationId** properties to implement this capability. For more information, see [Correlation in Service Bus Brokered Messaging](http://www.cloudcasts.net/devguide/Default.aspx?id=13029). +* Expose an API or endpoint from the background task that the UI or caller can access to obtain status information. Data that the background task must return to the caller can be included in the response. +* Have the background task call back to the UI or caller through an API to indicate status at predefined points or on completion. This might be through events raised locally or through a publish-and-subscribe mechanism. Data that the background task must return to the caller can be included in the request or event payload. + +## Hosting environment +You can host background tasks by using a range of different Azure platform services: + +* [**Azure Web Apps and WebJobs**](#azure-web-apps-and-webjobs). You can use WebJobs to execute custom jobs based on a range of different types of scripts or executable programs within the context of a web app. +* [**Azure Cloud Services web and worker roles**](#azure-cloud-services-web-and-worker-roles). You can write code within a role that executes as a background task. +* [**Azure Virtual Machines**](#azure-virtual-machines). If you have a Windows service or want to use the Windows Task Scheduler, it is common to host your background tasks within a dedicated virtual machine. +* [**Azure Batch**](/azure/batch/batch-technical-overview/). It's a platform service that schedules compute-intensive work to run on a managed collection of virtual machines, and can automatically scale compute resources to meet the needs of your jobs. + +The following sections describe each of these options in more detail, and include considerations to help you choose the appropriate option. + +## Azure Web Apps and WebJobs +You can use Azure WebJobs to execute custom jobs as background tasks within an Azure Web App. WebJobs run within the context of your web app as a continuous process. WebJobs also run in response to a trigger event from Azure Scheduler or external factors, such as changes to storage blobs and message queues. Jobs can be started and stopped on demand, and shut down gracefully. If a continuously running WebJob fails, it is automatically restarted. Retry and error actions are configurable. + +When you configure a WebJob: + +* If you want the job to respond to an event-driven trigger, you should configure it as **Run continuously**. The script or program is stored in the folder named site/wwwroot/app_data/jobs/continuous. +* If you want the job to respond to a schedule-driven trigger, you should configure it as **Run on a schedule**. The script or program is stored in the folder named site/wwwroot/app_data/jobs/triggered. +* If you choose the **Run on demand** option when you configure a job, it will execute the same code as the **Run on a schedule** option when you start it. + +Azure WebJobs run within the sandbox of the web app. This means that they can access environment variables and share information, such as connection strings, with the web app. The job has access to the unique identifier of the machine that is running the job. The connection string named **AzureWebJobsStorage** provides access to Azure storage queues, blobs, and tables for application data, and access to Service Bus for messaging and communication. The connection string named **AzureWebJobsDashboard** provides access to the job action log files. + +Azure WebJobs have the following characteristics: + +* **Security**: WebJobs are protected by the deployment credentials of the web app. +* **Supported file types**: You can define WebJobs by using command scripts (.cmd), batch files (.bat), PowerShell scripts (.ps1), bash shell scripts (.sh), PHP scripts (.php), Python scripts (.py), JavaScript code (.js), and executable programs (.exe, .jar, and more). +* **Deployment**: You can deploy scripts and executables by using the Azure portal, by using the [WebJobsVs](https://marketplace.visualstudio.com/items?itemName=Sayed-Ibrahim-Hashimi.WebJobsVs) add-in for Visual Studio or the [Visual Studio 2013 Update 4](http://www.visualstudio.com/news/vs2013-update4-rc-vs) (you can create and deploy with this option), by using the [Azure WebJobs SDK](/azure/app-service-web/websites-dotnet-webjobs-sdk-get-started/), or by copying them directly to the following locations: + * For triggered execution: site/wwwroot/app_data/jobs/triggered/{job name} + * For continuous execution: site/wwwroot/app_data/jobs/continuous/{job name} +* **Logging**: Console.Out is treated (marked) as INFO. Console.Error is treated as ERROR. You can access monitoring and diagnostics information by using the Azure portal. You can download log files directly from the site. They are saved in the following locations: + * For triggered execution: Vfs/data/jobs/triggered/jobName + * For continuous execution: Vfs/data/jobs/continuous/jobName +* **Configuration**: You can configure WebJobs by using the portal, the REST API, and PowerShell. You can use a configuration file named settings.job in the same root directory as the job script to provide configuration information for a job. For example: + * { "stopping_wait_time": 60 } + * { "is_singleton": true } + +### Considerations +* By default, WebJobs scale with the web app. However, you can configure jobs to run on single instance by setting the **is_singleton** configuration property to **true**. Single instance WebJobs are useful for tasks that you do not want to scale or run as simultaneous multiple instances, such as reindexing, data analysis, and similar tasks. +* To minimize the impact of jobs on the performance of the web app, consider creating an empty Azure Web App instance in a new App Service plan to host WebJobs that may be long running or resource intensive. + +### More information +* [Azure WebJobs recommended resources](/azure/app-service-web/websites-webjobs-resources/) lists the many useful resources, downloads, and samples for WebJobs. + +## Azure Cloud Services web and worker roles +You can execute background tasks within a web role or in a separate worker role. When you are deciding whether to use a worker role, consider scalability and elasticity requirements, task lifetime, release cadence, security, fault tolerance, contention, complexity, and the logical architecture. For more information, see [Compute Resource Consolidation Pattern](http://msdn.microsoft.com/library/dn589778.aspx). + +There are several ways to implement background tasks within a Cloud Services role: + +* Create an implementation of the **RoleEntryPoint** class in the role and use its methods to execute background tasks. The tasks run in the context of WaIISHost.exe. They can use the **GetSetting** method of the **CloudConfigurationManager** class to load configuration settings. For more information, see [Lifecycle (Cloud Services)](#lifecycle-cloud-services). +* Use startup tasks to execute background tasks when the application starts. To force the tasks to continue to run in the background, set the **taskType** property to **background** (if you do not do this, the application startup process will halt and wait for the task to finish). For more information, see [Run startup tasks in Azure](/azure/cloud-services/cloud-services-startup-tasks/). +* Use the WebJobs SDK to implement background tasks such as WebJobs that are initiated as a startup task. For more information, see [Get started with the Azure WebJobs SDK](/azure/app-service-web/websites-dotnet-webjobs-sdk-get-started/). +* Use a startup task to install a Windows service that executes one or more background tasks. You must set the **taskType** property to **background** so that the service executes in the background. For more information, see [Run startup tasks in Azure](/azure/cloud-services/cloud-services-startup-tasks/). + +### Running background tasks in the web role +The main advantage of running background tasks in the web role is the saving in hosting costs because there is no requirement to deploy additional roles. + +### Running background tasks in a worker role +Running background tasks in a worker role has several advantages: + +* It allows you to manage scaling separately for each type of role. For example, you might need more instances of a web role to support the current load, but fewer instances of the worker role that executes background tasks. By scaling background task compute instances separately from the UI roles, you can reduce hosting costs, while maintaining acceptable performance. +* It offloads the processing overhead for background tasks from the web role. The web role that provides the UI can remain responsive, and it may mean fewer instances are required to support a given volume of requests from users. +* It allows you to implement separation of concerns. Each role type can implement a specific set of clearly defined and related tasks. This makes designing and maintaining the code easier because there is less interdependence of code and functionality between each role. +* It can help to isolate sensitive processes and data. For example, web roles that implement the UI do not need to have access to data that is managed and controlled by a worker role. This can be useful in strengthening security, especially when you use a pattern such as the [Gatekeeper Pattern](http://msdn.microsoft.com/library/dn589793.aspx). + +### Considerations +Consider the following points when choosing how and where to deploy background tasks when using Cloud Services web and worker roles: + +* Hosting background tasks in an existing web role can save the cost of running a separate worker role just for these tasks. However, it is likely to affect the performance and availability of the application if there is contention for processing and other resources. Using a separate worker role protects the web role from the impact of long-running or resource-intensive background tasks. +* If you host background tasks by using the **RoleEntryPoint** class, you can easily move this to another role. For example, if you create the class in a web role and later decide that you need to run the tasks in a worker role, you can move the **RoleEntryPoint** class implementation into the worker role. +* Startup tasks are designed to execute a program or a script. Deploying a background job as an executable program might be more difficult, especially if it also requires deployment of dependent assemblies. It might be easier to deploy and use a script to define a background job when you use startup tasks. +* Exceptions that cause a background task to fail have a different impact, depending on the way that they are hosted: + * If you use the **RoleEntryPoint** class approach, a failed task will cause the role to restart so that the task automatically restarts. This can affect availability of the application. To prevent this, ensure that you include robust exception handling within the **RoleEntryPoint** class and all the background tasks. Use code to restart tasks that fail where this is appropriate, and throw the exception to restart the role only if you cannot gracefully recover from the failure within your code. + * If you use startup tasks, you are responsible for managing the task execution and checking if it fails. +* Managing and monitoring startup tasks is more difficult than using the **RoleEntryPoint** class approach. However, the Azure WebJobs SDK includes a dashboard to make it easier to manage WebJobs that you initiate through startup tasks. + +### More information +* [Compute Resource Consolidation Pattern](http://msdn.microsoft.com/library/dn589778.aspx) +* [Get started with the Azure WebJobs SDK](/azure/app-service-web/websites-dotnet-webjobs-sdk-get-started/) + +## Azure Virtual Machines +Background tasks might be implemented in a way that prevents them from being deployed to Azure Web Apps or Cloud Services, or these options might not be convenient. Typical examples are Windows services, and third-party utilities and executable programs. Another example might be programs written for an execution environment that is different than that hosting the application. For example, it might be a Unix or Linux program that you want to execute from a Windows or .NET application. You can choose from a range of operating systems for an Azure virtual machine, and run your service or executable on that virtual machine. + +To help you choose when to use Virtual Machines, see [Azure App Services, Cloud Services and Virtual Machines comparison](/azure/app-service-web/choose-web-site-cloud-service-vm/). For information about the options for Virtual Machines, see [Virtual Machine and Cloud Service sizes for Azure](http://msdn.microsoft.com/library/azure/dn197896.aspx). For more information about the operating systems and prebuilt images that are available for Virtual Machines, see [Azure Virtual Machines Marketplace](https://azure.microsoft.com/gallery/virtual-machines/). + +To initiate the background task in a separate virtual machine, you have a range of options: + +* You can execute the task on demand directly from your application by sending a request to an endpoint that the task exposes. This passes in any data that the task requires. This endpoint invokes the task. +* You can configure the task to run on a schedule by using a scheduler or timer that is available in your chosen operating system. For example, on Windows you can use Windows Task Scheduler to execute scripts and tasks. Or, if you have SQL Server installed on the virtual machine, you can use the SQL Server Agent to execute scripts and tasks. +* You can use Azure Scheduler to initiate the task by adding a message to a queue that the task listens on, or by sending a request to an API that the task exposes. + +See the earlier section [Triggers](#triggers) for more information about how you can initiate background tasks. + +### Considerations +Consider the following points when you are deciding whether to deploy background tasks in an Azure virtual machine: + +* Hosting background tasks in a separate Azure virtual machine provides flexibility and allows precise control over initiation, execution, scheduling, and resource allocation. However, it will increase runtime cost if a virtual machine must be deployed just to run background tasks. +* There is no facility to monitor the tasks in the Azure portal and no automated restart capability for failed tasks--although you can monitor the basic status of the virtual machine and manage it by using the [Azure Service Management Cmdlets](http://msdn.microsoft.com/library/azure/dn495240.aspx). However, there are no facilities to control processes and threads in compute nodes. Typically, using a virtual machine will require additional effort to implement a mechanism that collects data from instrumentation in the task, and from the operating system in the virtual machine. One solution that might be appropriate is to use the [System Center Management Pack for Azure](http://technet.microsoft.com/library/gg276383.aspx). +* You might consider creating monitoring probes that are exposed through HTTP endpoints. The code for these probes could perform health checks, collect operational information and statistics--or collate error information and return it to a management application. For more information, see [Health Endpoint Monitoring Pattern](http://msdn.microsoft.com/library/dn589789.aspx). + +### More information +* [Virtual Machines](https://azure.microsoft.com/services/virtual-machines/) on Azure +* [Azure Virtual Machines FAQ](/azure/virtual-machines/virtual-machines-linux-classic-faq?toc=%2fazure%2fvirtual-machines%2flinux%2fclassic%2ftoc.json) + +## Design considerations +There are several fundamental factors to consider when you design background tasks. The following sections discuss partitioning, conflicts, and coordination. + +## Partitioning +If you decide to include background tasks within an existing compute instance (such as a web app, web role, existing worker role, or virtual machine), you must consider how this will affect the quality attributes of the compute instance and the background task itself. These factors will help you to decide whether to colocate the tasks with the existing compute instance or separate them out into a separate compute instance: + +* **Availability**: Background tasks might not need to have the same level of availability as other parts of the application, in particular the UI and other parts that are directly involved in user interaction. Background tasks might be more tolerant of latency, retried connection failures, and other factors that affect availability because the operations can be queued. However, there must be sufficient capacity to prevent the backup of requests that could block queues and affect the application as a whole. +* **Scalability**: Background tasks are likely to have a different scalability requirement than the UI and the interactive parts of the application. Scaling the UI might be necessary to meet peaks in demand, while outstanding background tasks might be completed during less busy times by a fewer number of compute instances. +* **Resiliency**: Failure of a compute instance that just hosts background tasks might not fatally affect the application as a whole if the requests for these tasks can be queued or postponed until the task is available again. If the compute instance and/or tasks can be restarted within an appropriate interval, users of the application might not be affected. +* **Security**: Background tasks might have different security requirements or restrictions than the UI or other parts of the application. By using a separate compute instance, you can specify a different security environment for the tasks. You can also use patterns such as Gatekeeper to isolate the background compute instances from the UI in order to maximize security and separation. +* **Performance**: You can choose the type of compute instance for background tasks to specifically match the performance requirements of the tasks. This might mean using a less expensive compute option if the tasks do not require the same processing capabilities as the UI, or a larger instance if they require additional capacity and resources. +* **Manageability**: Background tasks might have a different development and deployment rhythm from the main application code or the UI. Deploying them to a separate compute instance can simplify updates and versioning. +* **Cost**: Adding compute instances to execute background tasks increases hosting costs. You should carefully consider the trade-off between additional capacity and these extra costs. + +For more information, see [Leader Election Pattern](http://msdn.microsoft.com/library/dn568104.aspx) and [Competing Consumers +Pattern](http://msdn.microsoft.com/library/dn568101.aspx). + +## Conflicts +If you have multiple instances of a background job, it is possible that they will compete for access to resources and services, such as databases and storage. This concurrent access can result in resource contention, which might cause conflicts in availability of the services and in the integrity of data in storage. You can resolve resource contention by using a pessimistic locking approach. This prevents competing instances of a task from concurrently accessing a service or corrupting data. + +Another approach to resolve conflicts is to define background tasks as a singleton, so that there is only ever one instance running. However, this eliminates the reliability and performance benefits that a multiple-instance configuration can provide. This is especially true if the UI can supply sufficient work to keep more than one background task busy. + +It is vital to ensure that the background task can automatically restart and that it has sufficient capacity to cope with peaks in demand. You can achieve this by allocating a compute instance with sufficient resources, by implementing a queueing mechanism that can store requests for later execution when demand decreases, or by using a combination of these techniques. + +## Coordination +The background tasks might be complex and might require multiple individual tasks to execute to produce a result or to fulfil all the requirements. It is common in these scenarios to divide the task into smaller discreet steps or subtasks that can be executed by multiple consumers. Multistep jobs can be more efficient and more flexible because individual steps might be reusable in multiple jobs. It is also easy to add, remove, or modify the order of the steps. + +Coordinating multiple tasks and steps can be challenging, but there are three common patterns that you can use to guide your implementation of a solution: + +* **Decomposing a task into multiple reusable steps**. An application might be required to perform a variety of tasks of varying complexity on the information that it processes. A straightforward but inflexible approach to implementing this application might be to perform this processing as a monolithic module. However, this approach is likely to reduce the opportunities for refactoring the code, optimizing it, or reusing it if parts of the same processing are required elsewhere within the application. For more information, see [Pipes and Filters Pattern](http://msdn.microsoft.com/library/dn568100.aspx). +* **Managing execution of the steps for a task**. An application might perform tasks that comprise a number of steps (some of which might invoke remote services or access remote resources). The individual steps might be independent of each other, but they are orchestrated by the application logic that implements the task. For more information, see [Scheduler Agent Supervisor Pattern](http://msdn.microsoft.com/library/dn589780.aspx). +* **Managing recovery for task steps that fail**. An application might need to undo the work that is performed by a series of steps (which together define an eventually consistent operation) if one or more of the steps fail. For more information, see [Compensating Transaction Pattern](http://msdn.microsoft.com/library/dn589804.aspx). + +## Lifecycle (Cloud Services) + If you decide to implement background jobs for Cloud Services applications that use web and worker roles by using the **RoleEntryPoint** class, it is important to understand the lifecycle of this class in order to use it correctly. + +Web and worker roles go through a set of distinct phases as they start, run, and stop. The **RoleEntryPoint** class exposes a series of events that indicate when these stages are occurring. You use these to initialize, run, and stop your custom background tasks. The complete cycle is: + +* Azure loads the role assembly and searches it for a class that derives from **RoleEntryPoint**. +* If it finds this class, it calls **RoleEntryPoint.OnStart()**. You override this method to initialize your background tasks. +* After the **OnStart** method has completed, Azure calls **Application_Start()** in the application’s Global file if this is present (for example, Global.asax in a web role running ASP.NET). +* Azure calls **RoleEntryPoint.Run()** on a new foreground thread that executes in parallel with **OnStart()**. You override this method to start your background tasks. +* When the Run method ends, Azure first calls **Application_End()** in the application’s Global file if this is present, and then calls **RoleEntryPoint.OnStop()**. You override the **OnStop** method to stop your background tasks, clean up resources, dispose of objects, and close connections that the tasks may have used. +* The Azure worker role host process is stopped. At this point, the role will be recycled and will restart. + +For more details and an example of using the methods of the **RoleEntryPoint** class, see [Compute Resource Consolidation Pattern](http://msdn.microsoft.com/library/dn589778.aspx). + +## Considerations +Consider the following points when you are planning how you will run background tasks in a web or worker role: + +* The default **Run** method implementation in the **RoleEntryPoint** class contains a call to **Thread.Sleep(Timeout.Infinite)** that keeps the role alive indefinitely. If you override the **Run** method (which is typically necessary to execute background tasks), you must not allow your code to exit from the method unless you want to recycle the role instance. +* A typical implementation of the **Run** method includes code to start each of the background tasks and a loop construct that periodically checks the state of all the background tasks. It can restart any that fail or monitor for cancellation tokens that indicate that jobs have completed. +* If a background task throws an unhandled exception, that task should be recycled while allowing any other background tasks in the role to continue running. However, if the exception is caused by corruption of objects outside the task, such as shared storage, the exception should be handled by your **RoleEntryPoint** class, all tasks should be cancelled, and the **Run** method should be allowed to end. Azure will then restart the role. +* Use the **OnStop** method to pause or kill background tasks and clean up resources. This might involve stopping long-running or multistep tasks. It is vital to consider how this can be done to avoid data inconsistencies. If a role instance stops for any reason other than a user-initiated shutdown, the code running in the **OnStop** method must be completed within five minutes before it is forcibly terminated. Ensure that your code can be completed in that time or can tolerate not running to completion. +* The Azure load balancer starts directing traffic to the role instance when the **RoleEntryPoint.OnStart** method returns the value **true**. Therefore, consider putting all your initialization code in the **OnStart** method so that role instances that do not successfully initialize will not receive any traffic. +* You can use startup tasks in addition to the methods of the **RoleEntryPoint** class. You should use startup tasks to initialize any settings that you need to change in the Azure load balancer because these tasks will execute before the role receives any requests. For more information, see [Run startup tasks in Azure](/azure/cloud-services/cloud-services-startup-tasks/). +* If there is an error in a startup task, it might force the role to continually restart. This can prevent you from performing a virtual IP (VIP) address swap back to a previously staged version because the swap requires exclusive access to the role. This cannot be obtained while the role is restarting. To resolve this: + + * Add the following code to the beginning of the **OnStart** and **Run** methods in your role: + + ```C# + var freeze = CloudConfigurationManager.GetSetting("Freeze"); + if (freeze != null) + { + if (Boolean.Parse(freeze)) + { + Thread.Sleep(System.Threading.Timeout.Infinite); + } + } + ``` + + * Add the definition of the **Freeze** setting as a Boolean value to the ServiceDefinition.csdef and ServiceConfiguration.*.cscfg files for the role and set it to **false**. If the role goes into a repeated restart mode, you can change the setting to **true** to freeze role execution and allow it to be swapped with a previous version. + +## Resiliency considerations +Background tasks must be resilient in order to provide reliable services to the application. When you are planning and designing background tasks, consider the following points: + +* Background tasks must be able to gracefully handle role or service restarts without corrupting data or introducing inconsistency into the application. For long-running or multistep tasks, consider using *check pointing* by saving the state of jobs in persistent storage, or as messages in a queue if this is appropriate. For example, you can persist state information in a message in a queue and incrementally update this state information with the task progress so that the task can be processed from the last known good checkpoint--instead of restarting from the beginning. When using Azure Service Bus queues, you can use message sessions to enable the same scenario. Sessions allow you to save and retrieve the application processing state by using the [SetState](http://msdn.microsoft.com/library/microsoft.servicebus.messaging.messagesession.setstate.aspx) and [GetState](http://msdn.microsoft.com/library/microsoft.servicebus.messaging.messagesession.getstate.aspx) methods. For more information about designing reliable multistep processes and workflows, see [Scheduler Agent Supervisor Pattern](http://msdn.microsoft.com/library/dn589780.aspx). +* When you use web or worker roles to host multiple background tasks, design your override of the **Run** method to monitor for failed or stalled tasks, and restart them. Where this is not practical, and you are using a worker role, force the worker role to restart by exiting from the **Run** method. +* When you use queues to communicate with background tasks, the queues can act as a buffer to store requests that are sent to the tasks while the application is under higher than usual load. This allows the tasks to catch up with the UI during less busy periods. It also means that recycling the role will not block the UI. For more information, see [Queue-Based Load Leveling Pattern](http://msdn.microsoft.com/library/dn589783.aspx). If some tasks are more important than others, consider implementing the [Priority Queue Pattern](http://msdn.microsoft.com/library/dn589794.aspx) to ensure that these tasks run before less important ones. +* Background tasks that are initiated by messages or process messages must be designed to handle inconsistencies, such as messages arriving out of order, messages that repeatedly cause an error (often referred to as *poison messages*), and messages that are delivered more than once. Consider the following: + * Messages that must be processed in a specific order, such as those that change data based on the existing data value (for example, adding a value to an existing value), might not arrive in the original order in which they were sent. Alternatively, they might be handled by different instances of a background task in a different order due to varying loads on each instance. Messages that must be processed in a specific order should include a sequence number, key, or some other indicator that background tasks can use to ensure that they are processed in the correct order. If you are using Azure Service Bus, you can use message sessions to guarantee the order of delivery. However, it is usually more efficient, where possible, to design the process so that the message order is not important. + * Typically, a background task will peek at messages in the queue, which temporarily hides them from other message consumers. Then it deletes the messages after they have been successfully processed. If a background task fails when processing a message, that message will reappear on the queue after the peek time-out expires. It will be processed by another instance of the task or during the next processing cycle of this instance. If the message consistently causes an error in the consumer, it will block the task, the queue, and eventually the application itself when the queue becomes full. Therefore, it is vital to detect and remove poison messages from the queue. If you are using Azure Service Bus, messages that cause an error can be moved automatically or manually to an associated dead letter queue. + * Queues are guaranteed at *least once* delivery mechanisms, but they might deliver the same message more than once. In addition, if a background task fails after processing a message but before deleting it from the queue, the message will become available for processing again. Background tasks should be idempotent, which means that processing the same message more than once does not cause an error or inconsistency in the application’s data. Some operations are naturally idempotent, such as setting a stored value to a specific new value. However, operations such as adding a value to an existing stored value without checking that the stored value is still the same as when the message was originally sent will cause inconsistencies. Azure Service Bus queues can be configured to automatically remove duplicated messages. + * Some messaging systems, such as Azure storage queues and Azure Service Bus queues, support a de-queue count property that indicates the number of times a message has been read from the queue. This can be useful in handling repeated and poison messages. For more information, see [Asynchronous Messaging Primer](http://msdn.microsoft.com/library/dn589781.aspx) and [Idempotency Patterns](http://blog.jonathanoliver.com/2010/04/idempotency-patterns/). + +## Scaling and performance considerations +Background tasks must offer sufficient performance to ensure they do not block the application, or cause inconsistencies due to delayed operation when the system is under load. Typically, performance is improved by scaling the compute instances that host the background tasks. When you are planning and designing background tasks, consider the following points around scalability and performance: + +* Azure supports autoscaling (both scaling out and scaling back in) based on current demand and load--or on a predefined schedule, for Web Apps, Cloud Services web and worker roles, and Virtual Machines hosted deployments. Use this feature to ensure that the application as a whole has sufficient performance capabilities while minimizing runtime costs. +* Where background tasks have a different performance capability from the other parts of a Cloud Services application (for example, the UI or components such as the data access layer), hosting the background tasks together in a separate worker role allows the UI and background task roles to scale independently to manage the load. If multiple background tasks have significantly different performance capabilities from each other, consider dividing them into separate worker roles and scaling each role type independently. However, note that this might increase runtime costs compared to combining all the tasks into fewer roles. +* Simply scaling the roles might not be sufficient to prevent loss of performance under load. You might also need to scale storage queues and other resources to prevent a single point of the overall processing chain from becoming a bottleneck. Also, consider other limitations, such as the maximum throughput of storage and other services that the application and the background tasks rely on. +* Background tasks must be designed for scaling. For example, they must be able to dynamically detect the number of storage queues in use in order to listen on or send messages to the appropriate queue. +* By default, WebJobs scale with their associated Azure Web Apps instance. However, if you want a WebJob to run as only a single instance, you can create a Settings.job file that contains the JSON data **{ "is_singleton": true }**. This forces Azure to only run one instance of the WebJob, even if there are multiple instances of the associated web app. This can be a useful technique for scheduled jobs that must run as only a single instance. + +## Related patterns +* [Asynchronous Messaging Primer](http://msdn.microsoft.com/library/dn589781.aspx) +* [Autoscaling Guidance](http://msdn.microsoft.com/library/dn589774.aspx) +* [Compensating Transaction Pattern](http://msdn.microsoft.com/library/dn589804.aspx) +* [Competing Consumers Pattern](http://msdn.microsoft.com/library/dn568101.aspx) +* [Compute Partitioning Guidance](http://msdn.microsoft.com/library/dn589773.aspx) +* [Compute Resource Consolidation Pattern](http://msdn.microsoft.com/library/dn589778.aspx) +* [Gatekeeper Pattern](http://msdn.microsoft.com/library/dn589793.aspx) +* [Leader Election Pattern](http://msdn.microsoft.com/library/dn568104.aspx) +* [Pipes and Filters Pattern](http://msdn.microsoft.com/library/dn568100.aspx) +* [Priority Queue Pattern](http://msdn.microsoft.com/library/dn589794.aspx) +* [Queue-based Load Leveling Pattern](http://msdn.microsoft.com/library/dn589783.aspx) +* [Scheduler Agent Supervisor Pattern](http://msdn.microsoft.com/library/dn589780.aspx) + +## More information +* [Scaling Azure Applications with Worker Roles](http://msdn.microsoft.com/library/hh534484.aspx#sec8) +* [Executing Background Tasks](http://msdn.microsoft.com/library/ff803365.aspx) +* [Azure Role Startup Life Cycle](http://blog.syntaxc4.net/post/2011/04/13/windows-azure-role-startup-life-cycle.aspx) (blog post) +* [Azure Cloud Services Role Lifecycle](http://channel9.msdn.com/Series/Windows-Azure-Cloud-Services-Tutorials/Windows-Azure-Cloud-Services-Role-Lifecycle) (video) +* [Get Started with the Azure WebJobs SDK](/azure/app-service-web/websites-dotnet-webjobs-sdk-get-started/) +* [Azure Queues and Service Bus Queues - Compared and Contrasted](/azure/service-bus-messaging/service-bus-azure-and-service-bus-queues-compared-contrasted/) +* [How to Enable Diagnostics in a Cloud Service](/azure/cloud-services/cloud-services-dotnet-diagnostics/) + diff --git a/docs/best-practices/caching.md b/docs/best-practices/caching.md new file mode 100644 index 00000000000..2337e7bb92b --- /dev/null +++ b/docs/best-practices/caching.md @@ -0,0 +1,1242 @@ +--- +title: Caching guidance +description: Guidance on caching to improve performance and scalability. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: c86e2d49-066b-43b0-b0b6-f70ff4f87cdd +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/14/2016 +ms.author: masashin + +--- +# Caching +[!INCLUDE [header](../_includes/header.md)] + +Caching is a common technique that aims to improve the performance and +scalability of a system. It does this by temporarily copying frequently accessed data +to fast storage that's located close to the application. If this fast data storage +is located closer to the application than the original source, then caching +can significantly improve response times for client applications by serving +data more quickly. + +Caching is most effective when a client instance repeatedly +reads the same data, especially if all the following conditions apply to the original data store: + +* It remains relatively static. +* It's slow compared to the speed of the cache. +* It's subject to a high level of contention. +* It's far away when network latency can cause access to be slow. + +## Caching in distributed applications +Distributed applications typically implement either or both of the +following strategies when caching data: + +* Using a private cache, where data is held locally on the computer that's running an instance of an application or service. +* Using a shared cache, serving as a common source which can be accessed by multiple processes and/or machines. + +In both cases, caching can be performed client-side and/or server-side. Client-side caching is done by the process that provides +the user interface for a system, such as a web browser or desktop application. +Server-side caching is done by the process that provides the business services +that are running remotely. + +### Private caching +The most basic type of cache is an in-memory store. It's held in the address +space of a single process and accessed directly by the code that runs +in that process. This type of cache is very quick to access. It can +also provide an extremely effective means for storing modest amounts of +static data, since the size of a cache is typically constrained by the +volume of memory that's available on the machine hosting the process. + +If you need to cache more information than is physically possible in memory, +you can write cached data to the local file system. This will +be slower to access than data that's held in-memory, but should +still be faster and more reliable than retrieving data across a network. + +If you have multiple instances of an application that uses this model +running concurrently, each application instance has its own +independent cache holding its own copy of the data. + +Think of a cache as a snapshot of the original data at some +point in the past. If this data is not static, it is likely that +different application instances hold different versions of the +data in their caches. Therefore, the same query performed by these +instances can return different results, as shown in Figure 1. + +![Using an in-memory cache in different instances of an application](./images/caching/Figure1.png) + +*Figure 1: Using an in-memory cache in different instances of an application* + +### Shared caching +Using a shared cache can help alleviate concerns that data might +differ in each cache, which can occur with in-memory caching. Shared +caching ensures that different application instances see the same +view of cached data. It does this by locating the cache in a separate location, +typically hosted as part of a separate service, as shown in Figure 2. + +![Using a shared cache](./images/caching/Figure2.png) + +*Figure 2: Using a shared cache* + +An important benefit of the shared caching approach is the +scalability it provides. Many shared cache services are +implemented by using a cluster of servers, and utilize software that +distributes the data across the cluster in a transparent manner. An +application instance simply sends a request to the cache service. +The underlying infrastructure is responsible for determining the +location of the cached data in the cluster. You can easily scale the +cache by adding more servers. + +There are two main disadvantages of the shared caching approach: + +* The cache is slower to access because it is no longer held locally to each + application instance. +* The requirement to implement a separate + cache service might add complexity to the solution. + +## Considerations for using caching +The following sections describe in more detail the considerations +for designing and using a cache. + +### Decide when to cache data +Caching can dramatically improve performance, scalability, and availability. The more data +that you have and the larger the number of users that need to access this data, the greater +the benefits of caching become. That's because caching reduces the latency and contention that's associated with handling +large volumes of concurrent requests in the original data store. + +For example, a database +might support a limited number of concurrent connections. Retrieving data from a shared +cache, however, rather than the underlying database, makes it possible for a client application to access this data +even if the number of available connections is currently exhausted. Additionally, if the +database becomes unavailable, client applications might be able to continue by using the +data that's held in the cache. + +Consider caching data that is read frequently but modified infrequently +(for example, data that has a higher proportion of read operations than write operations). However, +we don't recommend that you use the cache as the authoritative store of critical information. Instead, +ensure that all changes that your application cannot afford to lose are always saved to a +persistent data store. This means that if the cache is unavailable, your application can +still continue to operate by using the data store, and you won't lose important +information. + +### Determine how to cache data effectively +The key to using a cache effectively lies in determining the most appropriate data to +cache, and caching it at the appropriate time. The data can be added to the cache on +demand the first time it is retrieved by an application. This means that the application needs to +fetch the data only once from the data store, and that subsequent access can be satisfied +by using the cache. + +Alternatively, a cache can be partially or fully populated with data in advance, +typically when the application starts (an approach known as seeding). However, it might +not be advisable to implement seeding for a large cache because this approach can impose +a sudden, high load on the original data store when the application starts running. + +Often an analysis of usage patterns can help you decide whether to fully or partially +prepopulate a cache, and to choose the data to cache. For example, it +can be useful to seed the cache with the static user profile data for +customers who use the application regularly (perhaps every day), but not for +customers who use the application only once a week. + +Caching typically works well with data that is immutable or that changes +infrequently. Examples include reference information such as product and pricing +information in an e-commerce application, or shared static resources that are costly +to construct. Some or all of this data can be loaded into the cache at application +startup to minimize demand on resources and to improve performance. It might also be +appropriate to have a background process that periodically updates reference data +in the cache to ensure it is up to date, or that refreshes the cache when reference +data changes. + +Caching is less useful for dynamic data, although there are some exceptions to +this consideration (see the section Cache highly dynamic data later in this +article for more information). When the original data changes regularly, either +the cached information becomes stale very quickly or the overhead of synchronizing the cache with the original data store reduces the effectiveness of +caching. + +Note that a cache does not have to include the complete data for an +entity. For example, if a data item represents a multivalued object such as a bank +customer with a name, address, and account balance, some of these elements might +remain static (such as the name and address), while others (such as the account balance) +might be more dynamic. In these situations, it can be useful to cache the static +portions of the data and retrieve (or calculate) only the remaining information when it is required. + +We recommend that you carry out performance testing and usage analysis to determine whether +pre-population or on-demand loading of the cache, or a combination of both, is +appropriate. The decision should be based on the volatility and +usage pattern of the data. Cache utilization and performance analysis is +particularly important in applications that encounter heavy loads and must be +highly scalable. For example, in highly scalable scenarios it might make sense to +seed the cache to reduce the load on the data store at peak times. + +Caching can also be used to avoid repeating computations while the application is +running. If an operation transforms data or performs a complicated calculation, +it can save the results of the operation in the cache. If the same calculation +is required afterward, the application can simply retrieve the results from +the cache. + +An application can modify data that's held in a cache. However, we recommend thinking of the +cache as a transient data store that could disappear at any time. Do not store +valuable data in the cache only; make sure that you maintain the information +in the original data store as well. This means that if the cache becomes +unavailable, you minimize the chance of losing data. + +### Cache highly dynamic data +When you store rapidly-changing information in a persistent data store, it can impose +an overhead on the system. For example, consider a device that continually reports +status or some other measurement. If an application chooses not to cache this +data on the basis that the cached information will nearly always be outdated, then +the same consideration could be true when storing and retrieving this information +from the data store. In the time it takes to save and fetch this data, it might have +changed. + +In a situation such as this, consider the benefits of storing the dynamic +information directly in the cache instead of in the persistent data store. If the +data is non-critical and does not require auditing, then it doesn't matter +if the occasional change is lost. + +### Manage data expiration in a cache +In most cases, data that's held in a cache is a copy of data that's held in the original data +store. The data in the original data store might change after it was cached, causing +the cached data to become stale. Many caching systems enable you to configure the +cache to expire data and reduce the period for which data may be out of date. + +When cached data expires, it's removed from the cache, and the application must +retrieve the data from the original data store (it can put the newly-fetched +information back into cache). You can set a default expiration policy when you +configure the cache. In many cache services, you can also stipulate the expiration +period for individual objects when you store them programmatically in the cache. +Some caches enable you to specify the expiration period as an absolute value, or +as a sliding value that causes the item to be removed from the cache if it is not +accessed within the specified time. This setting overrides any cache-wide +expiration policy, but only for the specified objects. + +> [!NOTE] +> Consider the expiration period for the cache and the objects that it contains carefully. If you make it too short, objects will expire too quickly and you will reduce the benefits of using the cache. If you make the period too long, you risk the data becoming stale. +> +> + +It's also possible that the cache might fill up if data is allowed to remain +resident for a long time. In this case, any requests to add new items to the +cache might cause some items to be forcibly removed in a process known as +eviction. Cache services typically evict data on a least-recently-used (LRU) +basis, but you can usually override this policy and prevent items from being +evicted. However, if you adopt this approach, you risk exceeding the +memory that's available in the cache. An application that attempts to add an item +to the cache will fail with an exception. + +Some caching implementations might provide additional eviction policies. There are several types of eviction policies. These include: + +* A most-recently-used policy (in the expectation that the + data will not be required again). +* A first-in-first-out policy (oldest data is + evicted first). +* An explicit removal policy based on a triggered event (such as the + data being modified). + +### Invalidate data in a client-side cache +Data that's held in a client-side cache is generally considered to be outside +the auspices of the service that provides the data to the client. A service +cannot directly force a client to add or remove information from a +client-side cache. + +This means that it's possible for a client that uses +a poorly configured cache to continue using outdated information. For example, if the expiration policies of the cache aren't +properly implemented, a client might use outdated information that's cached +locally when the information in the original data source has changed. + +If you are building a web application that serves data over an HTTP +connection, you can implicitly force a web client (such as a browser or +web proxy) to fetch the most recent information. You can do this if a resource is updated by a change in the URI of that resource. Web clients typically use the URI +of a resource as the key in the client-side cache, so if the URI changes, +the web client ignores any previously cached versions of a +resource and fetches the new version instead. + +## Managing concurrency in a cache +Caches are often designed to be shared by multiple instances of an +application. Each application instance can read and modify data in +the cache. Consequently, the same concurrency issues that arise with +any shared data store also apply to a cache. In a situation +where an application needs to modify data that's held in the cache, you might +need to ensure that updates made by one instance of the application +do not overwrite the changes made by another instance. + +Depending on the nature of the data and the likelihood of collisions, +you can adopt one of two approaches to concurrency: + +* **Optimistic.** Immediately prior to updating the data, the application checks to see whether the data in the cache has changed since it was retrieved. If the data is still the same, the change can be made. Otherwise, the application has to decide whether to update it. (The business logic that drives this decision will be application-specific.) This approach is suitable for situations where updates are infrequent, or where collisions are unlikely to occur. +* **Pessimistic.** When it retrieves the data, the application locks it in the cache to prevent another instance from changing it. This process ensures that collisions cannot occur, but they can also block other instances that need to process the same data. Pessimistic concurrency can affect the scalability of a solution and is recommended only for short-lived operations. This approach might be appropriate for situations where collisions are more likely, especially if an application updates multiple items in the cache and must ensure that these changes are applied consistently. + +### Implement high availability and scalability, and improve performance +Avoid using a cache as the primary repository of data; this is the role +of the original data store from which the cache is populated. The +original data store is responsible for ensuring the persistence of the +data. + +Be careful not to introduce critical dependencies on the availability +of a shared cache service into your solutions. An application should be +able to continue functioning if the service that provides the shared cache +is unavailable. The application should not hang or fail while waiting +for the cache service to resume. + +Therefore, the application must be +prepared to detect the availability of the cache service and fall back +to the original data store if the cache is inaccessible. The +[Circuit-Breaker pattern](http://msdn.microsoft.com/library/dn589784.aspx) is useful for handling this scenario. The +service that provides the cache can be recovered, and once it becomes +available, the cache can be repopulated as data is read form the +original data store, following a strategy such as the [Cache-aside pattern](http://msdn.microsoft.com/library/dn589799.aspx). + +However, there might be a scalability impact on the system if the application falls back to the original data store when the cache is +temporarily unavailable. +While the data store is being recovered, the original data store +could be swamped with requests for data, resulting in timeouts and +failed connections. + +Consider +implementing a local, private cache in each instance of an application, +together with the shared cache that all application instances +access. When the application retrieves an item, it can check first +in its local cache, then in the shared cache, and finally in the original +data store. The local cache can be populated using the data in either the +shared cache, or in the database if the shared cache is unavailable. + +This approach requires careful configuration to prevent the local +cache from becoming too stale with respect to the shared cache. However, the local cache acts as a buffer if the shared cache is unreachable. Figure 3 +shows this structure. + +![Using a local, private cache with a shared cache](./images/caching/Caching3.png) +*Figure 3: Using a local, private cache with a shared cache* + +To support large caches that hold relatively long-lived data, some +cache services provide a high-availability option that implements +automatic failover if the cache becomes unavailable. This approach +typically involves replicating the cached data that's stored on a primary +cache server to a secondary cache server, and switching to the +secondary server if the primary server fails or connectivity is +lost. + +To reduce the latency that's associated with writing to multiple +destinations, the replication to the secondary server might occur +asynchronously when data is written to the cache on the primary +server. This approach leads to the possibility that some +cached information might be lost in the event of a failure, but the +proportion of this data should be small compared to the overall +size of the cache. + +If a shared cache is large, it might be beneficial to partition the +cached data across nodes to reduce the chances of contention and +improve scalability. Many shared caches support the ability to +dynamically add (and remove) nodes and rebalance the data across +partitions. This approach might involve clustering, in which the +collection of nodes is presented to client applications as a +seamless, single cache. Internally, however, the data is dispersed +between nodes following a predefined distribution strategy +that balances the load evenly. The [Data partitioning guidance document](http://msdn.microsoft.com/library/dn589795.aspx) +on the Microsoft website provides more information about possible +partitioning strategies. + +Clustering can also increase the availability of the cache. If a +node fails, the remainder of the cache is still accessible. +Clustering is frequently used in conjunction with replication +and failover. Each node can be replicated, and the replica can be +quickly brought online if the node fails. + +Many read and write operations are likely to involve single data +values or objects. However, at times it might be +necessary to store or retrieve large volumes of data quickly. +For example, seeding a cache could involve writing hundreds or +thousands of items to the cache. An application might also need to +retrieve a large number of related items from the cache as +part of the same request. + +Many large-scale caches provide batch +operations for these purposes. This enables a client application to +package up a large volume of items into a single request and +reduces the overhead that's associated with performing a large number +of small requests. + +## Caching and eventual consistency +For the cache-aside pattern to work, the instance of the application +that populates the cache must have access to the most recent and +consistent version of the data. In a system that implements +eventual consistency (such as a replicated data store) this might +not be the case. + +One instance of an application could modify a +data item and invalidate the cached version of that item. Another +instance of the application might attempt to read this item from +a cache, which causes a cache-miss, so it reads the data from the +data store and adds it to the cache. However, if the data store +has not been fully synchronized with the other replicas, the +application instance could read and populate the cache with the +old value. + +For more information about handling data consistency, see the +[Data consistency primer](http://msdn.microsoft.com/library/dn589800.aspx) page on the Microsoft website. + +### Protect cached data +Irrespective of the cache service you use, consider +how to protect the data that's held in the cache from unauthorized +access. There are two main concerns: + +* The privacy of the data in the cache +* The privacy of data as it flows between the cache and the + application that's using the cache + +To protect data in the cache, the cache service might implement +an authentication mechanism that requires that applications specify the following: + +* Which identities can access data in the cache. +* Which operations (read and write) that these identities are + allowed to perform. + +To reduce overhead that's associated with +reading and writing data, after an identity has been granted +write and/or read access to the cache, that identity can use +any data in the cache. + +If you need to restrict access to +subsets of the cached data, you can do one of the following: + +* Split the cache into partitions (by using different cache + servers) and only grant access to identities for the + partitions that they should be allowed to use. +* Encrypt the data in each subset by using different keys, + and provide the encryption keys only to identities that + should have access to each subset. A client application + might still be able to retrieve all of the data in the cache, + but it will only be able to decrypt the data for which it + has the keys. + +You must also protect the data as it flows in and out of the cache. To do this, +you depend on the security features provided by the network +infrastructure that client applications use to connect to the +cache. If the cache is implemented using an on-site server +within the same organization that hosts the client applications, +then the isolation of the network itself might not require you to +take additional steps. If the cache is located remotely and +requires a TCP or HTTP connection over a public network (such +as the Internet), consider implementing SSL. + +## Considerations for implementing caching with Microsoft Azure +Azure provides the Azure Redis Cache. This is an implementation +of the open source Redis cache that runs as a service in an +Azure datacenter. It provides a caching service that can be +accessed from any Azure application, whether the application +is implemented as a cloud service, a website, or inside an +Azure virtual machine. Caches can be shared by client +applications that have the appropriate access key. + +Azure Redis Cache is a high-performance caching solution that provides +availability, scalability and security. It typically runs +as a service spread across one or more dedicated machines. It +attempts to store as much information as it can in memory to +ensure fast access. This architecture is intended to provide +low latency and high throughput by reducing the need to +perform slow I/O operations. + + Azure Redis Cache is compatible with many of the various +APIs that are used by client applications. If you have existing +applications that already use Azure Redis Cache running on-premises, the +Azure Redis Cache provides a quick migration path to caching +in the cloud. + +> [!NOTE] +> Azure also provides the Managed Cache Service. This +> service is based on the Azure Service Fabric Cache engine. It +> enables you to create a distributed cache that can be shared +> by loosely-coupled applications. The cache is hosted on +> high-performance servers running in an Azure datacenter. +> However, this option is no longer recommended and is only +> provided to support existing applications that have been built +> to use it. For all new development, use Azure Redis +> Cache instead. +> +> Additionally, Azure supports in-role caching. This feature +> enables you to create a cache that's specific to a cloud service. +> The cache is hosted by instances of a web or worker role, and +> can only be accessed by roles that are operating as part of the same +> cloud service deployment unit. (A deployment unit is the set +> of role instances that are deployed as a cloud service to a specific +> region.) The cache is clustered, and all instances of the +> role within the same deployment unit that hosts the cache +> become part of the same cache cluster. However, this option is +> no longer recommended and is only provided to support existing +> applications that have been built to use it. For all new +> development, use Azure Redis Cache instead. +> +> Both Azure Managed Cache Service and Azure In-Role Cache +> are currently slated for retirement on November 16th, 2016. +> It is recommended that you migrate to Azure Redis Cache in +> preparation for this retirement. For more information, see +> [What is Azure Redis Cache offering and what size should I use?](/azure/redis-cache/cache-faq#what-redis-cache-offering-and-size-should-i-use)e. +> +> + +### Features of Redis + Redis is more than a simple cache server. It provides a distributed in-memory +database with an extensive command set that supports many common scenarios. These +are described later in this document, in the section Using Redis caching. This section summarizes some of the key features that Redis +provides. + +### Redis as an in-memory database +Redis supports both read and write operations. In Redis, writes can be protected from system failure either by being stored periodically in a local snapshot file or in an append-only log file. This is not the case in many caches (which should be considered transitory data stores). + + All writes are asynchronous and do not block clients from reading and writing data. When Redis starts running, it reads the data from the snapshot or log file and uses it to construct the in-memory cache. For more information, see [Redis persistence](http://redis.io/topics/persistence) on the Redis website. + +> [!NOTE] +> Redis does not guarantee that all writes will be saved in the event +> of a catastrophic failure, but at worst you might lose only a few seconds +> worth of data. Remember that a cache is not intended to act as an +> authoritative data source, and it is the responsibility of the applications +> using the cache to ensure that critical data is saved successfully to an +> appropriate data store. For more information, see the [cache-aside pattern](http://msdn.microsoft.com/library/dn589799.aspx). +> +> + +#### Redis data types +Redis is a key-value store, where values can contain simple types or complex data structures such as hashes, lists, and sets. It supports a set of atomic operations on these data types. Keys can be permanent or tagged with a limited time-to-live, at which point the key and its corresponding value are automatically removed from the cache. For more information about Redis keys and values, visit the page [An introduction to Redis data types and abstractions](http://redis.io/topics/data-types-intro) on the Redis website. + +#### Redis replication and clustering +Redis supports master/subordinate replication to help ensure availability and maintain throughput. Write operations to a Redis master node are replicated to one or more subordinate nodes. Read operations can be served by the master or any of the subordinates. + +In the event of a network partition, subordinates can continue to serve data and then transparently resynchronize with the master when the connection is reestablished. For further details, visit the [Replication](http://redis.io/topics/replication) page on the Redis website. + +Redis also provides clustering, which enables you to transparently partition data into shards across servers and spread the load. This feature improves scalability, because new Redis servers can be added and the data repartitioned as the size of the cache increases. + +Furthermore, each server in the cluster can be replicated by using master/subordinate replication. This ensures availability across each node in the cluster. For more information about clustering and sharding, visit the [Redis cluster tutorial page](http://redis.io/topics/cluster-tutorial) on the Redis website. + +### Redis memory use +A Redis cache has a finite size that depends on the resources available on the host computer. When you configure a Redis server, you can specify the maximum amount of memory it can use. You can also configure a key in a Redis cache to have an expiration time, after which it is automatically removed from the cache. This feature can help prevent the in-memory cache from filling with old or stale data. + +As memory fills up, Redis can automatically evict keys and their values by following a number of policies. The default is LRU (least recently used), but you can also select other policies such as evicting keys at random or turning off eviction altogether (in which, case attempts to add items to the cache fail if it is full). The page [Using Redis as an LRU cache](http://redis.io/topics/lru-cache) provides more information. + +### Redis transactions and batches +Redis enables a client application to submit a series of operations that read and write data in the cache as an atomic transaction. All the commands in the transaction are guaranteed to run sequentially, and no commands issued by other concurrent clients will be interwoven between them. + +However, these are not true transactions as a relational database would perform them. Transaction processing consists of two stages--the first is when the commands are queued, and the second is when the commands are run. During the command queuing stage, the commands that comprise the transaction are submitted by the client. If some sort of error occurs at this point (such as a syntax error, or the wrong number of parameters) then Redis refuses to process the entire transaction and discards it. + +During the run phase, Redis performs each queued command in sequence. If a command fails during this phase, Redis continues with the next queued command and does not roll back the effects of any commands that have already been run. This simplified form of transaction helps to maintain performance and avoid performance problems that are caused by contention. + +Redis does implement a form of optimistic locking to assist in maintaining consistency. For detailed information about transactions and locking with Redis, visit the [Transactions page](http://redis.io/topics/transactions) on the Redis website. + +Redis also supports non-transactional batching of requests. The Redis protocol that clients use to send commands to a Redis server enables a client to send a series of operations as part of the same request. This can help to reduce packet fragmentation on the network. When the batch is processed, each command is performed. If any of these commands are malformed, they will be rejected (which doesn't happen with a transaction), but the remaining commands will be performed. There is also no guarantee about the order in which the commands in the batch will be processed. + +### Redis security +Redis is focused purely on providing fast access to data, and is designed to run inside a trusted environment that can be accessed only by trusted clients. Redis supports a limited security model based on password authentication. (It is possible to remove authentication completely, although we don't recommend this.) + +All authenticated clients share the same global password and have access to the same resources. If you need more comprehensive sign-in security, you must implement your own security layer in front of the Redis server, and all client requests should pass through this additional layer. Redis should not be directly exposed to untrusted or unauthenticated clients. + +You can restrict access to commands by disabling them or renaming them (and by providing only privileged clients with the new names). + +Redis does not directly support any form of data encryption, so all encoding must be performed by client applications. Additionally, Redis does not provide any form of transport security. If you need to protect data as it flows across the network, we recommend implementing an SSL proxy. + +For more information, visit the [Redis security](http://redis.io/topics/security) page on the Redis website. + +> [!NOTE] +> Azure Redis Cache provides its own security layer through which clients connect. The underlying Redis +> servers are not exposed to the public network. +> +> + +### Using the Azure Redis cache +The Azure Redis Cache provides access to Redis servers running on servers hosted at an Azure datacenter; it acts as a façade that provides access control and security. You can provision a cache by using the Azure Management portal. The portal provides a number of predefined configurations, ranging from a 53GB cache running as a dedicated service that supports SSL communications (for privacy) and master/subordinate replication with an SLA of 99.9% availability, down to a 250MB cache without replication (no availability guarantees) running on shared hardware. + +Using the Azure Management portal, you can also configure the eviction policy of the cache, and control access to the cache by adding users to the roles provided; Owner, Contributor, Reader. These roles define the operations that members can perform. For example, members of the Owner role have complete control over the cache (including security) and its contents, members of the Contributor role can read and write information in the cache, and members of the Reader role can only retrieve data from the cache. + +Most administrative tasks are performed through the Azure Management portal, and for this reason many of the administrative commands available in the standard version of Redis are not available, including the ability to modify the configuration programmatically, shutdown the Redis server, configure additional slaves, or forcibly save data to disk. + +The Azure management portal includes a convenient graphical display that enables you to monitor the performance of the cache. For example, you can view the number of connections being made, the number of requests performed, the volume of reads and writes, and the number of cache hits versus cache misses. Using this information, you can determine the effectiveness of the cache and if necessary switch to a different configuration or change the eviction policy. Additionally, you can create alerts that send email messages to an administrator if one or more critical metrics fall outside of an expected range. For example, if the number of cache misses exceeds a specified value in the last hour, an administrator could be alerted as the cache may be too small or data may be being evicted too quickly. + +You can also monitor CPU, memory, and network usage for the cache. + +For further information and examples showing how to create and configure an Azure Redis Cache, visit the page [Lap around Azure Redis Cache](https://azure.microsoft.com/blog/2014/06/04/lap-around-azure-redis-cache-preview/) on the Azure blog. + +## Caching session state and HTML output +If you building ASP.NET web applications that run by using Azure web roles, you can save session state information and HTML output in an Azure Redis Cache. The Session State Provider for Azure Redis Cache enables you to share session information between different instances of an ASP.NET web application, and is very useful in web farm situations where client-server affinity is not available and caching session data in-memory would not be appropriate. + +Using the Session State Provider with Azure Redis Cache delivers several benefits, including: + +* It can share session state amongst a large number of instances of an ASP.NET web application, providing improved scalability, +* It supports controlled, concurrent access to the same session state data for multiple readers and a single writer, and +* It can use compression to save memory and improve network performance. + +For more information visit the [ASP.NET Session State Provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-session-state-provider/) page on the Microsoft website. + +> [!NOTE] +> Do not use the Session State Provider for Azure Redis Cache for ASP.NET applications that run outside of the Azure environment. The latency of accessing the cache from outside of Azure can eliminate the performance benefits of caching data. +> +> + +Similarly, the Output Cache Provider for Azure Redis Cache enables you to save the HTTP responses generated by an ASP.NET web application. Using the Output Cache Provider with Azure Redis Cache can improve the response times of applications that render complex HTML output; application instances generating similar responses can make use of the shared output fragments in the cache rather than generating this HTML output afresh. For more information visit the [ASP.NET Output Cache Provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-output-cache-provider/) page on the Microsoft website. + +### Azure Redis cache +Azure Redis Cache provides access to Redis servers that are hosted at an Azure datacenter. It acts as a façade that provides access control and security. You can provision a cache by using the Azure portal. + +The portal provides a number of predefined configurations. These range from a 53 GB cache running as a dedicated service that supports SSL communications (for privacy) and master/subordinate replication with an SLA of 99.9% availability, down to a 25 0MB cache without replication (no availability guarantees) running on shared hardware. + +Using the Azure portal, you can also configure the eviction policy of the cache, and control access to the cache by adding users to the roles provided. These roles, which define the operations that members can perform, include Owner, Contributor, and Reader. For example, members of the Owner role have complete control over the cache (including security) and its contents, members of the Contributor role can read and write information in the cache, and members of the Reader role can only retrieve data from the cache. + +Most administrative tasks are performed through the Azure portal. For this reason, many of the administrative commands that are available in the standard version of Redis are not available, including the ability to modify the configuration programmatically, shut down the Redis server, configure additional subordinates, or forcibly save data to disk. + +The Azure portal includes a convenient graphical display that enables you to monitor the performance of the cache. For example, you can view the number of connections being made, the number of requests being performed, the volume of reads and writes, and the number of cache hits versus cache misses. Using this information, you can determine the effectiveness of the cache and if necessary, switch to a different configuration or change the eviction policy. + +Additionally, you can create alerts that send email messages to an administrator if one or more critical metrics fall outside of an expected range. For example, you might want to alert an administrator if the number of cache misses exceeds a specified value in the last hour, because it means the cache might be too small or data might be being evicted too quickly. + +You can also monitor the CPU, memory, and network usage for the cache. + +For further information and examples showing how to create and configure an Azure Redis Cache, visit the page [Lap around Azure Redis Cache](https://azure.microsoft.com/blog/2014/06/04/lap-around-azure-redis-cache-preview/) on the Azure blog. + +## Caching session state and HTML output +If you're building ASP.NET web applications that run by using Azure web roles, you can save session state information and HTML output in an Azure Redis Cache. The session state provider for Azure Redis Cache enables you to share session information between different instances of an ASP.NET web application, and is very useful in web farm situations where client-server affinity is not available and caching session data in-memory would not be appropriate. + +Using the session state provider with Azure Redis Cache delivers several benefits, including: + +* Sharing session state with a large number of instances of ASP.NET web applications. +* Providing improved scalability. +* Supporting controlled, concurrent access to the same session state data for multiple readers and a single writer. +* Using compression to save memory and improve network performance. + +For more information, visit the [ASP.NET session state provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-session-state-provider/) page on the Microsoft website. + +> [!NOTE] +> Do not use the session state provider for Azure Redis Cache with ASP.NET applications that run outside of the Azure environment. The latency of accessing the cache from outside of Azure can eliminate the performance benefits of caching data. +> +> + +Similarly, the output cache provider for Azure Redis Cache enables you to save the HTTP responses generated by an ASP.NET web application. Using the output cache provider with Azure Redis Cache can improve the response times of applications that render complex HTML output. Application instances that generate similar responses can make use of the shared output fragments in the cache rather than generating this HTML output afresh. For more information, visit the [ASP.NET output cache provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-output-cache-provider/) page on the Microsoft website. + +## Building a custom Redis cache +Azure Redis Cache acts as a façade to the underlying Redis servers. Currently it supports a fixed set of configurations but does not provide for Redis clustering. If you require an advanced configuration that is not covered by the Azure Redis cache (such as a cache bigger than 53 GB) you can build and host your own Redis servers by using Azure virtual machines. + +This is a potentially complex process because you might need to create several VMs to act as master and subordinate nodes if you want to implement replication. Furthermore, if you wish to create a cluster, then you need multiple masters and subordinate servers. A minimal clustered replication topology that provides a high degree of availability and scalability comprises at least six VMs organized as three pairs of master/subordinate servers (a cluster must contain at least three master nodes). + +Each master/subordinate pair should be located close together to minimize latency. However, each set of pairs can be running in different Azure datacenters located in different regions, if you wish to locate cached data close to the applications that are most likely to use it. The page [Running Redis on a CentOS Linux VM in Azure](http://blogs.msdn.com/b/tconte/archive/2012/06/08/running-redis-on-a-centos-linux-vm-in-windows-azure.aspx) on the Microsoft website walks through an example that shows how to build and configure a Redis node running as an Azure VM. + +[AZURE.NOTE] Please note that if you implement your own Redis cache in this way, you are responsible for monitoring, managing, and securing the service. + +## Partitioning a Redis cache +Partitioning the cache involves splitting the cache across multiple computers. This structure gives you several advantages over using a single cache server, including: + +* Creating a cache that is much bigger than can be stored on a single server. +* Distributing data across servers, improving availability. If one server fails or becomes inaccessible, the data that it holds is unavailable, but the data on the remaining servers can still be accessed. For a cache, this is not crucial because the cached data is only a transient copy of the data that's held in a database. Cached data on a server that becomes inaccessible can be cached on a different server instead. +* Spreading the load across servers, thereby improving performance and scalability. +* Geolocating data close to the users that access it, thus reducing latency. + +For a cache, the most common form of partitioning is sharding. In this strategy, each partition (or shard) is a Redis cache in its own right. Data is directed to a specific partition by using sharding logic, which can use a variety of approaches to distribute the data. The [Sharding pattern](http://msdn.microsoft.com/library/dn589797.aspx) provides more information about implementing sharding. + +To implement partitioning in a Redis cache, you can take one of the following approaches: + +* *Server-side query routing.* In this technique, a client application sends a request to any of the + Redis servers that comprise the cache (probably the closest server). Each Redis server stores + metadata that describes the partition that it holds, and also contains information about which + partitions are located on other servers. The Redis server examines the client request. If it + can be resolved locally, it will perform the requested operation. Otherwise it will forward the + request on to the appropriate server. This model is implemented by Redis clustering, and is + described in more detail on the [Redis cluster tutorial](http://redis.io/topics/cluster-tutorial) page on the Redis website. Redis clustering + is transparent to client applications, and additional Redis servers can be added to the cluster + (and the data re-partitioned) without requiring that you reconfigure the clients. +* *Client-side partitioning.* In this model, the client application contains logic (possibly in + the form of a library) that routes requests to the appropriate Redis server. This approach + can be used with Azure Redis Cache. Create multiple Azure Redis Caches (one for each data + partition) and implement the client-side logic that routes the requests to the correct + cache. If the partitioning scheme changes (if additional Azure Redis Caches are created, + for example), client applications might need to be reconfigured. +* *Proxy-assisted partitioning.* In this scheme, client applications send requests to an + intermediary proxy service which understands how the data is partitioned and then routes + the request to the appropriate Redis server. This approach can also be used with Azure + Redis Cache; the proxy service can be implemented as an Azure cloud service. This + approach requires an additional level of complexity to implement the service, and + requests might take longer to perform than using client-side partitioning. + +The page [Partitioning: how to split data among multiple Redis instances](http://redis.io/topics/partitioning) +on the Redis website provides further information about implementing partitioning with Redis. + +### Implement Redis cache client applications +Redis supports client applications written in numerous programming languages. If you are building new applications by using the .NET Framework, the recommended approach is to use the StackExchange.Redis client library. This library provides a .NET Framework object model that abstracts the details for connecting to a Redis server, sending commands, and receiving responses. It is available in Visual Studio as a NuGet package. You can use this same library to connect to an Azure Redis Cache, or a custom Redis cache hosted on a VM. + +To connect to a Redis server you use the static `Connect` method of the `ConnectionMultiplexer` class. The connection that this method creates is designed to be used throughout the lifetime of the client application, and the same connection can be used by multiple concurrent threads. Do not reconnect and disconnect each time you perform a Redis operation because this can degrade performance. + +You can specify the connection parameters, such as the address of the Redis host and the password. If you are using Azure Redis Cache, the password is either the primary or secondary key that is generated for Azure Redis Cache by using the Azure Management portal. + +After you have connected to the Redis server, you can obtain a handle on the Redis database that acts as the cache. The Redis connection provides the `GetDatabase` method to do this. You can then retrieve items from the cache and store data in the cache by using the `StringGet` and `StringSet` methods. These methods expect a key as a parameter, and return the item either in the cache that has a matching value (`StringGet`) or add the item to the cache with this key (`StringSet`). + +Depending on the location of the Redis server, many operations might incur some latency while a request is transmitted to the server and a response is returned to the client. The StackExchange library provides asynchronous versions of many of the methods that it exposes to help client applications remain responsive. These methods support the [Task-based Asynchronous Pattern](http://msdn.microsoft.com/library/hh873175.aspx) in the .NET Framework. + +The following code snippet shows a method named `RetrieveItem`. It illustrates an implementation of the cache-aside pattern based on Redis and the StackExchange library. The method takes a string key value and attempts to retrieve the corresponding item from the Redis cache by calling the `StringGetAsync` method (the asynchronous version of `StringGet`). + +If the item is not found, it is fetched from the underlying data source using the `GetItemFromDataSourceAsync` method (which is a local method and not part of the StackExchange library). It's then added to the cache by using the `StringSetAsync` method so it can be retrieved more quickly next time. + +```csharp +// Connect to the Azure Redis cache +ConfigurationOptions config = new ConfigurationOptions(); +config.EndPoints.Add(".redis.cache.windows.net"); +config.Password = ""; +ConnectionMultiplexer redisHostConnection = ConnectionMultiplexer.Connect(config); +IDatabase cache = redisHostConnection.GetDatabase(); +... +private async Task RetrieveItem(string itemKey) +{ + // Attempt to retrieve the item from the Redis cache + string itemValue = await cache.StringGetAsync(itemKey); + + // If the value returned is null, the item was not found in the cache + // So retrieve the item from the data source and add it to the cache + if (itemValue == null) + { + itemValue = await GetItemFromDataSourceAsync(itemKey); + await cache.StringSetAsync(itemKey, itemValue); + } + + // Return the item + return itemValue; +} +``` + +The `StringGet` and `StringSet` methods are not restricted to retrieving or storing string values. They can take any item that is serialized as an array of bytes. If you need to save a .NET object, you can serialize it as a byte stream and use the `StringSet` method to write it to the cache. + +Similarly, you can read an object from the cache by using the `StringGet` method and deserializing it as a .NET object. The following code shows a set of extension methods for the IDatabase interface (the `GetDatabase` method of a Redis connection returns an `IDatabase` object), and some sample code that uses these methods to read and write a `BlogPost` object to the cache: + +```csharp +public static class RedisCacheExtensions +{ + public static async Task GetAsync(this IDatabase cache, string key) + { + return Deserialize(await cache.StringGetAsync(key)); + } + + public static async Task GetAsync(this IDatabase cache, string key) + { + return Deserialize(await cache.StringGetAsync(key)); + } + + public static async Task SetAsync(this IDatabase cache, string key, object value) + { + await cache.StringSetAsync(key, Serialize(value)); + } + + static byte[] Serialize(object o) + { + byte[] objectDataAsStream = null; + + if (o != null) + { + BinaryFormatter binaryFormatter = new BinaryFormatter(); + using (MemoryStream memoryStream = new MemoryStream()) + { + binaryFormatter.Serialize(memoryStream, o); + objectDataAsStream = memoryStream.ToArray(); + } + } + + return objectDataAsStream; + } + + static T Deserialize(byte[] stream) + { + T result = default(T); + + if (stream != null) + { + BinaryFormatter binaryFormatter = new BinaryFormatter(); + using (MemoryStream memoryStream = new MemoryStream(stream)) + { + result = (T)binaryFormatter.Deserialize(memoryStream); + } + } + + return result; + } +} +``` + +The following code illustrates a method named `RetrieveBlogPost` that uses these extension methods to read and write a serializable `BlogPost` object to the cache following the cache-aside pattern: + +```csharp +// The BlogPost type +[Serializable] +private class BlogPost +{ + private HashSet tags = new HashSet(); + + public BlogPost(int id, string title, int score, IEnumerable tags) + { + this.Id = id; + this.Title = title; + this.Score = score; + this.tags = new HashSet(tags); + } + + public int Id { get; set; } + public string Title { get; set; } + public int Score { get; set; } + public ICollection Tags { get { return this.tags; } } +} +... +private async Task RetrieveBlogPost(string blogPostKey) +{ + BlogPost blogPost = await cache.GetAsync(blogPostKey); + if (blogPost == null) + { + blogPost = await GetBlogPostFromDataSourceAsync(blogPostKey); + await cache.SetAsync(blogPostKey, blogPost); + } + + return blogPost; +} +``` + +Redis supports command pipelining if a client application sends multiple asynchronous requests. Redis can multiplex the requests using the same connection rather than receiving and responding to commands in a strict sequence. + +This approach helps to reduce latency by making more efficient use of the network. The following code snippet shows an example that retrieves the details of two customers concurrently. The code submits two requests and then performs some other processing (not shown) before waiting to receive the results. The `Wait` method of the cache object is similar to the .NET Framework `Task.Wait` method: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +var task1 = cache.StringGetAsync("customer:1"); +var task2 = cache.StringGetAsync("customer:2"); +... +var customer1 = cache.Wait(task1); +var customer2 = cache.Wait(task2); +``` + +The page [Azure Redis Cache documentation](https://azure.microsoft.com/documentation/services/cache/) on the Microsoft website provides more information about how to write client applications that can use the Azure Redis Cache. Additional information is available on the [Basic usage page](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/Basics.md) on the StackExchange.Redis website. + +The page [Pipelines and multiplexers](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/PipelinesMultiplexers.md) on the same website provides more information about asynchronous operations and pipelining with Redis and the StackExchange library. The next section in this article, Using Redis Caching, provides examples of some of the more advanced techniques that you can apply to data that's held in a Redis cache. + +## Using Redis caching +The simplest use of Redis for caching concerns is key-value pairs where the value is an uninterpreted string of arbitrary length that can contain any binary data. (It is essentially an array of bytes that can be treated as a string). This scenario was illustrated in the section Implement Redis Cache client applications earlier in this article. + +Note that keys also contain uninterpreted data, so you can use any binary information as the key. The longer the key is, however, the more space it will take to store, and the longer it will take to perform lookup operations. For usability and ease of maintenance, design your keyspace carefully and use meaningful (but not verbose) keys. + +For example, use structured keys such as "customer:100" to represent the key for the customer with ID 100 rather than simply "100". This scheme enables you to easily distinguish between values that store different data types. For example, you could also use the key "orders:100" to represent the key for the order with ID 100. + +Apart from one-dimensional binary strings, a value in a Redis key-value pair can also hold more structured information, including lists, sets (sorted and unsorted), and hashes. Redis provides a comprehensive command set that can manipulate these types, and many of these commands are available to .NET Framework applications through a client library such as StackExchange. The page [An introduction to Redis data types and abstractions](http://redis.io/topics/data-types-intro) on the Redis website provides a more detailed overview of these types and the commands that you can use to manipulate them. + +This section summarizes some common use cases for these data types and commands. + +### Perform atomic and batch operations +Redis supports a series of atomic get-and-set operations on string values. These operations remove the possible race hazards that might occur when using separate `GET` and `SET` commands. The operations that are available include: + +* `INCR`, `INCRBY`, `DECR`, and `DECRBY`, which perform atomic increment and decrement operations on + integer numeric data values. The StackExchange library provides overloaded versions of the + `IDatabase.StringIncrementAsync` and `IDatabase.StringDecrementAsync` methods to perform + these operations and return the resulting value that is stored in the cache. The following code + snippet illustrates how to use these methods: + + ```csharp + ConnectionMultiplexer redisHostConnection = ...; + IDatabase cache = redisHostConnection.GetDatabase(); + ... + await cache.StringSetAsync("data:counter", 99); + ... + long oldValue = await cache.StringIncrementAsync("data:counter"); + // Increment by 1 (the default) + // oldValue should be 100 + + long newValue = await cache.StringDecrementAsync("data:counter", 50); + // Decrement by 50 + // newValue should be 50 + ``` +* `GETSET`, which retrieves the value that's associated with a key and changes it to a new value. The + StackExchange library makes this operation available through the `IDatabase.StringGetSetAsync` + method. The code snippet below shows an example of this method. This code returns the current + value that's associated with the key "data:counter" from the previous example. Then it resets the value + for this key back to zero, all as part of the same operation: + + ```csharp + ConnectionMultiplexer redisHostConnection = ...; + IDatabase cache = redisHostConnection.GetDatabase(); + ... + string oldValue = await cache.StringGetSetAsync("data:counter", 0); + ``` +* `MGET` and `MSET`, which can return or change a set of string values as a single operation. The + `IDatabase.StringGetAsync` and `IDatabase.StringSetAsync` methods are overloaded to support + this functionality, as shown in the following example: + + ```csharp + ConnectionMultiplexer redisHostConnection = ...; + IDatabase cache = redisHostConnection.GetDatabase(); + ... + // Create a list of key-value pairs + var keysAndValues = + new List>() + { + new KeyValuePair("data:key1", "value1"), + new KeyValuePair("data:key99", "value2"), + new KeyValuePair("data:key322", "value3") + }; + + // Store the list of key-value pairs in the cache + cache.StringSet(keysAndValues.ToArray()); + ... + // Find all values that match a list of keys + RedisKey[] keys = { "data:key1", "data:key99", "data:key322"}; + RedisValue[] values = null; + values = cache.StringGet(keys); + // values should contain { "value1", "value2", "value3" } + ``` + +You can also combine multiple operations into a single Redis transaction as described in the Redis transactions and batches section earlier in this article. The StackExchange library provides support for transactions through the `ITransaction` interface. + +You create an `ITransaction` object by using the `IDatabase.CreateTransaction` method. You invoke commands to the transaction by using the methods provided by the `ITransaction` object. + +The `ITransaction` interface provides access to a set of methods that's similar to those accessed by the `IDatabase` interface, except that all the methods are asynchronous. This means that they are only performed when the `ITransaction.Execute` method is invoked. The value that's returned by the `ITransaction.Execute` method indicates whether the transaction was created successfully (true) or if it failed (false). + +The following code snippet shows an example that increments and decrements two counters as part of the same transaction: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +ITransaction transaction = cache.CreateTransaction(); +var tx1 = transaction.StringIncrementAsync("data:counter1"); +var tx2 = transaction.StringDecrementAsync("data:counter2"); +bool result = transaction.Execute(); +Console.WriteLine("Transaction {0}", result ? "succeeded" : "failed"); +Console.WriteLine("Result of increment: {0}", tx1.Result); +Console.WriteLine("Result of decrement: {0}", tx2.Result); +``` + +Remember that Redis transactions are unlike transactions in relational databases. The `Execute` method simply queues all the commands that comprise the transaction to be run, and if any of them is malformed then the transaction is stopped. If all the commands have been queued successfully, each command runs asynchronously. + +If any command fails, the others still continue processing. If you need to verify that a command has completed successfully, you must fetch the results of the command by using the **Result** property of the corresponding task, as shown in the example above. Reading the **Result** property will block the calling thread until the task has completed. + +For more information, see the [Transactions in Redis](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/Transactions.md) page on the StackExchange.Redis website. + +When performing batch operations, you can use the `IBatch` interface of the StackExchange library. This interface provides access to a set of methods similar to those accessed by the `IDatabase` interface, except that all the methods are asynchronous. + +You create an `IBatch` object by using the `IDatabase.CreateBatch` method, and then run the batch by using the `IBatch.Execute` method, as shown in the following example. This code simply sets a string value, increments and decrements the same counters used in the previous example, and displays the results: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +IBatch batch = cache.CreateBatch(); +batch.StringSetAsync("data:key1", 11); +var t1 = batch.StringIncrementAsync("data:counter1"); +var t2 = batch.StringDecrementAsync("data:counter2"); +batch.Execute(); +Console.WriteLine("{0}", t1.Result); +Console.WriteLine("{0}", t2.Result); +``` + +It is important to understand that unlike a transaction, if a command in a batch fails because it is malformed, the other commands might still run. The `IBatch.Execute` method does not return any indication of success or failure. + +### Perform fire and forget cache operations +Redis supports fire and forget operations by using command flags. In this situation, the client simply initiates an operation but has no interest in the result and does not wait for the command to be completed. The example below shows how to perform the INCR command as a fire and forget operation: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +await cache.StringSetAsync("data:key1", 99); +... +cache.StringIncrement("data:key1", flags: CommandFlags.FireAndForget); +``` + +### Specify automatically expiring keys +When you store an item in a Redis cache, you can specify a timeout after which the item will be automatically removed from the cache. You can also query how much more time a key has before it expires by using the `TTL` command. This command is available to StackExchange applications by using the `IDatabase.KeyTimeToLive` method. + +The following code snippet shows how to set an expiration time of 20 seconds on a key, and query the remaining lifetime of the key: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +// Add a key with an expiration time of 20 seconds +await cache.StringSetAsync("data:key1", 99, TimeSpan.FromSeconds(20)); +... +// Query how much time a key has left to live +// If the key has already expired, the KeyTimeToLive function returns a null +TimeSpan? expiry = cache.KeyTimeToLive("data:key1"); +``` + +You can also set the expiration time to a specific date and time by using the EXPIRE command, which is available in the StackExchange library as the `KeyExpireAsync` method: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +// Add a key with an expiration date of midnight on 1st January 2015 +await cache.StringSetAsync("data:key1", 99); +await cache.KeyExpireAsync("data:key1", + new DateTime(2015, 1, 1, 0, 0, 0, DateTimeKind.Utc)); +... +``` + +> *Tip:* You can manually remove an item from the cache by using the DEL command, which is available through the StackExchange library as the `IDatabase.KeyDeleteAsync` method. +> +> + +### Use tags to cross-correlate cached items +A Redis set is a collection of multiple items that share a single key. You can create a set by using the SADD command. You can retrieve the items in a set by using the SMEMBERS command. The StackExchange library implements the SADD command with the `IDatabase.SetAddAsync` method, and the SMEMBERS command with the `IDatabase.SetMembersAsync` method. + +You can also combine existing sets to create new sets by using the SDIFF (set difference), SINTER (set intersection), and SUNION (set union) commands. The StackExchange library unifies these operations in the `IDatabase.SetCombineAsync` method. The first parameter to this method specifies the set operation to perform. + +The following code snippets show how sets can be useful for quickly storing and retrieving collections of related items. This code uses the `BlogPost` type that was described in the section Implement Redis Cache Client Applications earlier in this article. + +A `BlogPost` object contains four fields—an ID, a title, a ranking score, and a collection of tags. The first code snippet below shows the sample data that's used for populating a C# list of `BlogPost` objects: + +```csharp +List tags = new List() +{ + new string[] { "iot","csharp" }, + new string[] { "iot","azure","csharp" }, + new string[] { "csharp","git","big data" }, + new string[] { "iot","git","database" }, + new string[] { "database","git" }, + new string[] { "csharp","database" }, + new string[] { "iot" }, + new string[] { "iot","database","git" }, + new string[] { "azure","database","big data","git","csharp" }, + new string[] { "azure" } +}; + +List posts = new List(); +int blogKey = 0; +int blogPostId = 0; +int numberOfPosts = 20; +Random random = new Random(); +for (int i = 0; i < numberOfPosts; i++) +{ + blogPostId = blogKey++; + posts.Add(new BlogPost( + blogPostId, // Blog post ID + string.Format(CultureInfo.InvariantCulture, "Blog Post #{0}", + blogPostId), // Blog post title + random.Next(100, 10000), // Ranking score + tags[i % tags.Count])); // Tags--assigned from a collection + // in the tags list +} +``` + +You can store the tags for each `BlogPost` object as a set in a Redis cache and associate each set with the ID of the `BlogPost`. This enables an application to quickly find all the tags that belong to a specific blog post. To enable searching in the opposite direction and find all blog posts that share a specific tag, you can create another set that holds the blog posts referencing the tag ID in the key: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +// Tags are easily represented as Redis Sets +foreach (BlogPost post in posts) +{ + string redisKey = string.Format(CultureInfo.InvariantCulture, + "blog:posts:{0}:tags", post.Id); + // Add tags to the blog post in Redis + await cache.SetAddAsync( + redisKey, post.Tags.Select(s => (RedisValue)s).ToArray()); + + // Now do the inverse so we can figure how which blog posts have a given tag + foreach (var tag in post.Tags) + { + await cache.SetAddAsync(string.Format(CultureInfo.InvariantCulture, + "tag:{0}:blog:posts", tag), post.Id); + } +} +``` + +These structures enable you to perform many common queries very efficiently. For example, you can find and display all of the tags for blog post 1 like this: + +```csharp +// Show the tags for blog post #1 +foreach (var value in await cache.SetMembersAsync("blog:posts:1:tags")) +{ + Console.WriteLine(value); +} +``` + +You can find all tags that are common to blog post 1 and blog post 2 by performing a set intersection operation, as follows: + +```csharp +// Show the tags in common for blog posts #1 and #2 +foreach (var value in await cache.SetCombineAsync(SetOperation.Intersect, new RedisKey[] + { "blog:posts:1:tags", "blog:posts:2:tags" })) +{ + Console.WriteLine(value); +} +``` + +And you can find all blog posts that contain a specific tag: + +```csharp +// Show the ids of the blog posts that have the tag "iot". +foreach (var value in await cache.SetMembersAsync("tag:iot:blog:posts")) +{ + Console.WriteLine(value); +} +``` + +### Find recently accessed items +A common task required of many applications is to find the most recently accessed items. For example, a blogging site might want to display information about the most recently read blog posts. + +You can implement this functionality by using a Redis list. A Redis list contains multiple items that share the same key. The list acts as a double-ended queue. You can push items to either end of the list by using the LPUSH (left push) and RPUSH (right push) commands. You can retrieve items from either end of the list by using the LPOP and RPOP commands. You can also return a set of elements by using the LRANGE and RRANGE commands. + +The code snippets below show how you can perform these operations by using the StackExchange library. This code uses the `BlogPost` type from the previous examples. As a blog post is read by a user, the `IDatabase.ListLeftPushAsync` method pushes the title of the blog post onto a list that's associated with the key "blog:recent_posts" in the Redis cache. + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +string redisKey = "blog:recent_posts"; +BlogPost blogPost = ...; // Reference to the blog post that has just been read +await cache.ListLeftPushAsync( + redisKey, blogPost.Title); // Push the blog post onto the list +``` + +As more blog posts are read, their titles are pushed onto the same list. The list is ordered by the sequence in which the titles have been added. The most recently read blog posts are towards the left end of the list. (If the same blog post is read more than once, it will have multiple entries in the list.) + +You can display the titles of the most recently read posts by using the `IDatabase.ListRange` method. This method takes the key that contains the list, a starting point, and an ending point. The following code retrieves the titles of the 10 blog posts (items from 0 to 9) at the left-most end of the list: + +```csharp +// Show latest ten posts +foreach (string postTitle in await cache.ListRangeAsync(redisKey, 0, 9)) +{ + Console.WriteLine(postTitle); +} +``` + +Note that the `ListRangeAsync` method does not remove items from the list. To do this, you can use the `IDatabase.ListLeftPopAsync` and `IDatabase.ListRightPopAsync` methods. + +To prevent the list from growing indefinitely, you can periodically cull items by trimming the list. The code snippet below shows you how to remove all but the five left-most items from the list: + +```csharp +await cache.ListTrimAsync(redisKey, 0, 5); +``` + +### Implement a leader board +By default, the items in a set are not held in any specific order. You can create an ordered set by using the ZADD command (the `IDatabase.SortedSetAdd` method in the StackExchange library). The items are ordered by using a numeric value called a score, which is provided as a parameter to the command. + +The following code snippet adds the title of a blog post to an ordered list. In this example, each blog post also has a score field that contains the ranking of the blog post. + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +IDatabase cache = redisHostConnection.GetDatabase(); +... +string redisKey = "blog:post_rankings"; +BlogPost blogPost = ...; // Reference to a blog post that has just been rated +await cache.SortedSetAddAsync(redisKey, blogPost.Title, blogpost.Score); +``` + +You can retrieve the blog post titles and scores in ascending score order by using the `IDatabase.SortedSetRangeByRankWithScores` method: + +```csharp +foreach (var post in await cache.SortedSetRangeByRankWithScoresAsync(redisKey)) +{ + Console.WriteLine(post); +} +``` + +> [!NOTE] +> The StackExchange library also provides the `IDatabase.SortedSetRangeByRankAsync` method, which returns the data in score order, but does not return the scores. +> +> + +You can also retrieve items in descending order of scores, and limit the number of items that are returned by providing additional parameters to the `IDatabase.SortedSetRangeByRankWithScoresAsync` method. The next example displays the titles and scores of the top 10 ranked blog posts: + +```csharp +foreach (var post in await cache.SortedSetRangeByRankWithScoresAsync( + redisKey, 0, 9, Order.Descending)) +{ + Console.WriteLine(post); +} +``` + +The next example uses the `IDatabase.SortedSetRangeByScoreWithScoresAsync` method, which you can use to limit the items that are returned to those that fall within a given score range: + +```csharp +// Blog posts with scores between 5000 and 100000 +foreach (var post in await cache.SortedSetRangeByScoreWithScoresAsync( + redisKey, 5000, 100000)) +{ + Console.WriteLine(post); +} +``` + +### Message by using channels +Apart from acting as a data cache, a Redis server provides messaging through a high-performance publisher/subscriber mechanism. Client applications can subscribe to a channel, and other applications or services can publish messages to the channel. Subscribing applications will then receive these messages and can process them. + +Redis provides the SUBSCRIBE command for client applications to use to subscribe to channels. This command expects the name of one or more channels on which the application will accept messages. The StackExchange library includes the `ISubscription` interface, which enables a .NET Framework application to subscribe and publish to channels. + +You create an `ISubscription` object by using the `GetSubscriber` method of the connection to the Redis server. Then you listen for messages on a channel by using the `SubscribeAsync` method of this object. The following code example shows how to subscribe to a channel named "messages:blogPosts": + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +ISubscriber subscriber = redisHostConnection.GetSubscriber(); +... +await subscriber.SubscribeAsync("messages:blogPosts", (channel, message) => +{ + Console.WriteLine("Title is: {0}", message); +}); +``` + +The first parameter to the `Subscribe` method is the name of the channel. This name follows the same conventions that are used by keys in the cache. The name can contain any binary data, although it is advisable to use relatively short, meaningful strings to help ensure good performance and maintainability. + +Note also that the namespace used by channels is separate from that used by keys. This means you can have channels and keys that have the same name, although this may make your application code more difficult to maintain. + +The second parameter is an Action delegate. This delegate runs asynchronously whenever a new message appears on the channel. This example simply displays the message on the console (the message will contain the title of a blog post). + +To publish to a channel, an application can use the Redis PUBLISH command. The StackExchange library provides the `IServer.PublishAsync` method to perform this operation. The next code snippet shows how to publish a message to the "messages:blogPosts" channel: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +ISubscriber subscriber = redisHostConnection.GetSubscriber(); +... +BlogPost blogpost = ...; +subscriber.PublishAsync("messages:blogPosts", blogPost.Title); +``` + +There are several points you should understand about the publish/subscribe mechanism: + +* Multiple subscribers can subscribe to the same channel, and they will all receive the messages that are published to that channel. +* Subscribers only receive messages that have been published after they have subscribed. Channels are not buffered, and once a message is published, the Redis infrastructure pushes the message to each subscriber and then removes it. +* By default, messages are received by subscribers in the order in which they are sent. In a highly active system with a large number + of messages and many subscribers and publishers, guaranteed sequential delivery of messages can slow performance of the system. If + each message is independent and the order is unimportant, you can enable concurrent processing by the Redis system, which can help to + improve responsiveness. You can achieve this in a StackExchange client by setting the PreserveAsyncOrder of the connection used by + the subscriber to false: + +```csharp +ConnectionMultiplexer redisHostConnection = ...; +redisHostConnection.PreserveAsyncOrder = false; +ISubscriber subscriber = redisHostConnection.GetSubscriber(); +``` + +## Related patterns and guidance +The following pattern might also be relevant to your scenario when you implement caching in your applications: + +* [Cache-aside pattern](http://msdn.microsoft.com/library/dn589799.aspx): This pattern describes how to load data on demand into a cache from a data store. This pattern also helps to maintain consistency between data that's held in the cache and the data in the original data store. +* The [Sharding pattern](http://msdn.microsoft.com/library/dn589797.aspx) provides information about implementing horizontal partitioning to help improve scalability when storing and accessing large volumes of data. + +## More information +* The [MemoryCache class](http://msdn.microsoft.com/library/system.runtime.caching.memorycache.aspx) page on the Microsoft website +* The [Azure Redis Cache documentation](https://azure.microsoft.com/documentation/services/cache/) page on the Microsoft website +* The [Azure Redis Cache FAQ](/azure/redis-cache/cache-faq/) page on the Microsoft website +* The [Configuration model](http://msdn.microsoft.com/library/windowsazure/hh914149.aspx) page on the Microsoft website +* The [Task-based Asynchronous Pattern](http://msdn.microsoft.com/library/hh873175.aspx) page on the Microsoft website +* The [Pipelines and multiplexers](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/PipelinesMultiplexers.md) page on the StackExchange.Redis GitHub repo +* The [Redis persistence](http://redis.io/topics/persistence) page on the Redis website +* The [Replication page](http://redis.io/topics/replication) on the Redis website +* The [Redis cluster tutorial](http://redis.io/topics/cluster-tutorial) page on the Redis website +* The [Partitioning: how to split data among multiple Redis instances](http://redis.io/topics/partitioning) page on the Redis website +* The [Using Redis as an LRU Cache](http://redis.io/topics/lru-cache) page on the Redis website +* The [Transactions](http://redis.io/topics/transactions) page on the Redis website +* The [Redis security](http://redis.io/topics/security) page on the Redis website +* The [Lap around Azure Redis Cache](https://azure.microsoft.com/blog/2014/06/04/lap-around-azure-redis-cache-preview/) page on the Azure blog +* The [Running Redis on a CentOS Linux VM in Azure](http://blogs.msdn.com/b/tconte/archive/2012/06/08/running-redis-on-a-centos-linux-vm-in-windows-azure.aspx) page on the Microsoft website +* The [ASP.NET session state provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-session-state-provider/) page on the Microsoft website +* The [ASP.NET output cache provider for Azure Redis Cache](/azure/redis-cache/cache-aspnet-output-cache-provider/) page on the Microsoft website +* The [An Introduction to Redis data types and abstractions](http://redis.io/topics/data-types-intro) page on the Redis website +* The [Basic usage](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/Basics.md) page on the StackExchange.Redis website +* The [Transactions in Redis](https://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/Transactions.md) page on the StackExchange.Redis repo +* The [Data partitioning guide](http://msdn.microsoft.com/library/dn589795.aspx) on the Microsoft website + diff --git a/docs/best-practices/cdn.md b/docs/best-practices/cdn.md new file mode 100644 index 00000000000..3954a9ac60e --- /dev/null +++ b/docs/best-practices/cdn.md @@ -0,0 +1,244 @@ +--- +title: Content Delivery Network guidance +description: Guidance on Content Delivery Network (CDN) to deliver high bandwidth content hosted in Azure. +services: cdn +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 57df0e00-d540-46e2-930e-f800c2301bf4 +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 09/30/2016 +ms.author: masashin + +--- +# Content Delivery Network +[!INCLUDE [header](../_includes/header.md)] + +The Microsoft Azure Content Delivery Network (CDN) offers developers a global solution for delivering high-bandwidth content that is hosted in Azure or any other location. Using the CDN, you can cache publicly available objects loaded from Azure blob storage, a web application, virtual machine, application folder, or other HTTP/HTTPS location. The CDN cache can be held at strategic locations to provide maximum bandwidth for delivering content to users. The CDN is typically used for delivering static content such as images, style sheets, documents, files, client-side scripts, and HTML pages. + +You can also use the CDN as a cache for serving dynamic content, such as a PDF report or graph based on specified inputs; if the same input values are provided by different users the result should be the same. + +The major advantages of using the CDN are lower latency and faster delivery of content to users irrespective of their geographical location in relation to the datacenter where the application is hosted. + +![CDN diagram](./images/cdn/CDN.png) + +Using the CDN should also help to reduce the load on application because it is relieved of the processing required to access and deliver the content. This reduction in load can help to increase the performance and scalability of the application, as well as minimizing hosting costs by reducing the processing resources required to achieve a specific level of performance and availability. + +## How and why a CDN is used +Typical uses for a CDN include: + +* Delivering static resources for client applications, often from a website. These resources can be images, style sheets, documents, files, client-side scripts, HTML pages, HTML fragments, or any other content that the server does not need to modify for each request. The application can create items at runtime and make them available to the CDN (for example, by creating a list of current news headlines), but it does not do so for each request. +* Delivering public static and shared content to devices such as mobile phones and tablet computers. The application itself is a web service that offers an API to clients running on the various devices. The CDN can also deliver static datasets (via the web service) for the clients to use, perhaps to generate the client UI. For example, the CDN could be used to distribute JSON or XML documents. +* Serving entire websites that consist of only public static content to clients, without requiring any dedicated compute resources. +* Streaming video files to the client on demand. Video benefits from the low latency and reliable connectivity available from the globally located datacenters that offer CDN connections. +* Generally improving the experience for users, especially those located far from the datacenter hosting the application. These users might otherwise suffer higher latency. A large proportion of the total size of the content in a web application is often static, and using the CDN can help to maintain performance and overall user experience while eliminating the requirement to deploy the application to multiple data centers. +* Handling the growing load on applications that support IoT (Internet of Things) solutions. The huge numbers of such devices and appliances involved could easily overwhelm an application if it was required to process broadcast messages and manage firmware update distribution directly to each device. +* Coping with peaks and surges in demand without requiring the application to scale, avoiding the consequent increased running costs. For example, when an update to an operating system is released for a hardware device such as a specific model of router, or for a consumer device such as a smart TV, there will be a huge peak in demand as it is downloaded by millions of users and devices over a short period. + +The following list shows examples of the median time to first byte from various geographic locations. The target web role is deployed to Azure West US. There is a strong correlation between greater boost due to the CDN and proximity to a CDN node. A complete list of Azure CDN node locations is available at [Azure Content Delivery Network (CDN) Node Locations](/azure/cdn/cdn-pop-locations/). + +| | Time (ms) to First Byte (Origin) | Time (ms) to First (CDN) | %CDN time improvement | +| --- | --- | --- | --- | +| \*San Jose, CA |47.5 |46.5 |2 % | +| \*\*Dulles, VA |109 |40.5 |169% | +| Buenos Aires, AR |210 |151 |39% | +| \*London, UK |195 |44 |343% | +| Shanghai, CN |242 |206 |17% | +| \*Singapore |214 |74 |189 % | +| \*Tokyo, JP |163 |48 |204 % | +| Seoul, KR |190 |190 |0% | + +\* Has an Azure CDN node in the same city. +\*\* Has an Azure CDN node in a neighboring city. + +## Challenges +There are several challenges to take into account when planning to use the CDN: + +* **Deployment**. You must decide the origin from which the CDN will fetch the content, and whether you need to deploy the content in more than one storage system (such as in the CDN and an alternative location). + + Your application deployment mechanism must take into account the process for deploying static content and resources as well as deploying the application files, such as ASPX pages. For example, you may need to implement a separate step to load content into Azure blob storage. +* **Versioning and cache-control**. You must consider how you will update static content and deploy new versions. The CDN content may be [purged](/azure/cdn/cdn-purge-endpoint/) using the Azure Portal when new versions of your assets are available. This is a similar challenge to managing client side caching, such as that which occurs in a web browser. +* **Testing**. It can be difficult to perform local testing of your CDN settings when developing and testing an application locally or in a staging environment. +* **Search engine optimisation (SEO)**. Content such as images and documents are served from a different domain when you use the CDN. This can have an effect on SEO for this content. +* **Content security**. Many CDN services such as Azure CDN do not currently offer any type of access control for the content. +* **Client security**. Clients might connect from an environment that does not allow access to resources on the CDN. This could be a security-constrained environment that limits access to only a set of known sources, or one that prevents loading of resources from anything other than the page origin. A fallback implementation is required to handle these cases. +* **Resilience**. The CDN is a potential single point of failure for an application. It has a lower availability SLA than blob storage (which can be used to deliver content directly) so you may need to consider implementing a fallback mechanism for critical content. + + You can monitor your CDN content availability, bandwidth, data transferred, hits, cache hit ratio and cache metrics from the Azure Portal in [real-time](/azure/cdn/cdn-real-time-stats/) and [aggregate reports](/azure/cdn/cdn-analyze-usage-patterns/). + +Scenarios where CDN may be less useful include: + +* If the content has a low hit rate it might be accessed only few times while it is valid (determined by its time-to-live setting). The first time an item is downloaded you incur two transaction charges from the origin to the CDN, and then from the CDN to the customer. +* If the data is private, such as for large enterprises or supply chain ecosystems. + +## General guidelines and good practices +Using the CDN is a good way to minimize the load on your application, and maximize availability and performance. You should consider adopting this strategy for all of the appropriate content and resources you application uses. Consider the points in the following sections when designing your strategy to use the CDN: + +### Origin +Deploying content through the CDN simply requires you to specify an HTTP and/or HTTPS endpoint that the CDN service will use to access and cache the content. + +The endpoint can specify an Azure blob storage container that holds the static content you want to deliver through the CDN. The container must be marked as public. Only blobs in a public container that have public read access will be available through the CDN. + +The endpoint can specify a folder named **cdn** in the root of one of application’s compute layers (such as a web role or a virtual machine). The results from requests for resources, including dynamic resources such as ASPX pages, will be cached on the CDN. The minimum cache period is 300 seconds. Any shorter period will prevent the content from being deployed to the CDN (see the heading *Cache control* below for more information). + +If you are using Azure Web Apps, the endpoint is set to the root folder of the site by selecting the site when creating the CDN instance. All of the content for the site will be available through the CDN. + +In most cases, pointing your CDN endpoint at a folder within one of the compute layers of your application will offer more flexibility and control. For instance, it makes it easier to manage current and future routing requirements, and dynamically generate static content such as image thumbnails. + +You can use [query strings](/azure/cdn/cdn-query-string/) to differentiate objects in the cache when content is delivered from dynamic sources, such as ASPX pages. However, this behavior can be disabled by a setting in the Azure Portal when you specify the CDN endpoint. When delivering content from blob storage, query strings are treated as string literals so two items that have the same name but different query strings will be stored as separate items on the CDN. + +You can utilize URL rewriting for resources, such as scripts and other content, to avoid moving your files to the CDN origin folder. + +When using Azure storage blobs to hold content for the CDN, the URL of the resources in blobs is case sensitive for the container and blob name. + +When using custom origins or Azure Web Apps, you specify the path to the CDN instance in the links to resources. For example, the following specifies an image file in the **Images** folder of the site that will be delivered through the CDN: + +```XML + +``` + +### Deployment +Static content may need to be provisioned and deployed independently from the application if you do not include it in the application deployment package or process. Consider how this will affect the versioning approach you use to manage both the application components and the static resource content. + +Consider how bundling (combining several files into one file) and minification (removing unnecessary characters such as white space, new line characters, comments, and other characters) for script and CSS files will be handled. These are commonly used techniques that can reduce load times for clients, and are compatible with delivering content through the CDN. For more information, see [Bundling and Minification](http://www.asp.net/mvc/tutorials/mvc-4/bundling-and-minification). + +If you need to deploy the content to an additional location, this will be an extra step in the deployment process. If the application updates the content for the CDN, perhaps at regular intervals or in response to an event, it must store the updated content in any additional locations as well as the endpoint for the CDN. + +You cannot set up a CDN endpoint for an application in the local Azure emulator in Visual Studio. This restriction will affect unit testing, functional testing, and final pre-deployment testing. You must allow for this by implementing an alternative mechanism. For example, you could pre-deploy the content to the CDN using a custom application or utility, and perform testing during the period in which it is cached. Alternatively, use compile directives or global constants to control from where the application loads the resources. For example, when running in debug mode it could load resources such as client-side script bundles and other content from a local folder, and use the CDN when running in release mode. + +Consider which compression approach you want your CDN to support: + +* You can [enable compression](/azure/cdn/cdn-improve-performance/) on your origin server, in which case the CDN will support compression by default and deliver compressed content to clients in a format such as zip or gzip. When using an application folder as the CDN endpoint, the server may compress some content automatically in the same way as when delivering it directly to a web browser or other type of client. The format depends on the value of the **Accept-Encoding** header in the request sent by the client. In Azure the default mechanism is to automatically compress content when CPU utilization is below 50%. If you are using a cloud service to host the application, changing the settings may require using a startup task to turn on compression of dynamic output in IIS. See [Enabling gzip compression with Microsoft Azure CDN through a Web Role](http://blogs.msdn.com/b/avkashchauhan/archive/2012/03/05/enableing-gzip-compression-with-windows-azure-cdn-through-web-role.aspx) for more information. +* You can enable compression directly on CDN edge servers, in which case the CDN will compress the files and serve it to end users. For more information, see [Azure CDN Compression](/azure/cdn/cdn-improve-performance/). + +### Routing and versioning +You may need to use different CDN instances at various times. For example, when you deploy a new version of the application you may want to use a new CDN and retain the old CDN (holding content in an older format) for previous versions. If you use Azure blob storage as the content origin, you can simply create a separate storage account or a separate container and point the CDN endpoint to it. If you use the *cdn* root folder within the application as the CDN endpoint you can use URL rewriting techniques to direct requests to a different folder. + +Do not use the query string to denote different versions of the application in links to resources on the CDN because, when retrieving content from Azure blob storage, the query string is part of the resource name (the blob name). This approach can also affect how the client caches resources. + +Deploying new versions of static content when you update an application can be a challenge if the previous resources are cached on the CDN. For more information, see the section *Cache control*). + +Consider restricting the CDN content access by country. Azure CDN allows you to filter requests based on the country of origin and restrict the content delivered. For more information, see [Restrict access to your content by country](/azure/cdn/cdn-restrict-access-by-country/). + +### Cache control +Consider how to manage caching within the system. For example, when using a folder as the CDN origin you can specify the cacheability of pages that generate the content, and the content expiry time for all the resources in a specific folder. You can also specify cache properties for the CDN, and for the client using standard HTTP headers. Although you should already be managing caching on the server and client, using the CDN will help to make you more aware of how your content is cached, and where. + +To prevent objects from being available on the CDN you can delete them from the origin (blob container or application *cdn* root folder), remove or delete the CDN endpoint, or, in the case of blob storage, make the container or blob private. However, items will be removed from the CDN only when their time-to-live expires. If no cache expiry period is specified (such as when content is loaded from blob storage), it will be cached on the CDN for up to 7 days. You can also manually [purge a CDN endpoint](/azure/cdn/cdn-purge-endpoint/). + +In a web application, you can set the caching and expiry for all content by using the *clientCache* element in the *system.webServer/staticContent* section of the web.config file. Remember that when you place a web.config file in a folder it affects the files in that folder and all subfolders. + +If you create the content for the CDN dynamically (in your application code for example), ensure that you specify the *Cache.SetExpires* property on each page. The CDN will not cache the output from pages that use the default cacheability setting of *public*. Set the cache expiry period to a suitable value to ensure that the content is not discarded and reloaded from the application at very short intervals. + +### Security +The CDN can deliver content over HTTPS (SSL) using the certificate provided by the CDN, but it will also be available over HTTP as well. You cannot block HTTP access to items in the CDN. You may need to use HTTPS to request static content that is displayed in pages loaded through HTTPS (such as a shopping cart) to avoid browser warnings about mixed content. + +The Azure CDN does not provide any facilities for access control to secure access to content. You cannot use Shared Access Signatures (SAS) with the CDN. + +If you deliver client-side scripts using the CDN, you may encounter issues if these scripts use an *XMLHttpRequest* call to make HTTP requests for other resources such as data, images, or fonts in a different domain. Many web browsers prevent cross-origin resource sharing (CORS) unless the web server is configured to set the appropriate response headers. You can configure the CDN to support CORS: + +* If the origin from which you are delivering content is Azure blob storage, you can add a *CorsRule* to the service properties. The rule can specify the allowed origins for CORS requests, the allowed methods such as GET, and the maximum age in seconds for the rule (the period within which the client must request the linked resources after loading the original content). For more information, see [Cross-Origin Resource Sharing (CORS) Support for the Azure Storage Services](http://msdn.microsoft.com/library/azure/dn535601.aspx). +* If the origin from which you are delivering content is a folder within the application, such as the *cdn* root folder, you can configure outbound rules in the application configuration file to set an *Access-Control-Allow-Origin* header on all responses. For more information about using rewrite rules, see [URL Rewrite Module](http://www.iis.net/learn/extensions/url-rewrite-module). + +### Custom domains +The Azure CDN allows you to specify a [custom domain name](/azure/cdn/cdn-map-content-to-custom-domain/) and use it to access resources through the CDN. You can also set up a custom subdomain name using a *CNAME* record in your DNS. Using this approach can provide an additional layer of abstraction and control. + +If you use a *CNAME*, you cannot use SSL because the CDN uses its own single SSL certificate, and this certificate will not match your custom domain/subdomain names. + +### CDN fallback +You should consider how your application will cope with a failure or temporary unavailability of the CDN. Client applications may be able to use copies of the resources that were cached locally (on the client) during previous requests, or you can include code that detects failure and instead requests resources from the origin (the application folder or Azure blob container that holds the resources) if the CDN is unavailable. + +### Search engine optimisation +If SEO is an important consideration in your application, perform the following tasks: + +* Include a *Rel* canonical header in each page or resource. +* Use a *CNAME* subdomain record and access the resources using this name. +* Consider the impact of the fact that the IP address of the CDN may be a country or region that differs from that of the application itself. +* When using Azure blob storage as the origin, maintain the same file structure for resources on the CDN as in the application folders. + +### Monitoring and logging +Include the CDN as part of your application monitoring strategy to detect and measure failures or extended latency occurrences. Monitoring is available from the CDN profile manager located on the Azure portal site + +Enable logging for the CDN and monitor this log as part of your daily operations. + +Consider analyzing the CDN traffic for usage patterns. The Azure portal provides tools that enable you to monitor: + +* Bandwidth, +* Data Transferred, +* Hits (status codes), +* Cache Status, +* Cache HIT Ratio, and +* Ratio of IPV4/IPV6 requests. + +For more information, see [Analyze CDN usage patterns](/azure/cdn/cdn-analyze-usage-patterns/). + +### Cost implications +You are charged for outbound data transfers from the CDN. Additionally, if you're using blob storage to host your assets, you are charged for storage transactions when the CDN loads data from your application. You should set realistic cache expiry periods for content to ensure freshness, but not so short as to cause repeated reloading of content from the application or blob storage to the CDN. + +Items that are rarely downloaded will incur the two transaction charges without providing any significant reduction in server load. + +### Bundling and minification +Use bundling and minification to reduce the size of resources such as JavaScript code and HTML pages stored in the CDN. This strategy can help to reduce the time taken to download these items to the client. + +Bundling and minification can be handled by ASP.NET. In an MVC project, you define your bundles in *BundleConfig.cs*. A reference to the minified script bundle is created by calling the *Script.Render* method, typically in code in the view class. This reference contains a query string that includes a hash, which is based on the content of the bundle. If the bundle contents change, the generated hash will also change. + +By default, Azure CDN instances have the *Query String Status* setting disabled. In order for updated script bundles to be handled properly by the CDN, you must enable the *Query String Status* setting for the CDN instance. Note that it may take an hour or more before the setting takes effect. + +## Example code +This section contains some examples of code and techniques for working with the CDN. + +### URL rewriting +The following excerpt from a Web.config file in the root of a Cloud Services hosted application demonstrates how to perform [URL rewriting](https://technet.microsoft.com/library/ee215194.aspx) when using the CDN. Requests from the CDN for content that is cached are redirected to specific folders within the application root based on the type of the resource (such as scripts and images). + +```XML + + ... + + + + + + + + + + + + + + + + + + + + + + + + + ... + +``` + +These rewrite rules perform the following redirections: + +* The first rule allows you to embed a version in the file name of a resource, which is then ignored. For example, *Filename_v123.jpg *is rewritten as *Filename.jpg*. +* The next four rules show how to redirect requests if you do not want to store the resources in a folder named *cdn** in the root of the web role. The rules map the *cdn/Images*, *cdn/Content*, *cdn/Scripts*, and *cdn/bundles* URLs to their respective root folders in the web role. + +Note that using URL rewriting requires you to make some changes to the bundling of resources. + +## More information +* [Azure CDN](https://azure.microsoft.com/services/cdn/) +* [Azure Content Delievery Network (CDN) Documentation](https://azure.microsoft.com/documentation/services/cdn/) +* [Using Azure CDN](/azure/cdn/cdn-create-new-endpoint/) +* [Integrate a cloud service with Azure CDN](/azure/cdn/cdn-cloud-service-with-cdn/) +* [Best Practices for the Microsoft Azure Content Delivery Network](https://azure.microsoft.com/blog/2011/03/18/best-practices-for-the-windows-azure-content-delivery-network/) diff --git a/docs/best-practices/data-partitioning.md b/docs/best-practices/data-partitioning.md new file mode 100644 index 00000000000..148e25cb66d --- /dev/null +++ b/docs/best-practices/data-partitioning.md @@ -0,0 +1,585 @@ +--- +title: Data partitioning guidance +description: Guidance for how to separate partitions to be managed and accessed separately. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 401559b5-f25f-4010-9811-5743fbb72aeb +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/14/2016 +ms.author: masashin + +--- +# Data partitioning +[!INCLUDE [header](../_includes/header.md)] + +In many large-scale solutions, data is divided into separate partitions that can be managed and accessed separately. The partitioning strategy must be chosen carefully to maximize the benefits while minimizing adverse effects. Partitioning can help improve scalability, reduce contention, and optimize performance. Another benefit of partitioning is that it can provide a mechanism for dividing data by the pattern of use. For example, you can archive older, less active (cold) data in cheaper data storage. + +## Why partition data? +Most cloud applications and services store and retrieve data as part of their operations. The design of the data stores that an application uses can have a significant bearing on the performance, throughput, and scalability of a system. One technique that is commonly applied in large-scale systems is to divide the data into separate partitions. + +> The term *partitioning* that's used in this guidance refers to the process of physically dividing data into separate data stores. This is not the same as SQL Server table partitioning, which is a different concept. +> +> + +Partitioning data can offer a number of benefits. For example, it can be applied in order to: + +* **Improve scalability**. When you scale up a single database system, it will eventually reach a physical hardware limit. If you divide data across multiple partitions, each of which is hosted on a separate server, you can scale out the system almost indefinitely. +* **Improve performance**. Data access operations on each partition take place over a smaller volume of data. Provided that the data is partitioned in a suitable way, partitioning can make your system more efficient. Operations that affect more than one partition can run in parallel. Each partition can be located near the application that uses it to minimize network latency. +* **Improve availability**. Separating data across multiple servers avoids a single point of failure. If a server fails, or is undergoing planned maintenance, only the data in that partition is unavailable. Operations on other partitions can continue. Increasing the number of partitions reduces the relative impact of a single server failure by reducing the percentage of data that will be unavailable. Replicating each partition can further reduce the chance of a single partition failure affecting operations. It also makes it possible to separate critical data that must be continually and highly available from low-value data that has lower availability requirements (log files, for example). +* **Improve security**. Depending on the nature of the data and how it is partitioned, it might be possible to separate sensitive and non-sensitive data into different partitions, and therefore into different servers or data stores. Security can then be specifically optimized for the sensitive data. +* **Provide operational flexibility**. Partitioning offers many opportunities for fine tuning operations, maximizing administrative efficiency, and minimizing cost. For example, you can define different strategies for management, monitoring, backup and restore, and other administrative tasks based on the importance of the data in each partition. +* **Match the data store to the pattern of use**. Partitioning allows each partition to be deployed on a different type of data store, based on cost and the built-in features that data store offers. For example, large binary data can be stored in a blob data store, while more structured data can be held in a document database. For more information, see [Building a polyglot solution] in the patterns & practices guide and [Data access for highly-scalable solutions: Using SQL, NoSQL, and polyglot persistence] on the Microsoft website. + +Some systems do not implement partitioning because it is considered a cost rather than an advantage. Common reasons for this rationale include: + +* Many data storage systems do not support joins across partitions, and it can be difficult to maintain referential integrity in a partitioned system. It is frequently necessary to implement joins and integrity checks in application code (in the partitioning layer), which can result in additional I/O and application complexity. +* Maintaining partitions is not always a trivial task. In a system where the data is volatile, you might need to rebalance partitions periodically to reduce contention and hot spots. +* Some common tools do not work naturally with partitioned data. + +## Designing partitions +Data can be partitioned in different ways: horizontally, vertically, or functionally. The strategy you choose depends on the reason for partitioning the data, and the requirements of the applications and services that will use the data. + +> [!NOTE] +> The partitioning schemes described in this guidance are explained in a way that is independent of the underlying data storage technology. They can be applied to many types of data stores, including relational and NoSQL databases. +> +> + +### Partitioning strategies +The three typical strategies for partitioning data are: + +* **Horizontal partitioning** (often called *sharding*). In this strategy, each partition is a data store in its own right, but all partitions have the same schema. Each partition is known as a *shard* and holds a specific subset of the data, such as all the orders for a specific set of customers in an e-commerce application. +* **Vertical partitioning**. In this strategy, each partition holds a subset of the fields for items in the data store. The fields are divided according to their pattern of use. For example, frequently accessed fields might be placed in one vertical partition and less frequently accessed fields in another. +* **Functional partitioning**. In this strategy, data is aggregated according to how it is used by each bounded context in the system. For example, an e-commerce system that implements separate business functions for invoicing and managing product inventory might store invoice data in one partition and product inventory data in another. + +It’s important to note that the three strategies described here can be combined. They are not mutually exclusive, and we recommend that you consider them all when you design a partitioning scheme. For example, you might divide data into shards and then use vertical partitioning to further subdivide the data in each shard. Similarly, the data in a functional partition can be split into shards (which can also be vertically partitioned). + +However, the differing requirements of each strategy can raise a number of conflicting issues. You must evaluate and balance all of these when designing a partitioning scheme that meets the overall data processing performance targets for your system. The following sections explore each of the strategies in more detail. + +### Horizontal partitioning (sharding) +Figure 1 shows an overview of horizontal partitioning or sharding. In this example, product inventory data is divided into shards based on the product key. Each shard holds the data for a contiguous range of shard keys (A-G and H-Z), organized alphabetically. + +![Horizontally partitioning (sharding) data based on a partition key](./images/data-partitioning/DataPartitioning01.png) + +*Figure 1. Horizontally partitioning (sharding) data based on a partition key* + +Sharding helps you spread the load over more computers, which reduces contention and improves performance. You can scale the system out by adding further shards that run on additional servers. + +The most important factor when implementing this partitioning strategy is the choice of sharding key. It can be difficult to change the key after the system is in operation. The key must ensure that data is partitioned so that the workload is as even as possible across the shards. + +Note that different shards do not have to contain similar volumes of data. Rather, the more important consideration is to balance the number of requests. Some shards might be very large, but each item is the subject of a low number of access operations. Other shards might be smaller, but each item is accessed much more frequently. It is also important to ensure that a single shard does not exceed the scale limits (in terms of capacity and processing resources) of the data store that's being used to host that shard. + +If you use a sharding scheme, avoid creating hotspots (or hot partitions) that can affect performance and availability. For example, if you use a hash of a customer identifier instead of the first letter of a customer’s name, you prevent the unbalanced distribution that results from common and less common initial letters. This is a typical technique that helps distribute data more evenly across partitions. + +Choose a sharding key that minimizes any future requirements to split large shards into smaller pieces, coalesce small shards into larger partitions, or change the schema that describes the data stored in a set of partitions. These operations can be very time consuming, and might require taking one or more shards offline while they are performed. + +If shards are replicated, it might be possible to keep some of the replicas online while others are split, merged, or reconfigured. However, the system might need to limit the operations that can be performed on the data in these shards while the reconfiguration is taking place. For example, the data in the replicas can be marked as read-only to limit the scope of inconsistences that might occur while shards are being restructured. + +> For more detailed information and guidance about many of these considerations, and good practice techniques for designing data stores that implement horizontal partitioning, see [Sharding pattern]. +> +> + +### Vertical partitioning +The most common use for vertical partitioning is to reduce the I/O and performance costs associated with fetching the items that are accessed most frequently. Figure 2 shows an example of vertical partitioning. In this example, different properties for each data item are held in different partitions. One partition holds data that is accessed more frequently, including the name, description, and price information for products. Another holds the volume in stock and the last ordered date. + +![Vertically partitioning data by its pattern of use](./images/data-partitioning/DataPartitioning02.png) + +*Figure 2. Vertically partitioning data by its pattern of use* + +In this example, the application regularly queries the product name, description, and price when displaying the product details to customers. The stock level and date when the product was last ordered from the manufacturer are held in a separate partition because these two items are commonly used together. + +This partitioning scheme has the added advantage that the relatively slow-moving data (product name, description, and price) is separated from the more dynamic data (stock level and last ordered date). An application might find it beneficial to cache the slow-moving data in memory if it is frequently accessed. + +Another typical scenario for this partitioning strategy is to maximize the security of sensitive data. For example, you can do this by storing credit card numbers and the corresponding card security verification numbers in separate partitions. + +Vertical partitioning can also reduce the amount of concurrent access that's needed to the data. + +> Vertical partitioning operates at the entity level within a data store, partially normalizing an entity to break it down from a *wide* item to a set of *narrow* items. It is ideally suited for column-oriented data stores such as HBase and Cassandra. If the data in a collection of columns is unlikely to change, you can also consider using column stores in SQL Server. +> +> + +### Functional partitioning +For systems where it is possible to identify a bounded context for each distinct business area or service in the application, functional partitioning provides a technique for improving isolation and data access performance. Another common use of functional partitioning is to separate read-write data from read-only data that's used for reporting purposes. Figure 3 shows an overview of functional partitioning where inventory data is separated from customer data. + +![Functionally partitioning data by bounded context or subdomain](./images/data-partitioning/DataPartitioning03.png) + +*Figure 3. Functionally partitioning data by bounded context or subdomain* + +This partitioning strategy can help reduce data access contention across different parts of a system. + +## Designing partitions for scalability +It's vital to consider size and workload for each partition and balance them so that data is distributed to achieve maximum scalability. However, you must also partition the data so that it does not exceed the scaling limits of a single partition store. + +Follow these steps when designing partitions for scalability: + +1. Analyze the application to understand the data access patterns, such as the size of the result set returned by each query, the frequency of access, the inherent latency, and the server-side compute processing requirements. In many cases, a few major entities will demand most of the processing resources. +2. Use this analysis to determine the current and future scalability targets, such as data size and workload. Then distribute the data across the partitions to meet the scalability target. In the horizontal partitioning strategy, choosing the appropriate shard key is important to make sure distribution is even. For more information, see the [Sharding pattern]. +3. Make sure that the resources available to each partition are sufficient to handle the scalability requirements in terms of data size and throughput. For example, the node that's hosting a partition might impose a hard limit on the amount of storage space, processing power, or network bandwidth that it provides. If the data storage and processing requirements are likely to exceed these limits, it might be necessary to refine your partitioning strategy or split data out further. For example, one scalability approach might be to separate logging data from the core application features. You do this by using separate data stores to prevent the total data storage requirements from exceeding the scaling limit of the node. If the total number of data stores exceeds the node limit, it might be necessary to use separate storage nodes. +4. Monitor the system under use to verify that the data is distributed as expected and that the partitions can handle the load that is imposed on them. It's possible that the usage does not match the usage that's anticipated by the analysis. In that case, it might be possible to rebalance the partitions. Failing that, it might be necessary to redesign some parts of the system to gain the required balance. + +Note that some cloud environments allocate resources in terms of infrastructure boundaries. Ensure that the limits of your selected boundary provide enough room for any anticipated growth in the volume of data, in terms of data storage, processing power, and bandwidth. + +For example, if you use Azure table storage, a busy shard might require more resources than are available to a single partition to handle requests. (There is a limit to the volume of requests that can be handled by a single partition in a particular period of time. See the page [Azure storage scalability and performance targets] on the Microsoft website for more details.) + + If this is the case, the shard might need to be repartitioned to spread the load. If the total size or throughput of these tables exceeds the capacity of a storage account, it might be necessary to create additional storage accounts and spread the tables across these accounts. If the number of storage accounts exceeds the number of accounts that are available to a subscription, then it might be necessary to use multiple subscriptions. + +## Designing partitions for query performance +Query performance can often be boosted by using smaller data sets and by running parallel queries. Each partition should contain a small proportion of the entire data set. This reduction in volume can improve the performance of queries. However, partitioning is not an alternative for designing and configuring a database appropriately. For example, make sure that you have the necessary indexes in place if you are using a relational database. + +Follow these steps when designing partitions for query performance: + +1. Examine the application requirements and performance: + * Use the business requirements to determine the critical queries that must always perform quickly. + * Monitor the system to identify any queries that perform slowly. + * Establish which queries are performed most frequently. A single instance of each query might have minimal cost, but the cumulative consumption of resources could be significant. It might be beneficial to separate the data that's retrieved by these queries into a distinct partition, or even a cache. +2. Partition the data that is causing slow performance: + * Limit the size of each partition so that the query response time is within target. + * Design the shard key so that the application can easily find the partition if you are implementing horizontal partitioning. This prevents the query from having to scan through every partition. + * Consider the location of a partition. If possible, try to keep data in partitions that are geographically close to the applications and users that access it. +3. If an entity has throughput and query performance requirements, use functional partitioning based on that entity. If this still doesn't satisfy the requirements, apply horizontal partitioning as well. In most cases a single partitioning strategy will suffice, but in some cases it is more efficient to combine both strategies. +4. Consider using asynchronous queries that run in parallel across partitions to improve performance. + +## Designing partitions for availability +Partitioning data can improve the availability of applications by ensuring that the entire dataset does not constitute a single point of failure and that individual subsets of the dataset can be managed independently. Replicating partitions that contain critical data can also improve availability. + +When designing and implementing partitions, consider the following factors that affect availability: + +* **How critical the data is to business operations**. Some data might include critical business information such as invoice details or bank transactions. Other data might include less critical operational data, such as log files, performance traces, and so on. After identifying each type of data, consider: + * Storing critical data in highly-available partitions with an appropriate backup plan. + * Establishing separate management and monitoring mechanisms or procedures for the different criticalities of each dataset. Place data that has the same level of criticality in the same partition so that it can be backed up together at an appropriate frequency. For example, partitions that hold data for bank transactions might need to be backed up more frequently than partitions that hold logging or trace information. +* **How individual partitions can be managed**. Designing partitions to support independent management and maintenance provides several advantages. For example: + * If a partition fails, it can be recovered independently without affecting instances of applications that access data in other partitions. + * Partitioning data by geographical area allows scheduled maintenance tasks to occur at off-peak hours for each location. Ensure that partitions are not too big to prevent any planned maintenance from being completed during this period. +* **Whether to replicate critical data across partitions**. This strategy can improve availability and performance, although it can also introduce consistency issues. It takes time for changes made to data in a partition to be synchronized with every replica. During this period, different partitions will contain different data values. + +## Understanding how partitioning affects design and development +Using partitioning adds complexity to the design and development of your system. Consider partitioning as a fundamental part of system design even if the system initially only contains a single partition. If you address partitioning as an afterthought, when the system starts to suffer performance and scalability issues, the complexity increases because you already have a live system to maintain. + +If you update the system to incorporate partitioning in this environment, it necessitates modifying the data access logic. It can also involve migrating large quantities of existing data to distribute it across partitions, often while users expect to be able to continue using the system. + +In some cases, partitioning is not considered important because the initial dataset is small and can be easily handled by a single server. This might be true in a system that is not expected to scale beyond its initial size, but many commercial systems need to expand as the number of users increases. This expansion is typically accompanied by a growth in the volume of data. + +It's also important to understand that partitioning is not always a function of large data stores. For example, a small data store might be heavily accessed by hundreds of concurrent clients. Partitioning the data in this situation can help to reduce contention and improve throughput. + +Consider the following points when you design a data partitioning scheme: + +* **Where possible, keep data for the most common database operations together in each partition to minimize cross-partition data access operations**. Querying across partitions can be more time-consuming than querying only within a single partition, but optimizing partitions for one set of queries might adversely affect other sets of queries. When you can't avoid querying across partitions, minimize query time by running parallel queries and aggregating the results within the application. This approach might not be possible in some cases, such as when it's necessary to obtain a result from one query and use it in the next query. +* **If queries make use of relatively static reference data, such as postal code tables or product lists, consider replicating this data in all of the partitions to reduce the requirement for separate lookup operations in different partitions**. This approach can also reduce the likelihood of the reference data becoming a "hot" dataset that is subject to heavy traffic from across the entire system. However, there is an additional cost associated with synchronizing any changes that might occur to this reference data. +* **Where possible, minimize requirements for referential integrity across vertical and functional partitions**. In these schemes, the application itself is responsible for maintaining referential integrity across partitions when data is updated and consumed. Queries that must join data across multiple partitions run more slowly than queries that join data only within the same partition because the application typically needs to perform consecutive queries based on a key and then on a foreign key. Instead, consider replicating or de-normalizing the relevant data. To minimize the query time where cross-partition joins are necessary, run parallel queries over the partitions and join the data within the application. +* **Consider the effect that the partitioning scheme might have on the data consistency across partitions.** Evaluate whether strong consistency is actually a requirement. Instead, a common approach in the cloud is to implement eventual consistency. The data in each partition is updated separately, and the application logic ensures that the updates are all completed successfully. It also handles the inconsistencies that can arise from querying data while an eventually consistent operation is running. For more information about implementing eventual consistency, see the [Data consistency primer]. +* **Consider how queries locate the correct partition**. If a query must scan all partitions to locate the required data, there is a significant impact on performance, even when multiple parallel queries are running. Queries that are used with vertical and functional partitioning strategies can naturally specify the partitions. However, horizontal partitioning (sharding) can make locating an item difficult because every shard has the same schema. A typical solution for sharding is to maintain a map that can be used to look up the shard location for specific items of data. This map can be implemented in the sharding logic of the application, or maintained by the data store if it supports transparent sharding. +* **When using a horizontal partitioning strategy, consider periodically rebalancing the shards**. This helps distribute the data evenly by size and by workload to minimize hotspots, maximize query performance, and work around physical storage limitations. However, this is a complex task that often requires the use of a custom tool or process. +* **If you replicate each partition, it provides additional protection against failure**. If a single replica fails, queries can be directed towards a working copy. +* **If you reach the physical limits of a partitioning strategy, you might need to extend the scalability to a different level**. For example, if partitioning is at the database level, you might need to locate or replicate partitions in multiple databases. If partitioning is already at the database level, and physical limitations are an issue, it might mean that you need to locate or replicate partitions in multiple hosting accounts. +* **Avoid transactions that access data in multiple partitions**. Some data stores implement transactional consistency and integrity for operations that modify data, but only when the data is located in a single partition. If you need transactional support across multiple partitions, you will probably need to implement this as part of your application logic because most partitioning systems do not provide native support. + +All data stores require some operational management and monitoring activity. The tasks can range from loading data, backing up and restoring data, reorganizing data, and ensuring that the system is performing correctly and efficiently. + +Consider the following factors that affect operational management: + +* **How to implement appropriate management and operational tasks when the data is partitioned**. These tasks might include backup and restore, archiving data, monitoring the system, and other administrative tasks. For example, maintaining logical consistency during backup and restore operations can be a challenge. +* **How to load the data into multiple partitions and add new data that's arriving from other sources**. Some tools and utilities might not support sharded data operations such as loading data into the correct partition. This means that you might have to create or obtain new tools and utilities. +* **How to archive and delete the data on a regular basis**. To prevent the excessive growth of partitions, you need to archive and delete data on a regular basis (perhaps monthly). It might be necessary to transform the data to match a different archive schema. +* **How to locate data integrity issues**. Consider running a periodic process to locate any data integrity issues such as data in one partition that references missing information in another. The process can either attempt to fix these issues automatically or raise an alert to an operator to correct the problems manually. For example, in an e-commerce application, order information might be held in one partition but the line items that constitute each order might be held in another. The process of placing an order needs to add data to other partitions. If this process fails, there might be line items stored for which there is no corresponding order. + +Different data storage technologies typically provide their own features to support partitioning. The following sections summarize the options that are implemented by data stores commonly used by Azure applications. They also describe considerations for designing applications that can best take advantage of these features. + +## Partitioning strategies for Azure SQL Database +Azure SQL Database is a relational database-as-a-service that runs in the cloud. It is based on Microsoft SQL Server. A relational database divides information into tables, and each table holds information about entities as a series of rows. Each row contains columns that hold the data for the individual fields of an entity. The page [What is Azure SQL Database?] on the Microsoft website provides detailed documentation about creating and using SQL databases. + +## Horizontal partitioning with Elastic Database +A single SQL database has a limit to the volume of data that it can contain. Throughput is constrained by architectural factors and the number of concurrent connections that it supports. The Elastic Database feature of SQL Database supports horizontal scaling for a SQL database. Using Elastic Database, you can partition your data into shards that are spread across multiple SQL databases. You can also add or remove shards as the volume of data that you need to handle grows and shrinks. Using Elastic Database can also help reduce contention by distributing the load across databases. + +> [!NOTE] +> Elastic Database is a replacement for the Federations feature of Azure SQL Database. Existing SQL Database Federation installations can be migrated to Elastic Database by using the Federations migration utility. Alternatively, you can implement your own sharding mechanism if your scenario does not lend itself naturally to the features that are provided by Elastic Database. +> +> + +Each shard is implemented as a SQL database. A shard can hold more than one dataset (referred to as a *shardlet*). Each database maintains metadata that describes the shardlets that it contains. A shardlet can be a single data item, or it can be a group of items that share the same shardlet key. For example, if you are sharding data in a multitenant application, the shardlet key can be the tenant ID, and all data for a given tenant can be held as part of the same shardlet. Data for other tenants would be held in different shardlets. + +It is the programmer's responsibility to associate a dataset with a shardlet key. A separate SQL database acts as a global shard map manager. This database contains a list of all the shards and shardlets in the system. A client application that accesses data connects first to the global shard map manager database to obtain a copy of the shard map (listing shards and shardlets), which it then caches locally. + +The application then uses this information to route data requests to the appropriate shard. This functionality is hidden behind a series of APIs that are contained in the Azure SQL Database Elastic Database Client Library, which is available as a NuGet package. The page [Elastic Database features overview] on the Microsoft website provides a more comprehensive introduction to Elastic Database. + +> [!NOTE] +> You can replicate the global shard map manager database to reduce latency and improve availability. If you implement the database by using one of the Premium pricing tiers, you can configure active geo-replication to continuously copy data to databases in different regions. Create a copy of the database in each region in which users are based. Then configure your application to connect to this copy to obtain the shard map. +> +> An alternative approach is to use Azure SQL Data Sync or an Azure Data Factory pipeline to replicate the shard map manager database across regions. This form of replication runs periodically and is more suitable if the shard map changes infrequently. Additionally, the shard map manager database does not have to be created by using a Premium pricing tier. +> +> + +Elastic Database provides two schemes for mapping data to shardlets and storing them in shards: + +* A **list shard map** describes an association between a single key and a shardlet. For example, in a multitenant system, the data for each tenant can be associated with a unique key and stored in its own shardlet. To guarantee privacy and isolation (that is, to prevent one tenant from exhausting the data storage resources available to others), each shardlet can be held within its own shard. + +![Using a list shard map to store tenant data in separate shards](./images/data-partitioning/PointShardlet.png) + +*Figure 4. Using a list shard map to store tenant data in separate shards* + +* A **range shard map** describes an association between a set of contiguous key values and a shardlet. In the multitenant example described previously, as an alternative to implementing dedicated shardlets, you can group the data for a set of tenants (each with their own key) within the same shardlet. This scheme is less expensive than the first (because tenants share data storage resources), but it also creates a risk of reduced data privacy and isolation. + +![Using a range shard map to store data for a range of tenants in a shard](./images/data-partitioning/RangeShardlet.png) + +*Figure 5. Using a range shard map to store data for a range of tenants in a shard* + +Note that a single shard can contain the data for several shardlets. For example, you can use list shardlets to store data for different non-contiguous tenants in the same shard. You can also mix range shardlets and list shardlets in the same shard, although they will be addressed through different maps in the global shard map manager database. (The global shard map manager database can contain multiple shard maps.) Figure 6 depicts this approach. + +![Implementing multiple shard maps](./images/data-partitioning/MultipleShardMaps.png) + +*Figure 6. Implementing multiple shard maps* + +The partitioning scheme that you implement can have a significant bearing on the performance of your system. It can also affect the rate at which shards have to be added or removed, or the rate at which data must be repartitioned across shards. Consider the following points when you use Elastic Database to partition data: + +* Group data that is used together in the same shard, and avoid operations that need to access data that's held in multiple shards. Keep in mind that with Elastic Database, a shard is a SQL database in its own right, and Azure SQL Database does not support cross-database joins (which have to be performed on the client side). Remember also that in Azure SQL Database, referential integrity constraints, triggers, and stored procedures in one database cannot reference objects in another. Therefore, don't design a system that has dependencies between shards. A SQL database can, however, contain tables that hold copies of reference data frequently used by queries and other operations. These tables do not have to belong to any specific shardlet. Replicating this data across shards can help remove the need to join data that spans databases. Ideally, such data should be static or slow-moving to minimize the replication effort and reduce the chances of it becoming stale. + + > [!NOTE] + > Although SQL Database does not support cross-database joins, you can perform cross-shard queries with the Elastic Database API. These queries can transparently iterate through the data held in all the shardlets that are referenced by a shard map. The Elastic Database API breaks cross-shard queries down into a series of individual queries (one for each database) and then merges the results. For more information, see the page [Multi-shard querying] on the Microsoft website. + > + > +* The data stored in shardlets that belong to the same shard map should have the same schema. For example, don't create a list shard map that points to some shardlets containing tenant data and other shardlets containing product information. This rule is not enforced by Elastic Database, but data management and querying becomes very complex if each shardlet has a different schema. In the example just cited, a good is solution is to create two list shard maps: one that references tenant data and another that points to product information. Remember that the data belonging to different shardlets can be stored in the same shard. + + > [!NOTE] + > The cross-shard query functionality of the Elastic Database API depends on each shardlet in the shard map containing the same schema. + > + > +* Transactional operations are only supported for data that's held within the same shard, and not across shards. Transactions can span shardlets as long as they are part of the same shard. Therefore, if your business logic needs to perform transactions, either store the affected data in the same shard or implement eventual consistency. For more information, see the [Data consistency primer]. +* Place shards close to the users that access the data in those shards (in other words, geo-locate the shards). This strategy helps reduce latency. +* Avoid having a mixture of highly active (hotspots) and relatively inactive shards. Try to spread the load evenly across shards. This might require hashing the shardlet keys. +* If you are geo-locating shards, make sure that the hashed keys map to shardlets held in shards stored close to the users that access that data. +* Currently, only a limited set of SQL data types are supported as shardlet keys; *int, bigint, varbinary,* and *uniqueidentifier*. The SQL *int* and *bigint* types correspond to the *int* and *long* data types in C#, and have the same ranges. The SQL *varbinary* type can be handled by using a *Byte* array in C#, and the SQL *uniqueidentier* type corresponds to the *Guid* class in the .NET Framework. + +As the name implies, Elastic Database makes it possible for a system to add and remove shards as the volume of data shrinks and grows. The APIs in the Azure SQL Database Elastic Database client library enable an application to create and delete shards dynamically (and transparently update the shard map manager). However, removing a shard is a destructive operation that also requires deleting all the data in that shard. + +If an application needs to split a shard into two separate shards or combine shards, Elastic Database provides a separate split-merge service. This service runs in a cloud-hosted service (which must be created by the developer) and migrates data safely between shards. For more information, see the topic [Scaling using the Elastic Database split-merge tool] on the Microsoft website. + +## Partitioning strategies for Azure Storage +Azure storage provides three abstractions for managing data: + +* Table storage, which implements scalable structure storage. A table contains a collection of entities, each of which can include a set of properties and values. +* Blob storage, which supplies storage for large objects and files. +* Storage queues, which support reliable asynchronous messaging between applications. + +Table storage and blob storage are essentially key-value stores that are optimized to hold structured and unstructured data respectively. Storage queues provide a mechanism for building loosely coupled, scalable applications. Table storage, blob storage, and storage queues are created within the context of an Azure storage account. Storage accounts support three forms of redundancy: + +* **Locally redundant storage**, which maintains three copies of data within a single datacenter. This form of redundancy protects against hardware failure but not against a disaster that encompasses the entire datacenter. +* **Zone-redundant storage**, which maintains three copies of data spread across different datacenters within the same region (or across two geographically close regions). This form of redundancy can protect against disasters that occur within a single datacenter, but cannot protect against large-scale network disconnects that affect an entire region. Note that zone-redundant storage is currently only currently available for block blobs. +* **Geo-redundant storage**, which maintains six copies of data: three copies in one region (your local region), and another three copies in a remote region. This form of redundancy provides the highest level of disaster protection. + +Microsoft has published scalability targets for Azure Storage. For more information, see the page [Azure Storage scalability and performance targets] on the Microsoft website. Currently, the total storage account capacity cannot exceed 500 TB. (This includes the size of data that's held in table storage and blob storage, as well as outstanding messages that are held in storage queue). + +The maximum request rate (assuming a 1-KB entity, blob, or message size) is 20 KBps. If your system is likely to exceed these limits, consider partitioning the load across multiple storage accounts. A single Azure subscription can create up to 100 storage accounts. However, note that these limits might change over time. + +## Partitioning Azure table storage +Azure table storage is a key-value store that's designed around partitioning. All entities are stored in a partition, and partitions are managed internally by Azure table storage. Each entity that's stored in a table must provide a two-part key that includes: + +* **The partition key**. This is a string value that determines in which partition Azure table storage will place the entity. All entities with the same partition key will be stored in the same partition. +* **The row key**. This is another string value that identifies the entity within the partition. All entities within a partition are sorted lexically, in ascending order, by this key. The partition key/row key combination must be unique for each entity and cannot exceed 1 KB in length. + +The remainder of the data for an entity consists of application-defined fields. No particular schemas are enforced, and each row can contain a different set of application-defined fields. The only limitation is that the maximum size of an entity (including the partition and row keys) is currently 1 MB. The maximum size of a table is 200 TB, although these figures might change in the future. (Check the page [Azure Storage scalability and performance targets] on the Microsoft website for the most recent information about these limits.) + +If you are attempting to store entities that exceed this capacity, then consider splitting them into multiple tables. Use vertical partitioning to divide the fields into the groups that are most likely to be accessed together. + +Figure 7 shows the logical structure of an example storage account (Contoso Data) for a fictitious e-commerce application. The storage account contains three tables: Customer Info, Product Info, and Order Info. Each table has multiple partitions. + +In the Customer Info table, the data is partitioned according to the city in which the customer is located, and the row key contains the customer ID. In the Product Info table, the products are partitioned by product category, and the row key contains the product number. In the Order Info table, the orders are partitioned by the date on which they were placed, and the row key specifies the time the order was received. Note that all data is ordered by the row key in each partition. + +![The tables and partitions in an example storage account](./images/data-partitioning/TableStorage.png) + +*Figure 7. The tables and partitions in an example storage account* + +> [!NOTE] +> Azure table storage also adds a timestamp field to each entity. The timestamp field is maintained by table storage and is updated each time the entity is modified and written back to a partition. The table storage service uses this field to implement optimistic concurrency. (Each time an application writes an entity back to table storage, the table storage service compares the value of the timestamp in the entity that's being written with the value that's held in table storage. If the values are different, it means that another application must have modified the entity since it was last retrieved, and the write operation fails. Don't modify this field in your own code, and don't specify a value for this field when you create a new entity. +> +> + +Azure table storage uses the partition key to determine how to store the data. If an entity is added to a table with a previously unused partition key, Azure table storage creates a new partition for this entity. Other entities with the same partition key will be stored in the same partition. + +This mechanism effectively implements an automatic scale-out strategy. Each partition is stored on a single server in an Azure datacenter to help ensure that queries that retrieve data from a single partition run quickly. However, different partitions can be distributed across multiple servers. Additionally, a single server can host multiple partitions if these partitions are limited in size. + +Consider the following points when you design your entities for Azure table storage: + +* The selection of partition key and row key values should be driven by the way in which the data is accessed. Choose a partition key/row key combination that supports the majority of your queries. The most efficient queries retrieve data by specifying the partition key and the row key. Queries that specify a partition key and a range of row keys can be completed by scanning a single partition. This is relatively fast because the data is held in row key order. If queries don't specify which partition to scan, the partition key might require Azure table storage to scan every partition for your data. + + > [!TIP] + > If an entity has one natural key, then use it as the partition key and specify an empty string as the row key. If an entity has a composite key comprising two properties, select the slowest changing property as the partition key and the other as the row key. If an entity has more than two key properties, use a concatenation of properties to provide the partition and row keys. + > + > +* If you regularly perform queries that look up data by using fields other than the partition and row keys, consider implementing the [index table pattern]. +* If you generate partition keys by using a monotonic increasing or decreasing sequence (such as "0001", "0002", "0003", and so on) and each partition only contains a limited amount of data, then Azure table storage can physically group these partitions together on the same server. This mechanism assumes that the application is most likely to perform queries across a contiguous range of partitions (range queries) and is optimized for this case. However, this approach can lead to hotspots focused on a single server because all insertions of new entities are likely to be concentrated at one end or the other of the contiguous ranges. It can also reduce scalability. To spread the load more evenly across servers, consider hashing the partition key to make the sequence more random. +* Azure table storage supports transactional operations for entities that belong to the same partition. This means that an application can perform multiple insert, update, delete, replace, or merge operations as an atomic unit (as long as the transaction doesn't include more than 100 entities and the payload of the request doesn't exceed 4 MB). Operations that span multiple partitions are not transactional, and might require you to implement eventual consistency as described by the [Data consistency primer]. For more information about table storage and transactions, go to the page [Performing entity group transactions] on the Microsoft website. +* Give careful attention to the granularity of the partition key because of the following reasons: + * Using the same partition key for every entity causes the table storage service to create a single large partition that's held on one server. This prevents it from scaling out and instead focuses the load on a single server. As a result, this approach is only suitable for systems that manage a small number of entities. However, this approach does ensure that all entities can participate in entity group transactions. + * Using a unique partition key for every entity causes the table storage service to create a separate partition for each entity, possibly resulting in a large number of small partitions (depending on the size of the entities). This approach is more scalable than using a single partition key, but entity group transactions are not possible. Also, queries that fetch more than one entity might involve reading from more than one server. However, if the application performs range queries, then using a monotonic sequence to generate the partition keys might help to optimize these queries. + * Sharing the partition key across a subset of entities makes it possible for you to group related entities in the same partition. Operations that involve related entities can be performed by using entity group transactions, and queries that fetch a set of related entities can be satisfied by accessing a single server. + +For additional information about partitioning data in Azure table storage, see the article [Azure storage table design guide] on the Microsoft website. + +## Partitioning Azure blob storage +Azure blob storage makes it possible to hold large binary objects--currently up to 200 GB in size for block blobs or 1 TB for page blobs. (For the most recent information, go to the page [Azure Storage scalability and performance targets] on the Microsoft website.) Use block blobs in scenarios such as streaming where you need to upload or download large volumes of data quickly. Use page blobs for applications that require random rather than serial access to parts of the data. + +Each blob (either block or page) is held in a container in an Azure storage account. You can use containers to group related blobs that have the same security requirements, although this grouping is logical rather than physical. Inside a container, each blob has a unique name. + +Blob storage is automatically partitioned based on the blob name. Each blob is held in its own partition. Blobs in the same container do not share a partition. This architecture helps Azure blob storage to balance the load across servers transparently because different blobs in the same container can be distributed across different servers. + +The actions of writing a single block (block blob) or page (page blob) are atomic, but operations that span blocks, pages, or blobs are not. If you need to ensure consistency when performing write operations across blocks, pages, and blobs, take out a write lock by using a blob lease. + +Azure blob storage supports transfer rates of up to 60 MB per second or 500 requests per second for each blob. If you anticipate surpassing these limits, and the blob data is relatively static, then consider replicating blobs by using the Azure Content Delivery Network. For more information, see the page [Using Delivery Content Network for Azure] on the Microsoft website. For additional guidance and considerations, see [Using Content Delivery Network for Azure]. + +## Partitioning Azure storage queues +Azure storage queues enable you to implement asynchronous messaging between processes. An Azure storage account can contain any number of queues, and each queue can contain any number of messages. The only limitation is the space that's available in the storage account. The maximum size of an individual message is 64 KB. If you require messages bigger than this, then consider using Azure Service Bus queues instead. + +Each storage queue has a unique name within the storage account that contains it. Azure partitions queues based on the name. All messages for the same queue are stored in the same partition, which is controlled by a single server. Different queues can be managed by different servers to help balance the load. The allocation of queues to servers is transparent to applications and users. + + In a large-scale application, don't use the same storage queue for all instances of the application because this approach might cause the server that's hosting the queue to become a hotspot. Instead, use different queues for different functional areas of the application. Azure storage queues do not support transactions, so directing messages to different queues should have little impact on messaging consistency. + +An Azure storage queue can handle up to 2,000 messages per second. If you need to process messages at a greater rate than this, consider creating multiple queues. For example, in a global application, create separate storage queues in separate storage accounts to handle application instances that are running in each region. + +## Partitioning strategies for Azure Service Bus +Azure Service Bus uses a message broker to handle messages that are sent to a Service Bus queue or topic. By default, all messages that are sent to a queue or topic are handled by the same message broker process. This architecture can place a limitation on the overall throughput of the message queue. However, you can also partition a queue or topic when it is created. You do this by setting the *EnablePartitioning* property of the queue or topic description to *true*. + +A partitioned queue or topic is divided into multiple fragments, each of which is backed by a separate message store and message broker. Service Bus takes responsibility for creating and managing these fragments. When an application posts a message to a partitioned queue or topic, Service Bus assigns the message to a fragment for that queue or topic. When an application receives a message from a queue or subscription, Service Bus checks each fragment for the next available message and then passes it to the application for processing. + +This structure helps distribute the load across message brokers and message stores, increasing scalability and improving availability. If the message broker or message store for one fragment is temporarily unavailable, Service Bus can retrieve messages from one of the remaining available fragments. + +Service Bus assigns a message to a fragment as follows: + +* If the message belongs to a session, all messages with the same value for the * SessionId* property are sent to the same fragment. +* If the message does not belong to a session, but the sender has specified a value for the *PartitionKey* property, then all messages with the same *PartitionKey* value are sent to the same fragment. + + > [!NOTE] + > If the *SessionId* and *PartitionKey* properties are both specified, then they must be set to the same value or the message will be rejected. + > + > +* If the *SessionId* and *PartitionKey* properties for a message are not specified, but duplicate detection is enabled, the *MessageId* property will be used. All messages with the same *MessageId* will be directed to the same fragment. +* If messages do not include a *SessionId, PartitionKey,* or *MessageId* property, then Service Bus assigns messages to fragments sequentially. If a fragment is unavailable, Service Bus will move on to the next. This means that a temporary fault in the messaging infrastructure does not cause the message-send operation to fail. + +Consider the following points when deciding if or how to partition a Service Bus message queue or topic: + +* Service Bus queues and topics are created within the scope of a Service Bus namespace. Service Bus currently allows up to 100 partitioned queues or topics per namespace. +* Each Service Bus namespace imposes quotas on the available resources, such as the number of subscriptions per topic, the number of concurrent send and receive requests per second, and the maximum number of concurrent connections that can be established. These quotas are documented on the Microsoft website on the page [Service Bus quotas]. If you expect to exceed these values, then create additional namespaces with their own queues and topics, and spread the work across these namespaces. For example, in a global application, create separate namespaces in each region and configure application instances to use the queues and topics in the nearest namespace. +* Messages that are sent as part of a transaction must specify a partition key. This can be a *SessionId*, *PartitionKey*, or *MessageId* property. All messages that are sent as part of the same transaction must specify the same partition key because they must be handled by the same message broker process. You cannot send messages to different queues or topics within the same transaction. +* Partitioned queues and topics can't be configured to be automatically deleted when they become idle. +* Partitioned queues and topics can't currently be used with the Advanced Message Queuing Protocol (AMQP) if you are building cross-platform or hybrid solutions. + +## Partitioning strategies for Azure DocumentDB databases +Azure DocumentDB is a NoSQL database that can store documents. A document in a DocumentDB database is a JSON-serialized representation of an object or other piece of data. No fixed schemas are enforced except that every document must contain a unique ID. + +Documents are organized into collections. You can group related documents together in a collection. For example, in a system that maintains blog postings, you can store the contents of each blog post as a document in a collection. You can also create collections for each subject type. Alternatively, in a multitenant application, such as a system where different authors control and manage their own blog posts, you can partition blogs by author and create separate collections for each author. The storage space that's allocated to collections is elastic and can shrink or grow as needed. + +Document collections provide a natural mechanism for partitioning data within a single database. Internally, a DocumentDB database can span several servers and might attempt to spread the load by distributing collections across servers. The simplest way to implement sharding is to create a collection for each shard. + +> [!NOTE] +> Each DocumentDB database has a *performance level* that determines the amount of resources it gets. A performance level is associated with a *request unit* (RU) rate limit. The RU rate limit specifies the volume of resources that's reserved and available for exclusive use by that collection. The cost of a collection depends on the performance level that's selected for that collection. The higher the performance level (and RU rate limit) the higher the charge. You can adjust the performance level of a collection by using the Azure portal. For more information, see the page [Performance levels in DocumentDB] on the Microsoft website. +> +> + +All databases are created in the context of a DocumentDB account. A single DocumentDB account can contain several databases, and it specifies in which region the databases are created. Each DocumentDB account also enforces its own access control. You can use DocumentDB accounts to geo-locate shards (collections within databases) close to the users who need to access them, and enforce restrictions so that only those users can connect to them. + +Each DocumentDB account has a quota that limits the number of databases and collections that it can contain and the amount of document storage that's available. These limits are subject to change, but are described on the page [DocumentDB limits and quotas] on the Microsoft website. It is theoretically possible that if you implement a system where all shards belong to the same database, you might reach the storage capacity limit of the account. + +In this case, you might need to create additional DocumentDB accounts and databases, and distribute the shards across these databases. However, even if you are unlikely to reach the storage capacity of a database, it's a good practice to use multiple databases. That's because each database has its own set of users and permissions, and you can use this mechanism to isolate access to collections on a per-database basis. + +Figure 8 illustrates the high-level structure of the DocumentDB architecture. + +![The structure of DocumentDB](./images/data-partitioning/DocumentDBStructure.png) + +*Figure 8. The structure of the DocumentDB architecture* + +It is the task of the client application to direct requests to the appropriate shard, usually by implementing its own mapping mechanism based on some attributes of the data that define the shard key. Figure 9 shows two DocumentDB databases, each containing two collections that are acting as shards. The data is sharded by a tenant ID and contains the data for a specific tenant. The databases are created in separate DocumentDB accounts. These accounts are located in the same region as the tenants for which they contain data. The routing logic in the client application uses the tenant ID as the shard key. + +![Implementing sharding using Azure DocumentDB](./images/data-partitioning/DocumentDBPartitions.png) + +*Figure 9. Implementing sharding using an Azure DocumentDB database* + +Consider the following points when deciding how to partition data with a DocumentDB database: + +* **The resources available to a DocumentDB database are subject to the quota limitations of the DocumentDB account**. Each database can hold a number of collections (again, there is a limit), and each collection is associated with a performance level that governs the RU rate limit (reserved throughput) for that collection. For more information, go to the page [DocumentDB limits and quotas] on the Microsoft website. +* **Each document must have an attribute that can be used to uniquely identify that document within the collection in which it is held**. This attribute is different from the shard key, which defines which collection holds the document. A collection can contain a large number of documents. In theory, it's limited only by the maximum length of the document ID. The document ID can be up to 255 characters. +* **All operations against a document are performed within the context of a transaction. Transactions in DocumentDB databases are scoped to the collection in which the document is contained.** If an operation fails, the work that it has performed is rolled back. While a document is subject to an operation, any changes that are made are subject to snapshot-level isolation. This mechanism guarantees that if, for example, a request to create a new document fails, another user who's querying the database simultaneously will not see a partial document that is then removed. +* **DocumentDB database queries are also scoped to the collection level**. A single query can retrieve data from only one collection. If you need to retrieve data from multiple collections, you must query each collection individually and merge the results in your application code. +* **DocumentDB databases supports programmable items that can all be stored in a collection alongside documents**. These include stored procedures, user-defined functions, and triggers (written in JavaScript). These items can access any document within the same collection. Furthermore, these items run either inside the scope of the ambient transaction (in the case of a trigger that fires as the result of a create, delete, or replace operation performed against a document), or by starting a new transaction (in the case of a stored procedure that is run as the result of an explicit client request). If the code in a programmable item throws an exception, the transaction is rolled back. You can use stored procedures and triggers to maintain integrity and consistency between documents, but these documents must all be part of the same collection. +* **The collections that you intend to hold in the databases in a DocumentDB account should be unlikely to exceed the throughput limits defined by the performance levels of the collections**. These limits are described on the page [Manage DocumentDB capacity needs] on the Microsoft website. If you anticipate reaching these limits, consider splitting collections across databases in different DocumentDB accounts to reduce the load per collection. + +## Partitioning strategies for Azure Search +The ability to search for data is often the primary method of navigation and exploration that's provided by many web applications. It helps users find resources quickly (for example, products in an e-commerce application) based on combinations of search criteria. The Azure Search service provides full-text search capabilities over web content, and includes features such as type-ahead, suggested queries based on near matches, and faceted navigation. A full description of these capabilities is available on the page [What is Azure Search?] on the Microsoft website. + +Azure Search stores searchable content as JSON documents in a database. You define indexes that specify the searchable fields in these documents and provide these definitions to Azure Search. When a user submits a search request, Azure Search uses the appropriate indexes to find matching items. + +To reduce contention, the storage that's used by Azure Search can be divided into 1, 2, 3, 4, 6, or 12 partitions, and each partition can be replicated up to 6 times. The product of the number of partitions multiplied by the number of replicas is called the *search unit* (SU). A single instance of Azure Search can contain a maximum of 36 SUs (a database with 12 partitions only supports a maximum of 3 replicas). + +You are billed for each SU that is allocated to your service. As the volume of searchable content increases or the rate of search requests grows, you can add SUs to an existing instance of Azure Search to handle the extra load. Azure Search itself distributes the documents evenly across the partitions. No manual partitioning strategies are currently supported. + +Each partition can contain a maximum of 15 million documents or occupy 300 GB of storage space (whichever is smaller). You can create up to 50 indexes. The performance of the service varies and depends on the complexity of the documents, the available indexes, and the effects of network latency. On average, a single replica (1 SU) should be able to handle 15 queries per second (QPS), although we recommend performing benchmarking with your own data to obtain a more precise measure of throughput. For more information, see the page [Service limits in Azure Search] on the Microsoft website. + +> [!NOTE] +> You can store a limited set of data types in searchable documents, including strings, Booleans, numeric data, datetime data, and some geographical data. For more details, see the page [Supported data types (Azure Search)] on the Microsoft website. +> +> + +You have limited control over how Azure Search partitions data for each instance of the service. However, in a global environment you might be able to improve performance and reduce latency and contention further by partitioning the service itself using either of the following strategies: + +* Create an instance of Azure Search in each geographic region, and ensure that client applications are directed towards the nearest available instance. This strategy requires that any updates to searchable content are replicated in a timely manner across all instances of the service. +* Create two tiers of Azure Search: + + * A local service in each region that contains the data that's most frequently accessed by users in that region. Users can direct requests here for fast but limited results. + * A global service that encompasses all the data. Users can direct requests here for slower but more complete results. + +This approach is most suitable when there is a significant regional variation in the data that's being searched. + +## Partitioning strategies for Azure Redis Cache +Azure Redis Cache provides a shared caching service in the cloud that's based on the Redis key-value data store. As its name implies, Azure Redis Cache is intended as a caching solution. Use it only for holding transient data and not as a permanent data store. Applications that utilize Azure Redis Cache should be able to continue functioning if the cache is unavailable. Azure Redis Cache supports primary/secondary replication to provide high availability, but currently limits the maximum cache size to 53 GB. If you need more space than this, you must create additional caches. For more information, go to the page [Azure Redis Cache] on the Microsoft website. + +Partitioning a Redis data store involves splitting the data across instances of the Redis service. Each instance constitutes a single partition. Azure Redis Cache abstracts the Redis services behind a façade and does not expose them directly. The simplest way to implement partitioning is to create multiple Azure Redis Cache instances and spread the data across them. + +You can associate each data item with an identifier (a partition key) that specifies which cache stores the data item. The client application logic can then use this identifier to route requests to the appropriate partition. This scheme is very simple, but if the partitioning scheme changes (for example, if additional Azure Redis Cache instances are created), client applications might need to be reconfigured. + +Native Redis (not Azure Redis Cache) supports server-side partitioning based on Redis clustering. In this approach, you can divide the data evenly across servers by using a hashing mechanism. Each Redis server stores metadata that describes the range of hash keys that the partition holds, and also contains information about which hash keys are located in the partitions on other servers. + +Client applications simply send requests to any of the participating Redis servers (probably the closest one). The Redis server examines the client request. If it can be resolved locally, it performs the requested operation. Otherwise it forwards the request on to the appropriate server. + +This model is implemented by using Redis clustering, and is described in more detail on the [Redis cluster tutorial] page on the Redis website. Redis clustering is transparent to client applications. Additional Redis servers can be added to the cluster (and the data can be re-partitioned) without requiring that you reconfigure the clients. + +> [!IMPORTANT] +> Azure Redis Cache does not currently support Redis clustering. If you want to implement this approach with Azure, then you must implement your own Redis servers by installing Redis on a set of Azure virtual machines and configuring them manually. The page [Running Redis on a CentOS Linux VM in Azure] on the Microsoft website walks through an example that shows you how to build and configure a Redis node running as an Azure VM. +> +> + +The page [Partitioning: how to split data among multiple Redis instances] on the Redis website provides more information about implementing partitioning with Redis. The remainder of this section assumes that you are implementing client-side or proxy-assisted partitioning. + +Consider the following points when deciding how to partition data with Azure Redis Cache: + +* Azure Redis Cache is not intended to act as a permanent data store, so whatever partitioning scheme you implement, your application code must be able to retrieve data from a location that's not the cache. +* Data that is frequently accessed together should be kept in the same partition. Redis is a powerful key-value store that provides several highly optimized mechanisms for structuring data. These mechanisms can be one of the following: + + * Simple strings (binary data up to 512 MB in length) + * Aggregate types such as lists (which can act as queues and stacks) + * Sets (ordered and unordered) + * Hashes (which can group related fields together, such as the items that represent the fields in an object) +* The aggregate types enable you to associate many related values with the same key. A Redis key identifies a list, set, or hash rather than the data items that it contains. These types are all available with Azure Redis Cache and are described by the [Data types] page on the Redis website. For example, in part of an e-commerce system that tracks the orders that are placed by customers, the details of each customer can be stored in a Redis hash that is keyed by using the customer ID. Each hash can hold a collection of order IDs for the customer. A separate Redis set can hold the orders, again structured as hashes, and keyed by using the order ID. Figure 10 shows this structure. Note that Redis does not implement any form of referential integrity, so it is the developer's responsibility to maintain the relationships between customers and orders. + +![Suggested structure in Redis storage for recording customer orders and their details](./images/data-partitioning/RedisCustomersandOrders.png) + +*Figure 10. Suggested structure in Redis storage for recording customer orders and their details* + +> [!NOTE] +> In Redis, all keys are binary data values (like Redis strings) and can contain up to 512 MB of data. In theory, a key can contain almost any information. However, we recommend adopting a consistent naming convention for keys that is descriptive of the type of data and that identifies the entity, but is not excessively long. A common approach is to use keys of the form "entity_type:ID". For example, you can use "customer:99" to indicate the key for a customer with the ID 99. +> +> + +* You can implement vertical partitioning by storing related information in different aggregations in the same database. For example, in an e-commerce application, you can store commonly accessed information about products in one Redis hash and less frequently used detailed information in another. + Both hashes can use the same product ID as part of the key. For example, you can use "product: *nn*" (where *nn* is the product ID) for the product information and "product_details: *nn*" for the detailed data. This strategy can help reduce the volume of data that most queries are likely to retrieve. +* You can repartition a Redis data store, but keep in mind that it's a complex and time-consuming task. Redis clustering can repartition data automatically, but this capability is not available with Azure Redis Cache. Therefore, when you design your partitioning scheme, try to leave sufficient free space in each partition to allow for expected data growth over time. However, remember that Azure Redis Cache is intended to cache data temporarily, and that data held in the cache can have a limited lifetime specified as a time-to-live (TTL) value. For relatively volatile data, the TTL can be short, but for static data the TTL can be a lot longer. Avoid storing large amounts of long-lived data in the cache if the volume of this data is likely to fill the cache. You can specify an eviction policy that causes Azure Redis Cache to remove data if space is at a premium. + + > [!NOTE] + > When you use Azure Redis cache, you specify the maximum size of the cache (from 250 MB to 53 GB) by selecting the appropriate pricing tier. However, after an Azure Redis Cache has been created, you cannot increase (or decrease) its size. + > + > +* Redis batches and transactions cannot span multiple connections, so all data that is affected by a batch or transaction should be held in the same database (shard). + + > [!NOTE] + > A sequence of operations in a Redis transaction is not necessarily atomic. The commands that compose a transaction are verified and queued before they run. If an error occurs during this phase, the entire queue is discarded. However, after the transaction has been successfully submitted, the queued commands run in sequence. If any command fails, only that command stops running. All previous and subsequent commands in the queue are performed. For more information, go to the [Transactions] page on the Redis website. + > + > +* Redis supports a limited number of atomic operations. The only operations of this type that support multiple keys and values are MGET and MSET operations. MGET operations return a collection of values for a specified list of keys, and MSET operations store a collection of values for a specified list of keys. If you need to use these operations, the key-value pairs that are referenced by the MSET and MGET commands must be stored within the same database. + +## Rebalancing partitions +As a system matures and you understand the usage patterns better, you might have to adjust the partitioning scheme. For example, individual partitions might start attracting a disproportionate volume of traffic and become hot, leading to excessive contention. Additionally, you might have underestimated the volume of data in some partitions, causing you to approach the limits of the storage capacity in these partitions. Whatever the cause, it is sometimes necessary to rebalance partitions to spread the load more evenly. + +In some cases, data storage systems that don't publicly expose how data is allocated to servers can automatically rebalance partitions within the limits of the resources available. In other situations, rebalancing is an administrative task that consists of two stages: + +1. Determining the new partitioning strategy to ascertain: + * Which partitions might need to be split (or possibly combined). + * How to allocate data to these new partitions by designing new partition keys. +2. Migrating the affected data from the old partitioning scheme to the new set of partitions. + +> [!NOTE] +> The mapping of DocumentDB database collections to servers is transparent, but you can still reach the storage capacity and throughput limits of a DocumentDB account. If this happens, you might need to redesign your partitioning scheme and migrate the data. +> +> + +Depending on the data storage technology and the design of your data storage system, you might be able to migrate data between partitions while they are in use (online migration). If this isn't possible, you might need to make the affected partitions temporarily unavailable while the data is relocated (offline migration). + +## Offline migration +Offline migration is arguably the simplest approach because it reduces the chances of contention occurring. Don't make any changes to the data while it is being moved and restructured. + +Conceptually, this process includes the following steps: + +1. Mark the shard offline. +2. Split-merge and move the data to the new shards. +3. Verify the data. +4. Bring the new shards online. +5. Remove the old shard. + +To retain some availability, you can mark the original shard as read-only in step 1 rather than making it unavailable. This allows applications to read the data while it is being moved but not to change it. + +## Online migration +Online migration is more complex to perform but less disruptive to users because data remains available during the entire procedure. The process is similar to that used by offline migration, except that the original shard is not marked offline (step 1). Depending on the granularity of the migration process (for example, whether it's done item by item or shard by shard), the data access code in the client applications might have to handle reading and writing data that's held in two locations (the original shard and the new shard). + +For an example of a solution that supports online migration, see the article [Scaling using the Elastic Database split-merge tool] on the Microsoft website. + +## Related patterns and guidance +When considering strategies for implementing data consistency, the following patterns might also be relevant to your scenario: + +* The [Data consistency primer] page on the Microsoft website describes strategies for maintaining consistency in a distributed environment such as the cloud. +* The [Data partitioning guidance] page on the Microsoft website provides a general overview of how to design partitions to meet various criteria in a distributed solution. +* The [sharding pattern] as described on the Microsoft website summarizes some common strategies for sharding data. +* The [index table pattern] as described on the Microsoft website illustrates how to create secondary indexes over data. An application can quickly retrieve data with this approach, by using queries that do not reference the primary key of a collection. +* The [materialized view pattern] as described on the Microsoft website describes how to generate pre-populated views that summarize data to support fast query operations. This approach can be useful in a partitioned data store if the partitions that contain the data being summarized are distributed across multiple sites. +* The [Using Azure Content Delivery Network] article on the Microsoft website provides additional guidance on configuring and using Content Delivery Network with Azure. + +## More information +* The page [What is Azure SQL Database?] on the Microsoft website provides detailed documentation that describes how to create and use SQL databases. +* The page [Elastic Database features overview] on the Microsoft website provides a comprehensive introduction to Elastic Database. +* The page [Scaling using the Elastic Database split-merge tool] on the Microsoft website contains information about using the split-merge service to manage Elastic Database shards. +* The page [Azure storage scalability and performance targets](https://msdn.microsoft.com/library/azure/dn249410.aspx) on the Microsoft website documents the current sizing and throughput limits of Azure Storage. +* The page [Performing entity group transactions] on the Microsoft website provides detailed information about implementing transactional operations over entities that are stored in Azure table storage. +* The article [Azure Storage table design guide] on the Microsoft website contains detailed information about partitioning data in Azure table storage. +* The page [Using Azure Content Delivery Network] on the Microsoft website describes how to replicate data that's held in Azure blob storage by using the Azure Content Delivery Network. +* The page [Manage DocumentDB capacity needs] on the Microsoft website contains information about how Azure DocumentDB databases allocate resources. +* The page [What is Azure Search?] on the Microsoft website provides a full description of the capabilities that are available in Azure Search. +* The page [Service limits in Azure Search] on the Microsoft website contains information about the capacity of each instance of Azure Search. +* The page [Supported data types (Azure Search)] on the Microsoft website summarizes the data types that you can use in searchable documents and indexes. +* The page [Azure Redis Cache] on the Microsoft website provides an introduction to Azure Redis Cache. +* The [Partitioning: how to split data among multiple Redis instances] page on the Redis website provides information about how to implement partitioning with Redis. +* The page [Running Redis on a CentOS Linux VM in Azure] on the Microsoft website walks through an example that shows you how to build and configure a Redis node running as an Azure VM. +* The [Data types] page on the Redis website describes the data types that are available with Redis and Azure Redis Cache. + +[Azure Redis Cache]: http://azure.microsoft.com/services/cache/ +[Azure Storage Scalability and Performance Targets]: /azure/storage/storage-scalability-targets +[Azure Storage Table Design Guide]: /azure/storage/storage-table-design-guide +[Building a Polyglot Solution]: https://msdn.microsoft.com/library/dn313279.aspx +[Data Access for Highly-Scalable Solutions: Using SQL, NoSQL, and Polyglot Persistence]: https://msdn.microsoft.com/library/dn271399.aspx +[Data consistency primer]: http://aka.ms/Data-Consistency-Primer +[Data Partitioning Guidance]: https://msdn.microsoft.com/library/dn589795.aspx +[Data Types]: http://redis.io/topics/data-types +[DocumentDB limits and quotas]: /azure/documentdb/documentdb-limits +[Elastic Database features overview]: /azure/sql-database/sql-database-elastic-scale-introduction +[Federations Migration Utility]: https://code.msdn.microsoft.com/vstudio/Federations-Migration-ce61e9c1 +[Index Table Pattern]: http://aka.ms/Index-Table-Pattern +[Manage DocumentDB capacity needs]: /azure/documentdb/documentdb-manage +[Materialized View Pattern]: http://aka.ms/Materialized-View-Pattern +[Multi-shard querying]: /azure/sql-database/sql-database-elastic-scale-multishard-querying +[Partitioning: how to split data among multiple Redis instances]: http://redis.io/topics/partitioning +[Performance levels in DocumentDB]: /azure/documentdb/documentdb-performance-levels +[Performing Entity Group Transactions]: https://msdn.microsoft.com/library/azure/dd894038.aspx +[Redis cluster tutorial]: http://redis.io/topics/cluster-tutorial +[Running Redis on a CentOS Linux VM in Azure]: http://blogs.msdn.com/b/tconte/archive/2012/06/08/running-redis-on-a-centos-linux-vm-in-windows-azure.aspx +[Scaling using the Elastic Database split-merge tool]: /azure/sql-database/sql-database-elastic-scale-overview-split-and-merge +[Using Azure Content Delivery Network]: /azure/cdn/cdn-create-new-endpoint +[Service Bus quotas]: /azure/service-bus-messaging/service-bus-quotas +[Service limits in Azure Search]: /azure/search/search-limits-quotas-capacity +[Sharding pattern]: http://aka.ms/Sharding-Pattern +[Supported Data Types (Azure Search)]: https://msdn.microsoft.com/library/azure/dn798938.aspx +[Transactions]: 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-0,0 +1,3 @@ +--- +redirect_url: /azure/architecture/best-practices/api-design +--- diff --git a/docs/best-practices/monitoring.md b/docs/best-practices/monitoring.md new file mode 100644 index 00000000000..58425865a22 --- /dev/null +++ b/docs/best-practices/monitoring.md @@ -0,0 +1,713 @@ +--- +title: Monitoring and diagnostics guidance +description: Best practices for monitoring distributed applications in the cloud. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 2d2a8497-73d0-4a46-aac6-6d504003de2b +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# Monitoring and diagnostics +[!INCLUDE [header](../_includes/header.md)] + +Distributed applications and services running in the cloud are, by their nature, complex pieces of software that comprise many moving parts. In a production environment, it's important to be able to track the way in which users utilize your system, trace resource utilization, and generally monitor the health and performance of your system. You can use this information as a diagnostic aid to detect and correct issues, and also to help spot potential problems and prevent them from occurring. + +## Monitoring and diagnostics scenarios +You can use monitoring to gain an insight into how well a system is functioning. Monitoring is a crucial part of maintaining quality-of-service targets. Common scenarios for collecting monitoring data include: + +* Ensuring that the system remains healthy. +* Tracking the availability of the system and its component elements. +* Maintaining performance to ensure that the throughput of the system does not degrade unexpectedly as the volume of work increases. +* Guaranteeing that the system meets any service-level agreements (SLAs) established with customers. +* Protecting the privacy and security of the system, users, and their data. +* Tracking the operations that are performed for auditing or regulatory purposes. +* Monitoring the day-to-day usage of the system and spotting trends that might lead to problems if they're not addressed. +* Tracking issues that occur, from initial report through to analysis of possible causes, rectification, consequent software updates, and deployment. +* Tracing operations and debugging software releases. + +> [!NOTE] +> This list is not intended to be comprehensive. This document focuses on these scenarios as the most common situations for performing monitoring. There might be others that are less common or are specific to your environment. +> +> + +The following sections describe these scenarios in more detail. The information for each scenario is discussed in the following format: + +1. A brief overview of the scenario +2. The typical requirements of this scenario +3. The raw instrumentation data that's required to support the scenario, and possible sources of this information +4. How this raw data can be analyzed and combined to generate meaningful diagnostic information + +## Health monitoring +A system is healthy if it is running and capable of processing requests. The purpose of health monitoring is to generate a snapshot of the current health of the system so that you can verify that all components of the system are functioning as expected. + +### Requirements for health monitoring +An operator should be alerted quickly (within a matter of seconds) if any part of the system is deemed to be unhealthy. The operator should be able to ascertain which parts of the system are functioning normally, and which parts are experiencing problems. System health can be highlighted through a traffic-light system: + +* Red for unhealthy (the system has stopped) +* Yellow for partially healthy (the system is running with reduced functionality) +* Green for completely healthy + +A comprehensive health-monitoring system enables an operator to drill down through the system to view the health status of subsystems and components. For example, if the overall system is depicted as partially healthy, the operator should be able to zoom in and determine which functionality is currently unavailable. + +### Data sources, instrumentation, and data-collection requirements +The raw data that's required to support health monitoring can be generated as a result of: + +* Tracing execution of user requests. This information can be used to determine which requests have succeeded, which have failed, and how long each request takes. +* Synthetic user monitoring. This process simulates the steps performed by a user and follows a predefined series of steps. The results of each step should be captured. +* Logging exceptions, faults, and warnings. This information can be captured as a result of trace statements embedded into the application code, as well as retrieving information from the event logs of any services that the system references. +* Monitoring the health of any third-party services that the system uses. This monitoring might require retrieving and parsing health data that these services supply. This information might take a variety of formats. +* Endpoint monitoring. This mechanism is described in more detail in the "Availability monitoring" section. +* Collecting ambient performance information, such as background CPU utilization or I/O (including network) activity. + +### Analyzing health data +The primary focus of health monitoring is to quickly indicate whether the system is running. Hot analysis of the immediate data can trigger an alert if a critical component is detected as unhealthy. (It fails to respond to a consecutive series of pings, for example.) The operator can then take the appropriate corrective action. + +A more advanced system might include a predictive element that performs a cold analysis over recent and current workloads. A cold analysis can spot trends and determine whether the system is likely to remain healthy or whether the system will need additional resources. This predictive element should be based on critical performance metrics, such as: + +* The rate of requests directed at each service or subsystem. +* The response times of these requests. +* The volume of data flowing into and out of each service. + +If the value of any metric exceeds a defined threshold, the system can raise an alert to enable an operator or autoscaling (if available) to take the preventative actions necessary to maintain system health. These actions might involve adding resources, restarting one or more services that are failing, or applying throttling to lower-priority requests. + +## Availability monitoring +A truly healthy system requires that the components and subsystems that compose the system are available. Availability monitoring is closely related to health monitoring. But whereas health monitoring provides an immediate view of the current health of the system, availability monitoring is concerned with tracking the availability of the system and its components to generate statistics about the uptime of the system. + +In many systems, some components (such as a database) are configured with built-in redundancy to permit rapid failover in the event of a serious fault or loss of connectivity. Ideally, users should not be aware that such a failure has occurred. But from an availability monitoring perspective, it's necessary to gather as much information as possible about such failures to determine the cause and take corrective actions to prevent them from recurring. + +The data that's required to track availability might depend on a number of lower-level factors. Many of these factors might be specific to the application, system, and environment. An effective monitoring system captures the availability data that corresponds to these low-level factors and then aggregates them to give an overall picture of the system. For example, in an e-commerce system, the business functionality that enables a customer to place orders might depend on the repository where order details are stored and the payment system that handles the monetary transactions for paying for these orders. The availability of the order-placement part of the system is therefore a function of the availability of the repository and the payment subsystem. + +### Requirements for availability monitoring +An operator should also be able to view the historical availability of each system and subsystem, and use this information to spot any trends that might cause one or more subsystems to periodically fail. (Do services start to fail at a particular time of day that corresponds to peak processing hours?) + +A monitoring solution should provide an immediate and historical view of the availability or unavailability of each subsystem. It should also be capable of quickly alerting an operator when one or more services fail or when users can't connect to services. This is a matter of not only monitoring each service, but also examining the actions that each user performs if these actions fail when they attempt to communicate with a service. To some extent, a degree of connectivity failure is normal and might be due to transient errors. But it might be useful to allow the system to raise an alert for the number of connectivity failures to a specified subsystem that occur during a specific period. + +### Data sources, instrumentation, and data-collection requirements +As with health monitoring, the raw data that's required to support availability monitoring can be generated as a result of synthetic user monitoring and logging any exceptions, faults, and warnings that might occur. In addition, availability data can be obtained from performing endpoint monitoring. The application can expose one or more health endpoints, each testing access to a functional area within the system. The monitoring system can ping each endpoint by following a defined schedule and collect the results (success or fail). + +All timeouts, network connectivity failures, and connection retry attempts must be recorded. All data should be time-stamped. + + + +### Analyzing availability data +The instrumentation data must be aggregated and correlated to support the following types of analysis: + +* The immediate availability of the system and subsystems. +* The availability failure rates of the system and subsystems. Ideally, an operator should be able to correlate failures with specific activities: what was happening when the system failed? +* A historical view of failure rates of the system or any subsystems across any specified period, and the load on the system (number of user requests, for example) when a failure occurred. +* The reasons for unavailability of the system or any subsystems. For example, the reasons might be service not running, connectivity lost, connected but timing out, and connected but returning errors. + +You can calculate the percentage availability of a service over a period of time by using the following formula: + +``` +%Availability = ((Total Time – Total Downtime) / Total Time ) * 100 +``` + +This is useful for SLA purposes. ([SLA monitoring](#SLA-monitoring) is described in more detail later in this guidance.) The definition of *downtime* depends on the service. For example, Visual Studio Team Services Build Service defines downtime as the period (total accumulated minutes) during which Build Service is unavailable. A minute is considered unavailable if all continuous HTTP requests to Build Service to perform customer-initiated operations throughout the minute either result in an error code or do not return a response. + +## Performance monitoring +As the system is placed under more and more stress (by increasing the volume of users), the size of the datasets that these users access grows and the possibility of failure of one or more components becomes more likely. Frequently, component failure is preceded by a decrease in performance. If you're able detect such a decrease, you can take proactive steps to remedy the situation. + +System performance depends on a number of factors. Each factor is typically measured through key performance indicators (KPIs), such as the number of database transactions per second or the volume of network requests that are successfully serviced in a specified time frame. Some of these KPIs might be available as specific performance measures, whereas others might be derived from a combination of metrics. + +> [!NOTE] +> Determining poor or good performance requires that you understand the level of performance at which the system should be capable of running. This requires observing the system while it's functioning under a typical load and capturing the data for each KPI over a period of time. This might involve running the system under a simulated load in a test environment and gathering the appropriate data before deploying the system to a production environment. +> +> You should also ensure that monitoring for performance purposes does not become a burden on the system. You might be able to dynamically adjust the level of detail for the data that the performance monitoring process gathers. +> +> + +### Requirements for performance monitoring +To examine system performance, an operator typically needs to see information that includes: + +* The response rates for user requests. +* The number of concurrent user requests. +* The volume of network traffic. +* The rates at which business transactions are being completed. +* The average processing time for requests. + +It can also be helpful to provide tools that enable an operator to help spot correlations, such as: + +* The number of concurrent users versus request latency times (how long it takes to start processing a request after the user has sent it). +* The number of concurrent users versus the average response time (how long it takes to complete a request after it has started processing). +* The volume of requests versus the number of processing errors. + +Along with this high-level functional information, an operator should be able to obtain a detailed view of the performance for each component in the system. This data is typically provided through low-level performance counters that track information such as: + +* Memory utilization. +* Number of threads. +* CPU processing time. +* Request queue length. +* Disk or network I/O rates and errors. +* Number of bytes written or read. +* Middleware indicators, such as queue length. + +All visualizations should allow an operator to specify a time period. The displayed data might be a snapshot of the current situation and/or a historical view of the performance. + +An operator should be able to raise an alert based on any performance measure for any specified value during any specified time interval. + +### Data sources, instrumentation, and data-collection requirements +You can gather high-level performance data (throughput, number of concurrent users, number of business transactions, error rates, and so on) by monitoring the progress of users' requests as they arrive and pass through the system. This involves incorporating tracing statements at key points in the application code, together with timing information. All faults, exceptions, and warnings should be captured with sufficient data for correlating them with the requests that caused them. The Internet Information Services (IIS) log is another useful source. + +If possible, you should also capture performance data for any external systems that the application uses. These external systems might provide their own performance counters or other features for requesting performance data. If this is not possible, record information such as the start time and end time of each request made to an external system, together with the status (success, fail, or warning) of the operation. For example, you can use a stopwatch approach to time requests: start a timer when the request starts and then stop the timer when the request finishes. + +Low-level performance data for individual components in a system might be available through features and services such as Windows performance counters and Azure Diagnostics. + +### Analyzing performance data +Much of the analysis work consists of aggregating performance data by user request type and/or the subsystem or service to which each request is sent. An example of a user request is adding an item to a shopping cart or performing the checkout process in an e-commerce system. + +Another common requirement is summarizing performance data in selected percentiles. For example, an operator might determine the response times for 99 percent of requests, 95 percent of requests, and 70 percent of requests. There might be SLA targets or other goals set for each percentile. The ongoing results should be reported in near real time to help detect immediate issues. The results should also be aggregated over the longer time for statistical purposes. + +In the case of latency issues affecting performance, an operator should be able to quickly identify the cause of the bottleneck by examining the latency of each step that each request performs. The performance data must therefore provide a means of correlating performance measures for each step to tie them to a specific request. + +Depending on the visualization requirements, it might be useful to generate and store a data cube that contains views of the raw data. This data cube can allow complex ad hoc querying and analysis of the performance information. + +## Security monitoring +All commercial systems that include sensitive data must implement a security structure. The complexity of the security mechanism is usually a function of the sensitivity of the data. In a system that requires users to be authenticated, you should record: + +* All sign-in attempts, whether they fail or succeed. +* All operations performed by--and the details of all resources accessed by--an authenticated user. +* When a user ends a session and signs out. + +Monitoring might be able to help detect attacks on the system. For example, a large number of failed sign-in attempts might indicate a brute-force attack. An unexpected surge in requests might be the result of a distributed denial-of-service (DDoS) attack. You must be prepared to monitor all requests to all resources regardless of the source of these requests. A system that has a sign-in vulnerability might accidentally expose resources to the outside world without requiring a user to actually sign in. + +### Requirements for security monitoring +The most critical aspects of security monitoring should enable an operator to quickly: + +* Detect attempted intrusions by an unauthenticated entity. +* Identify attempts by entities to perform operations on data for which they have not been granted access. +* Determine whether the system, or some part of the system, is under attack from outside or inside. (For example, a malicious authenticated user might be attempting to bring the system down.) + +To support these requirements, an operator should be notified: + +* If one account makes repeated failed sign-in attempts within a specified period. +* If one authenticated account repeatedly tries to access a prohibited resource during a specified period. +* If a large number of unauthenticated or unauthorized requests occur during a specified period. + +The information that's provided to an operator should include the host address of the source for each request. If security violations regularly arise from a particular range of addresses, these hosts might be blocked. + +A key part in maintaining the security of a system is being able to quickly detect actions that deviate from the usual pattern. Information such as the number of failed and/or successful sign-in requests can be displayed visually to help detect whether there is a spike in activity at an unusual time. (An example of this activity is users signing in at 3:00 AM and performing a large number of operations when their working day starts at 9:00 AM). This information can also be used to help configure time-based autoscaling. For example, if an operator observes that a large number of users regularly sign in at a particular time of day, the operator can arrange to start additional authentication services to handle the volume of work, and then shut down these additional services when the peak has passed. + +### Data sources, instrumentation, and data-collection requirements +Security is an all-encompassing aspect of most distributed systems. The pertinent data is likely to be generated at multiple points throughout a system. You should consider adopting a Security Information and Event Management (SIEM) approach to gather the security-related information that results from events raised by the application, network equipment, servers, firewalls, antivirus software, and other intrusion-prevention elements. + +Security monitoring can incorporate data from tools that are not part of your application. These tools can include utilities that identify port-scanning activities by external agencies, or network filters that detect attempts to gain unauthenticated access to your application and data. + +In all cases, the gathered data must enable an administrator to determine the nature of any attack and take the appropriate countermeasures. + +### Analyzing security data +A feature of security monitoring is the variety of sources from which the data arises. The different formats and level of detail often require complex analysis of the captured data to tie it together into a coherent thread of information. Apart from the simplest of cases (such as detecting a large number of failed sign-ins, or repeated attempts to gain unauthorized access to critical resources), it might not be possible to perform any complex automated processing of security data. Instead, it might be preferable to write this data, time-stamped but otherwise in its original form, to a secure repository to allow for expert manual analysis. + + + +## SLA monitoring +Many commercial systems that support paying customers make guarantees about the performance of the system in the form of SLAs. Essentially, SLAs state that the system can handle a defined volume of work within an agreed time frame and without losing critical information. SLA monitoring is concerned with ensuring that the system can meet measurable SLAs. + +> [!NOTE] +> SLA monitoring is closely related to performance monitoring. But whereas performance monitoring is concerned with ensuring that the system functions *optimally*, SLA monitoring is governed by a contractual obligation that defines what *optimally* actually means. +> +> + +SLAs are often defined in terms of: + +* Overall system availability. For example, an organization might guarantee that the system will be available for 99.9 percent of the time. This equates to no more than 9 hours of downtime per year, or approximately 10 minutes a week. +* Operational throughput. This aspect is often expressed as one or more high-water marks, such as guaranteeing that the system can support up to 100,000 concurrent user requests or handle 10,000 concurrent business transactions. +* Operational response time. The system might also make guarantees for the rate at which requests are processed. An example is that 99 percent of all business transactions will finish within 2 seconds, and no single transaction will take longer than 10 seconds. + +> [!NOTE] +> Some contracts for commercial systems might also include SLAs for customer support. An example is that all help-desk requests will elicit a response within 5 minutes, and that 99 percent of all problems will be fully addressed within 1 working day. Effective [issue tracking](#issue-tracking) (described later in this section) is key to meeting SLAs such as these. +> +> + +### Requirements for SLA monitoring +At the highest level, an operator should be able to determine at a glance whether the system is meeting the agreed SLAs or not. And if not, the operator should be able to drill down and examine the underlying factors to determine the reasons for substandard performance. + +Typical high-level indicators that can be depicted visually include: + +* The percentage of service uptime. +* The application throughput (measured in terms of successful transactions and/or operations per second). +* The number of successful/failing application requests. +* The number of application and system faults, exceptions, and warnings. + +All of these indicators should be capable of being filtered by a specified period of time. + +A cloud application will likely comprise a number of subsystems and components. An operator should be able to select a high-level indicator and see how it's composed from the health of the underlying elements. For example, if the uptime of the overall system falls below an acceptable value, an operator should be able to zoom in and determine which elements are contributing to this failure. + +> [!NOTE] +> System uptime needs to be defined carefully. In a system that uses redundancy to ensure maximum availability, individual instances of elements might fail, but the system can remain functional. System uptime as presented by health monitoring should indicate the aggregate uptime of each element and not necessarily whether the system has actually halted. Additionally, failures might be isolated. So even if a specific system is unavailable, the remainder of the system might remain available, although with decreased functionality. (In an e-commerce system, a failure in the system might prevent a customer from placing orders, but the customer might still be able to browse the product catalog.) +> +> + +For alerting purposes, the system should be able to raise an event if any of the high-level indicators exceed a specified threshold. The lower-level details of the various factors that compose the high-level indicator should be available as contextual data to the alerting system. + +### Data sources, instrumentation, and data-collection requirements +The raw data that's required to support SLA monitoring is similar to the raw data that's required for performance monitoring, together with some aspects of health and availability monitoring. (See those sections for more details.) You can capture this data by: + +* Performing endpoint monitoring. +* Logging exceptions, faults, and warnings. +* Tracing the execution of user requests. +* Monitoring the availability of any third-party services that the system uses. +* Using performance metrics and counters. + +All data must be timed and time-stamped. + +### Analyzing SLA data +The instrumentation data must be aggregated to generate a picture of the overall performance of the system. Aggregated data must also support drill-down to enable examination of the performance of the underlying subsystems. For example, you should be able to: + +* Calculate the total number of user requests during a specified period and determine the success and failure rate of these requests. +* Combine the response times of user requests to generate an overall view of system response times. +* Analyze the progress of user requests to break down the overall response time of a request into the response times of the individual work items in that request. +* Determine the overall availability of the system as a percentage of uptime for any specific period. +* Analyze the percentage time availability of the individual components and services in the system. This might involve parsing logs that third-party services have generated. + +Many commercial systems are required to report real performance figures against agreed SLAs for a specified period, typically a month. This information can be used to calculate credits or other forms of repayments for customers if the SLAs are not met during that period. You can calculate availability for a service by using the technique described in the section [Analyzing availability data](#analyzing-availability-data). + +For internal purposes, an organization might also track the number and nature of incidents that caused services to fail. Learning how to resolve these issues quickly, or eliminate them completely, will help to reduce downtime and meet SLAs. + +## Auditing +Depending on the nature of the application, there might be statutory or other legal regulations that specify requirements for auditing users' operations and recording all data access. Auditing can provide evidence that links customers to specific requests. Non-repudiation is an important factor in many e-business systems to help maintain trust be between a customer and the organization that's responsible for the application or service. + +### Requirements for auditing +An analyst must be able to trace the sequence of business operations that users are performing so that you can reconstruct users' actions. This might be necessary simply as a matter of record, or as part of a forensic investigation. + +Audit information is highly sensitive. It will likely include data that identifies the users of the system, together with the tasks that they're performing. For this reason, audit information will most likely take the form of reports that are available only to trusted analysts rather than as an interactive system that supports drill-down of graphical operations. An analyst should be able to generate a range of reports. For example, reports might list all users' activities occurring during a specified time frame, detail the chronology of activity for a single user, or list the sequence of operations performed against one or more resources. + +### Data sources, instrumentation, and data-collection requirements +The primary sources of information for auditing can include: + +* The security system that manages user authentication. +* Trace logs that record user activity. +* Security logs that track all identifiable and unidentifiable network requests. + +The format of the audit data and the way in which it's stored might be driven by regulatory requirements. For example, it might not be possible to clean the data in any way. (It must be recorded in its original format.) Access to the repository where it's held must be protected to prevent tampering. + +### Analyzing audit data +An analyst must be able to access the raw data in its entirety, in its original form. Aside from the requirement to generate common audit reports, the tools for analyzing this data are likely to be specialized and kept external to the system. + +## Usage monitoring +Usage monitoring tracks how the features and components of an application are used. An operator can use the gathered data to: + +* Determine which features are heavily used and determine any potential hotspots in the system. High-traffic elements might benefit from functional partitioning or even replication to spread the load more evenly. An operator can also use this information to ascertain which features are infrequently used and are possible candidates for retirement or replacement in a future version of the system. +* Obtain information about the operational events of the system under normal use. For example, in an e-commerce site, you can record the statistical information about the number of transactions and the volume of customers that are responsible for them. This information can be used for capacity planning as the number of customers grows. +* Detect (possibly indirectly) user satisfaction with the performance or functionality of the system. For example, if a large number of customers in an e-commerce system regularly abandon their shopping carts, this might be due to a problem with the checkout functionality. +* Generate billing information. A commercial application or multitenant service might charge customers for the resources that they use. +* Enforce quotas. If a user in a multitenant system exceeds their paid quota of processing time or resource usage during a specified period, their access can be limited or processing can be throttled. + +### Requirements for usage monitoring +To examine system usage, an operator typically needs to see information that includes: + +* The number of requests that are processed by each subsystem and directed to each resource. +* The work that each user is performing. +* The volume of data storage that each user occupies. +* The resources that each user is accessing. + +An operator should also be able to generate graphs. For example, a graph might display the most resource-hungry users, or the most frequently accessed resources or system features. + +### Data sources, instrumentation, and data-collection requirements +Usage tracking can be performed at a relatively high level. It can note the start and end times of each request and the nature of the request (read, write, and so on, depending on the resource in question). You can obtain this information by: + +* Tracing user activity. +* Capturing performance counters that measure the utilization for each resource. +* Monitoring the resource consumption by each user. + +For metering purposes, you also need to be able to identify which users are responsible for performing which operations, and the resources that these operations utilize. The gathered information should be detailed enough to enable accurate billing. + + + +## Issue tracking +Customers and other users might report issues if unexpected events or behavior occurs in the system. Issue tracking is concerned with managing these issues, associating them with efforts to resolve any underlying problems in the system, and informing customers of possible resolutions. + +### Requirements for issue tracking +Operators often perform issue tracking by using a separate system that enables them to record and report the details of problems that users report. These details can include the tasks that the user was trying to perform, symptoms of the problem, the sequence of events, and any error or warning messages that were issued. + +### Data sources, instrumentation, and data-collection requirements +The initial data source for issue-tracking data is the user who reported the issue in the first place. The user might be able to provide additional data such as: + +* A crash dump (if the application includes a component that runs on the user's desktop). +* A screen snapshot. +* The date and time when the error occurred, together with any other environmental information such as the user's location. + +This information can be used to help the debugging effort and help construct a backlog for future releases of the software. + +### Analyzing issue-tracking data +Different users might report the same problem. The issue-tracking system should associate common reports. + +The progress of the debugging effort should be recorded against each issue report. When the problem is resolved, the customer can be informed of the solution. + +If a user reports an issue that has a known solution in the issue-tracking system, the operator should be able to inform the user of the solution immediately. + +## Tracing operations and debugging software releases +When a user reports an issue, the user is often only aware of the immediate impact that it has on their operations. The user can only report the results of their own experience back to an operator who is responsible for maintaining the system. These experiences are usually just a visible symptom of one or more fundamental problems. In many cases, an analyst will need to dig through the chronology of the underlying operations to establish the root cause of the problem. This process is called *root cause analysis*. + +> [!NOTE] +> Root cause analysis might uncover inefficiencies in the design of an application. In these situations, it might be possible to rework the affected elements and deploy them as part of a subsequent release. This process requires careful control, and the updated components should be monitored closely. +> +> + +### Requirements for tracing and debugging +For tracing unexpected events and other problems, it's vital that the monitoring data provides enough information to enable an analyst to trace back to the origins of these issues and reconstruct the sequence of events that occurred. This information must be sufficient to enable an analyst to diagnose the root cause of any problems. A developer can then make the necessary modifications to prevent them from recurring. + +### Data sources, instrumentation, and data-collection requirements +Troubleshooting can involve tracing all the methods (and their parameters) invoked as part of an operation to build up a tree that depicts the logical flow through the system when a customer makes a specific request. Exceptions and warnings that the system generates as a result of this flow need to be captured and logged. + +To support debugging, the system can provide hooks that enable an operator to capture state information at crucial points in the system. Or, the system can deliver detailed step-by-step information as selected operations progress. Capturing data at this level of detail can impose an additional load on the system and should be a temporary process. An operator uses this process mainly when a highly unusual series of events occurs and is difficult to replicate, or when a new release of one or more elements into a system requires careful monitoring to ensure that the elements function as expected. + +## The monitoring and diagnostics pipeline +Monitoring a large-scale distributed system poses a significant challenge. Each of the scenarios described in the previous section should not necessarily be considered in isolation. There is likely to be a significant overlap in the monitoring and diagnostic data that's required for each situation, although this data might need to be processed and presented in different ways. For these reasons, you should take a holistic view of monitoring and diagnostics. + +You can envisage the entire monitoring and diagnostics process as a pipeline that comprises the stages shown in Figure 1. + +![Stages in the monitoring and diagnostics pipeline](./images/monitoring/Pipeline.png) + +*Figure 1. +The stages in the monitoring and diagnostics pipeline* + +Figure 1 highlights how the data for monitoring and diagnostics can come from a variety of data sources. The instrumentation and collection stages are concerned with identifying the sources from where the data needs to be captured, determining which data to capture, how to capture it, and how to format this data so that it can be easily examined. The analysis/diagnosis stage takes the raw data and uses it to generate meaningful information that an operator can use to determine the state of the system. The operator can use this information to make decisions about possible actions to take, and then feed the results back into the instrumentation and collection stages. The visualization/alerting stage phase presents a consumable view of the system state. It can display information in near real time by using a series of dashboards. And it can generate reports, graphs, and charts to provide a historical view of the data that can help identify long-term trends. If information indicates that a KPI is likely to exceed acceptable bounds, this stage can also trigger an alert to an operator. In some cases, an alert can also be used to trigger an automated process that attempts to take corrective actions, such as autoscaling. + +Note that these steps constitute a continuous-flow process where the stages are happening in parallel. Ideally, all the phases should be dynamically configurable. At some points, especially when a system has been newly deployed or is experiencing problems, it might be necessary to gather extended data on a more frequent basis. At other times, it should be possible to revert to capturing a base level of essential information to verify that the system is functioning properly. + +Additionally, the entire monitoring process should be considered a live, ongoing solution that's subject to fine-tuning and improvements as a result of feedback. For example, you might start with measuring many factors to determine system health. Analysis over time might lead to a refinement as you discard measures that aren't relevant, enabling you to more precisely focus on the data that you need while minimizing background noise. + +## Sources of monitoring and diagnostic data +The information that the monitoring process uses can come from several sources, as illustrated in Figure 1. At the application level, information comes from trace logs incorporated into the code of the system. Developers should follow a standard approach for tracking the flow of control through their code. For example, an entry to a method can emit a trace message that specifies the name of the method, the current time, the value of each parameter, and any other pertinent information. Recording the entry and exit times can also prove useful. + +You should log all exceptions and warnings, and ensure that you retain a full trace of any nested exceptions and warnings. Ideally, you should also capture information that identifies the user who is running the code, together with activity correlation information (to track requests as they pass through the system). And you should log attempts to access all resources such as message queues, databases, files, and other dependent services. This information can be used for metering and auditing purposes. + +Many applications make use of libraries and frameworks to perform common tasks such as accessing a data store or communicating over a network. These frameworks might be configurable to provide their own trace messages and raw diagnostic information, such as transaction rates and data transmission successes and failures. + +> [!NOTE] +> Many modern frameworks automatically publish performance and trace events. Capturing this information is simply a matter of providing a means to retrieve and store it where it can be processed and analyzed. +> +> + +The operating system where the application is running can be a source of low-level system-wide information, such as performance counters that indicate I/O rates, memory utilization, and CPU usage. Operating system errors (such as the failure to open a file correctly) might also be reported. + +You should also consider the underlying infrastructure and components on which your system runs. Virtual machines, virtual networks, and storage services can all be sources of important infrastructure-level performance counters and other diagnostic data. + +If your application uses other external services, such as a web server or database management system, these services might publish their own trace information, logs, and performance counters. Examples include SQL Server Dynamic Management Views for tracking operations performed against a SQL Server database, and IIS trace logs for recording requests made to a web server. + +As the components of a system are modified and new versions are deployed, it's important to be able to attribute issues, events, and metrics to each version. This information should be tied back to the release pipeline so that problems with a specific version of a component can be tracked quickly and rectified. + +Security issues might occur at any point in the system. For example, a user might attempt to sign in with an invalid user ID or password. An authenticated user might try to obtain unauthorized access to a resource. Or a user might provide an invalid or outdated key to access encrypted information. Security-related information for successful and failing requests should always be logged. + +The section [Instrumenting an application](#instrumenting-an-application) contains more guidance on the information that you should capture. But you can use a variety of strategies to gather this information: + +* **Application/system monitoring**. This strategy uses internal sources within the application, application frameworks, operating system, and infrastructure. The application code can generate its own monitoring data at notable points during the lifecycle of a client request. The application can include tracing statements that might be selectively enabled or disabled as circumstances dictate. It might also be possible to inject diagnostics dynamically by using a diagnostics framework. These frameworks typically provide plug-ins that can attach to various instrumentation points in your code and capture trace data at these points. + + Additionally, your code and/or the underlying infrastructure might raise events at critical points. Monitoring agents that are configured to listen for these events can record the event information. +* **Real user monitoring**. This approach records the interactions between a user and the application and observes the flow of each request and response. This information can have a two-fold purpose: it can be used for metering usage by each user, and it can be used to determine whether users are receiving a suitable quality of service (for example, fast response times, low latency, and minimal errors). You can use the captured data to identify areas of concern where failures occur most often. You can also use the data to identify elements where the system slows down, possibly due to hotspots in the application or some other form of bottleneck. If you implement this approach carefully, it might be possible to reconstruct users' flows through the application for debugging and testing purposes. + + > [!IMPORTANT] + > You should consider the data that's captured by monitoring real users to be highly sensitive because it might include confidential material. If you save captured data, store it securely. If you want to use the data for performance monitoring or debugging purposes, strip out all personally identifiable information first. + > + > +* **Synthetic user monitoring**. In this approach, you write your own test client that simulates a user and performs a configurable but typical series of operations. You can track the performance of the test client to help determine the state of the system. You can also use multiple instances of the test client as part of a load-testing operation to establish how the system responds under stress, and what sort of monitoring output is generated under these conditions. + + > [!NOTE] + > You can implement real and synthetic user monitoring by including code that traces and times the execution of method calls and other critical parts of an application. + > + > +* **Profiling**. This approach is primarily targeted at monitoring and improving application performance. Rather than operating at the functional level of real and synthetic user monitoring, it captures lower-level information as the application runs. You can implement profiling by using periodic sampling of the execution state of an application (determining which piece of code that the application is running at a given point in time). You can also use instrumentation that inserts probes into the code at important junctures (such as the start and end of a method call) and records which methods were invoked, at what time, and how long each call took. You can then analyze this data to determine which parts of the application might cause performance problems. +* **Endpoint monitoring**. This technique uses one or more diagnostic endpoints that the application exposes specifically to enable monitoring. An endpoint provides a pathway into the application code and can return information about the health of the system. Different endpoints can focus on various aspects of the functionality. You can write your own diagnostics client that sends periodic requests to these endpoints and assimilate the responses. This approach is described more in [Health Endpoint Monitoring Pattern](https://msdn.microsoft.com/library/dn589789.aspx) on the Microsoft website. + +For maximum coverage, you should use a combination of these techniques. + + + +## Instrumenting an application +Instrumentation is a critical part of the monitoring process. You can make meaningful decisions about the performance and health of a system only if you first capture the data that enables you to make these decisions. The information that you gather by using instrumentation should be sufficient to enable you to assess performance, diagnose problems, and make decisions without requiring you to sign in to a remote production server to perform tracing (and debugging) manually. Instrumentation data typically comprises metrics and information that's written to trace logs. + +The contents of a trace log can be the result of textual data that's written by the application or binary data that's created as the result of a trace event (if the application is using Event Tracing for Windows--ETW). They can also be generated from system logs that record events arising from parts of the infrastructure, such as a web server. Textual log messages are often designed to be human-readable, but they should also be written in a format that enables an automated system to parse them easily. + +You should also categorize logs. Don't write all trace data to a single log, but use separate logs to record the trace output from different operational aspects of the system. You can then quickly filter log messages by reading from the appropriate log rather than having to process a single lengthy file. Never write information that has different security requirements (such as audit information and debugging data) to the same log. + +> [!NOTE] +> A log might be implemented as a file on the file system, or it might be held in some other format, such as a blob in blob storage. Log information might also be held in more structured storage, such as rows in a table. +> +> + +Metrics will generally be a measure or count of some aspect or resource in the system at a specific time, with one or more associated tags or dimensions (sometimes called a *sample*). A single instance of a metric is usually not useful in isolation. Instead, metrics have to be captured over time. The key issue to consider is which metrics you should record and how frequently. Generating data for metrics too often can impose a significant additional load on the system, whereas capturing metrics infrequently might cause you to miss the circumstances that lead to a significant event. The considerations will vary from metric to metric. For example, CPU utilization on a server might vary significantly from second to second, but high utilization becomes an issue only if it's long-lived over a number of minutes. + + + +### Information for correlating data +You can easily monitor individual system-level performance counters, capture metrics for resources, and obtain application trace information from various log files. But some forms of monitoring require the analysis and diagnostics stage in the monitoring pipeline to correlate the data that's retrieved from several sources. This data might take several forms in the raw data, and the analysis process must be provided with sufficient instrumentation data to be able to map these different forms. For example, at the application framework level, a task might be identified by a thread ID. Within an application, the same work might be associated with the user ID for the user who is performing that task. + +Also, there's unlikely to be a 1:1 mapping between threads and user requests, because asynchronous operations might reuse the same threads to perform operations on behalf of more than one user. To complicate matters further, a single request might be handled by more than one thread as execution flows through the system. If possible, associate each request with a unique activity ID that's propagated through the system as part of the request context. (The technique for generating and including activity IDs in trace information depends on the technology that's used to capture the trace data.) + +All monitoring data should be time-stamped in the same way. For consistency, record all dates and times by using Coordinated Universal Time. This will help you more easily trace sequences of events. + +> [!NOTE] +> Computers operating in different time zones and networks might not be synchronized. Don't depend on using time stamps alone for correlating instrumentation data that spans multiple machines. +> +> + +### Information to include in the instrumentation data +Consider the following points when you're deciding which instrumentation data you need to collect: + +* Make sure that information captured by trace events is machine and human readable. Adopt well-defined schemas for this information to facilitate automated processing of log data across systems, and to provide consistency to operations and engineering staff reading the logs. Include environmental information, such as the deployment environment, the machine on which the process is running, the details of the process, and the call stack. +* Enable profiling only when necessary because it can impose a significant overhead on the system. Profiling by using instrumentation records an event (such as a method call) every time it occurs, whereas sampling records only selected events. The selection can be time-based (once every *n* seconds), or frequency-based (once every *n* requests). If events occur very frequently, profiling by instrumentation might cause too much of a burden and itself affect overall performance. In this case, the sampling approach might be preferable. However, if the frequency of events is low, sampling might miss them. In this case, instrumentation might be the better approach. +* Provide sufficient context to enable a developer or administrator to determine the source of each request. This might include some form of activity ID that identifies a specific instance of a request. It might also include information that can be used to correlate this activity with the computational work performed and the resources used. Note that this work might cross process and machine boundaries. For metering, the context should also include (either directly or indirectly via other correlated information) a reference to the customer who caused the request to be made. This context provides valuable information about the application state at the time that the monitoring data was captured. +* Record all requests, and the locations or regions from which these requests are made. This information can assist in determining whether there are any location-specific hotspots. This information can also be useful in determining whether to repartition an application or the data that it uses. +* Record and capture the details of exceptions carefully. Often, critical debug information is lost as a result of poor exception handling. Capture the full details of exceptions that the application throws, including any inner exceptions and other context information. Include the call stack if possible. +* Be consistent in the data that the different elements of your application capture, because this can assist in analyzing events and correlating them with user requests. Consider using a comprehensive and configurable logging package to gather information, rather than depending on developers to adopt the same approach as they implement different parts of the system. Gather data from key performance counters, such as the volume of I/O being performed, network utilization, number of requests, memory use, and CPU utilization. Some infrastructure services might provide their own specific performance counters, such as the number of connections to a database, the rate at which transactions are being performed, and the number of transactions that succeed or fail. Applications might also define their own specific performance counters. +* Log all calls made to external services, such as database systems, web services, or other system-level services that are part of the infrastructure. Record information about the time taken to perform each call, and the success or failure of the call. If possible, capture information about all retry attempts and failures for any transient errors that occur. + +### Ensuring compatibility with telemetry systems +In many cases, the information that instrumentation produces is generated as a series of events and passed to a separate telemetry system for processing and analysis. A telemetry system is typically independent of any specific application or technology, but it expects information to follow a specific format that's usually defined by a schema. The schema effectively specifies a contract that defines the data fields and types that the telemetry system can ingest. The schema should be generalized to allow for data arriving from a range of platforms and devices. + +A common schema should include fields that are common to all instrumentation events, such as the event name, the event time, the IP address of the sender, and the details that are required for correlating with other events (such as a user ID, a device ID, and an application ID). Remember that any number of devices might raise events, so the schema should not depend on the device type. Additionally, various devices might raise events for the same application; the application might support roaming or some other form of cross-device distribution. + +The schema might also include domain fields that are relevant to a particular scenario that's common across different applications. This might be information about exceptions, application start and end events, and success and/or failure of web service API calls. All applications that use the same set of domain fields should emit the same set of events, enabling a set of common reports and analytics to be built. + +Finally, a schema might contain custom fields for capturing the details of application-specific events. + +### Best practices for instrumenting applications +The following list summarizes best practices for instrumenting a distributed application running in the cloud. + +* Make logs easy to read and easy to parse. Use structured logging where possible. Be concise and descriptive in log messages. +* In all logs, identify the source and provide context and timing information as each log record is written. +* Use the same time zone and format for all time stamps. This will help to correlate events for operations that span hardware and services running in different geographic regions. +* Categorize logs and write messages to the appropriate log file. +* Do not disclose sensitive information about the system or personal information about users. Scrub this information before it's logged, but ensure that the relevant details are retained. For example, remove the ID and password from any database connection strings, but write the remaining information to the log so that an analyst can determine that the system is accessing the correct database. Log all critical exceptions, but enable the administrator to turn logging on and off for lower levels of exceptions and warnings. Also, capture and log all retry logic information. This data can be useful in monitoring the transient health of the system. +* Trace out of process calls, such as requests to external web services or databases. +* Don't mix log messages with different security requirements in the same log file. For example, don't write debug and audit information to the same log. +* With the exception of auditing events, make sure that all logging calls are fire-and-forget operations that do not block the progress of business operations. Auditing events are exceptional because they are critical to the business and can be classified as a fundamental part of business operations. +* Make sure that logging is extensible and does not have any direct dependencies on a concrete target. For example, rather than writing information by using *System.Diagnostics.Trace*, define an abstract interface (such as *ILogger*) that exposes logging methods and that can be implemented through any appropriate means. +* Make sure that all logging is fail-safe and never triggers any cascading errors. Logging must not throw any exceptions. +* Treat instrumentation as an ongoing iterative process and review logs regularly, not just when there is a problem. + +## Collecting and storing data +The collection stage of the monitoring process is concerned with retrieving the information that instrumentation generates, formatting this data to make it easier for the analysis/diagnosis stage to consume, and saving the transformed data in reliable storage. The instrumentation data that you gather from different parts of a distributed system can be held in a variety of locations and with varying formats. For example, your application code might generate trace log files and generate application event log data, whereas performance counters that monitor key aspects of the infrastructure that your application uses can be captured through other technologies. Any third-party components and services that your application uses might provide instrumentation information in different formats, by using separate trace files, blob storage, or even a custom data store. + +Data collection is often performed through a collection service that can run autonomously from the application that generates the instrumentation data. Figure 2 illustrates an example of this architecture, highlighting the instrumentation data-collection subsystem. + +![Example of collecting instrumentation data](./images/monitoring/TelemetryService.png) + +*Figure 2. +Collecting instrumentation data* + +Note that this is a simplified view. The collection service is not necessarily a single process and might comprise many constituent parts running on different machines, as described in the following sections. Additionally, if the analysis of some telemetry data must be performed quickly (hot analysis, as described in the section [Supporting hot, warm, and cold analysis](#supporting-hot-warm-and-cold-analysis) later in this document), local components that operate outside the collection service might perform the analysis tasks immediately. Figure 2 depicts this situation for selected events. After analytical processing, the results can be sent directly to the visualization and alerting subsystem. Data that's subjected to warm or cold analysis is held in storage while it awaits processing. + +For Azure applications and services, Azure Diagnostics provides one possible solution for capturing data. Azure Diagnostics gathers data from the following sources for each compute node, aggregates it, and then uploads it to Azure Storage: + +* IIS logs +* IIS Failed Request logs +* Windows event logs +* Performance counters +* Crash dumps +* Azure Diagnostics infrastructure logs +* Custom error logs +* .NET EventSource +* Manifest-based ETW + +For more information, see the article [Azure: Telemetry Basics and Troubleshooting](http://social.technet.microsoft.com/wiki/contents/articles/18146.windows-azure-telemetry-basics-and-troubleshooting.aspx). + +### Strategies for collecting instrumentation data +Considering the elastic nature of the cloud, and to avoid the necessity of manually retrieving telemetry data from every node in the system, you should arrange for the data to be transferred to a central location and consolidated. In a system that spans multiple datacenters, it might be useful to first collect, consolidate, and store data on a region-by-region basis, and then aggregate the regional data into a single central system. + +To optimize the use of bandwidth, you can elect to transfer less urgent data in chunks, as batches. However, the data must not be delayed indefinitely, especially if it contains time-sensitive information. + +#### *Pulling and pushing instrumentation data* +The instrumentation data-collection subsystem can actively retrieve instrumentation data from the various logs and other sources for each instance of the application (the *pull model*). Or, it can act as a passive receiver that waits for the data to be sent from the components that constitute each instance of the application (the *push model*). + +One approach to implementing the pull model is to use monitoring agents that run locally with each instance of the application. A monitoring agent is a separate process that periodically retrieves (pulls) telemetry data collected at the local node and writes this information directly to centralized storage that all instances of the application share. This is the mechanism that Azure Diagnostics implements. Each instance of an Azure web or worker role can be configured to capture diagnostic and other trace information that's stored locally. The monitoring agent that runs alongside each instance copies the specified data to Azure Storage. The article [Enabling Diagnostics in Azure Cloud Services and Virtual Machines](/azure/cloud-services/cloud-services-dotnet-diagnostics) provides more details on this process. Some elements, such as IIS logs, crash dumps, and custom error logs, are written to blob storage. Data from the Windows event log, ETW events, and performance counters is recorded in table storage. Figure 3 illustrates this mechanism. + +![Illustration of using a monitoring agent to pull information and write to shared storage](./images/monitoring/PullModel.png) + +*Figure 3. +Using a monitoring agent to pull information and write to shared storage* + +> [!NOTE] +> Using a monitoring agent is ideally suited to capturing instrumentation data that's naturally pulled from a data source. An example is information from SQL Server Dynamic Management Views or the length of an Azure Service Bus queue. +> +> + +It's feasible to use the approach just described to store telemetry data for a small-scale application running on a limited number of nodes in a single location. However, a complex, highly scalable, global cloud application might generate huge volumes of data from hundreds of web and worker roles, database shards, and other services. This flood of data can easily overwhelm the I/O bandwidth available with a single, central location. Therefore, your telemetry solution must be scalable to prevent it from acting as a bottleneck as the system expands. Ideally, your solution should incorporate a degree of redundancy to reduce the risks of losing important monitoring information (such as auditing or billing data) if part of the system fails. + +To address these issues, you can implement queuing, as shown in Figure 4. In this architecture, the local monitoring agent (if it can be configured appropriately) or custom data-collection service (if not) posts data to a queue. A separate process running asynchronously (the storage writing service in Figure 4) takes the data in this queue and writes it to shared storage. A message queue is suitable for this scenario because it provides "at least once" semantics that help ensure that queued data will not be lost after it's posted. You can implement the storage writing service by using a separate worker role. + +![Illustration of using a queue to buffer instrumentation data](./images/monitoring/BufferedQueue.png) + +*Figure 4. +Using a queue to buffer instrumentation data* + +The local data-collection service can add data to a queue immediately after it's received. The queue acts as a buffer, and the storage writing service can retrieve and write the data at its own pace. By default, a queue operates on a first-in, first-out basis. But you can prioritize messages to accelerate them through the queue if they contain data that must be handled more quickly. For more information, see the [Priority Queue](https://msdn.microsoft.com/library/dn589794.aspx) pattern. Alternatively, you can use different channels (such as Service Bus topics) to direct data to different destinations depending on the form of analytical processing that's required. + +For scalability, you can run multiple instances of the storage writing service. If there is a high volume of events, you can use an event hub to dispatch the data to different compute resources for processing and storage. + + + +#### *Consolidating instrumentation data* +The instrumentation data that the data-collection service retrieves from a single instance of an application gives a localized view of the health and performance of that instance. To assess the overall health of the system, it's necessary to consolidate some aspects of the data in the local views. You can perform this after the data has been stored, but in some cases, you can also achieve it as the data is collected. Rather than being written directly to shared storage, the instrumentation data can pass through a separate data consolidation service that combines data and acts as a filter and cleanup process. For example, instrumentation data that includes the same correlation information such as an activity ID can be amalgamated. (It's possible that a user starts performing a business operation on one node and then gets transferred to another node in the event of node failure, or depending on how load balancing is configured.) This process can also detect and remove any duplicated data (always a possibility if the telemetry service uses message queues to push instrumentation data out to storage). Figure 5 illustrates an example of this structure. + +![Example of using a service to consolidate instrumentation data](./images/monitoring/Consolidation.png) + +*Figure 5. +Using a separate service to consolidate and clean up instrumentation data* + +### Storing instrumentation data +The previous discussions have depicted a rather simplistic view of the way in which instrumentation data is stored. In reality, it can make sense to store the different types of information by using technologies that are most appropriate to the way in which each type is likely to be used. + +For example, Azure blob and table storage have some similarities in the way in which they're accessed. But they have limitations in the operations that you can perform by using them, and the granularity of the data that they hold is quite different. If you need to perform more analytical operations or require full-text search capabilities on the data, it might be more appropriate to use data storage that provides capabilities that are optimized for specific types of queries and data access. For example: + +* Performance counter data can be stored in a SQL database to enable ad hoc analysis. +* Trace logs might be better stored in Azure DocumentDB. +* Security information can be written to HDFS. +* Information that requires full-text search can be stored through Elasticsearch (which can also speed searches by using rich indexing). + +You can implement an additional service that periodically retrieves the data from shared storage, partitions and filters the data according to its purpose, and then writes it to an appropriate set of data stores as shown in Figure 6. An alternative approach is to include this functionality in the consolidation and cleanup process and write the data directly to these stores as it's retrieved rather than saving it in an intermediate shared storage area. Each approach has its advantages and disadvantages. Implementing a separate partitioning service lessens the load on the consolidation and cleanup service, and it enables at least some of the partitioned data to be regenerated if necessary (depending on how much data is retained in shared storage). However, it consumes additional resources. Also, there might be a delay between the receipt of instrumentation data from each application instance and the conversion of this data into actionable information. + +![Partitioning and storage of data](./images/monitoring/DataStorage.png) + +*Figure 6. +Partitioning data according to analytical and storage requirements* + +The same instrumentation data might be required for more than one purpose. For example, performance counters can be used to provide a historical view of system performance over time. This information might be combined with other usage data to generate customer billing information. In these situations, the same data might be sent to more than one destination, such as a document database that can act as a long-term store for holding billing information, and a multidimensional store for handling complex performance analytics. + +You should also consider how urgently the data is required. Data that provides information for alerting must be accessed quickly, so it should be held in fast data storage and indexed or structured to optimize the queries that the alerting system performs. In some cases, it might be necessary for the telemetry service that gathers the data on each node to format and save data locally so that a local instance of the alerting system can quickly notify you of any issues. The same data can be dispatched to the storage writing service shown in the previous diagrams and stored centrally if it's also required for other purposes. + +Information that's used for more considered analysis, for reporting, and for spotting historical trends is less urgent and can be stored in a manner that supports data mining and ad hoc queries. For more information, see the section [Supporting hot, warm, and cold analysis](#supporting-hot-warm-and-cold-analysis) later in this document. + +#### *Log rotation and data retention* +Instrumentation can generate considerable volumes of data. This data can be held in several places, starting with the raw log files, trace files, and other information captured at each node to the consolidated, cleaned, and partitioned view of this data held in shared storage. In some cases, after the data has been processed and transferred, the original raw source data can be removed from each node. In other cases, it might be necessary or simply useful to save the raw information. For example, data that's generated for debugging purposes might be best left available in its raw form but can then be discarded quickly after any bugs have been rectified. + +Performance data often has a longer life so that it can be used for spotting performance trends and for capacity planning. The consolidated view of this data is usually kept online for a finite period to enable fast access. After that, it can be archived or discarded. Data gathered for metering and billing customers might need to be saved indefinitely. Additionally, regulatory requirements might dictate that information collected for auditing and security purposes also needs to be archived and saved. This data is also sensitive and might need to be encrypted or otherwise protected to prevent tampering. You should never record users' passwords or other information that might be used to commit identity fraud. Such details should be scrubbed from the data before it's stored. + +#### *Down-sampling* +It's useful to store historical data so you can spot long-term trends. Rather than saving old data in its entirety, it might be possible to down-sample the data to reduce its resolution and save storage costs. As an example, rather than saving minute-by-minute performance indicators, you can consolidate data that's more than a month old to form an hour-by-hour view. + +### Best practices for collecting and storing logging information +The following list summarizes best practices for capturing and storing logging information: + +* The monitoring agent or data-collection service should run as an out-of-process service and should be simple to deploy. +* All output from the monitoring agent or data-collection service should be an agnostic format that's independent of the machine, operating system, or network protocol. For example, emit information in a self-describing format such as JSON, MessagePack, or Protobuf rather than ETL/ETW. Using a standard format enables the system to construct processing pipelines; components that read, transform, and send data in the agreed format can be easily integrated. +* The monitoring and data-collection process must be fail-safe and must not trigger any cascading error conditions. +* In the event of a transient failure in sending information to a data sink, the monitoring agent or data-collection service should be prepared to reorder telemetry data so that the newest information is sent first. (The monitoring agent/data-collection service might elect to drop the older data, or save it locally and transmit it later to catch up, at its own discretion.) + +## Analyzing data and diagnosing issues +An important part of the monitoring and diagnostics process is analyzing the gathered data to obtain a picture of the overall well-being of the system. You should have defined your own KPIs and performance metrics, and it's important to understand how you can structure the data that has been gathered to meet your analysis requirements. It's also important to understand how the data that's captured in different metrics and log files is correlated, because this information can be key to tracking a sequence of events and help diagnose problems that arise. + +As described in the section [Consolidating instrumentation data](#consolidating-instrumentation-data), the data for each part of the system is typically captured locally, but it generally needs to be combined with data generated at other sites that participate in the system. This information requires careful correlation to ensure that data is combined accurately. For example, the usage data for an operation might span a node that hosts a website to which a user connects, a node that runs a separate service accessed as part of this operation, and data storage held on another node. This information needs to be tied together to provide an overall view of the resource and processing usage for the operation. Some pre-processing and filtering of data might occur on the node on which the data is captured, whereas aggregation and formatting are more likely to occur on a central node. + + + +### Supporting hot, warm, and cold analysis +Analyzing and reformatting data for visualization, reporting, and alerting purposes can be a complex process that consumes its own set of resources. Some forms of monitoring are time-critical and require immediate analysis of data to be effective. This is known as *hot analysis*. Examples include the analyses that are required for alerting and some aspects of security monitoring (such as detecting an attack on the system). Data that's required for these purposes must be quickly available and structured for efficient processing. In some cases, it might be necessary to move the analysis processing to the individual nodes where the data is held. + +Other forms of analysis are less time-critical and might require some computation and aggregation after the raw data has been received. This is called *warm analysis*. Performance analysis often falls into this category. In this case, an isolated, single performance event is unlikely to be statistically significant. (It might be caused by a sudden spike or glitch.) The data from a series of events should provide a more reliable picture of system performance. + +Warm analysis can also be used to help diagnose health issues. A health event is typically processed through hot analysis and can raise an alert immediately. An operator should be able to drill into the reasons for the health event by examining the data from the warm path. This data should contain information about the events leading up to the issue that caused the health event. + +Some types of monitoring generate more long-term data. This analysis can be performed at a later date, possibly according to a predefined schedule. In some cases, the analysis might need to perform complex filtering of large volumes of data captured over a period of time. This is called *cold analysis*. The key requirement is that the data is stored safely after it has been captured. For example, usage monitoring and auditing require an accurate picture of the state of the system at regular points in time, but this state information does not have to be available for processing immediately after it has been gathered. + +An operator can also use cold analysis to provide the data for predictive health analysis. The operator can gather historical information over a specified period and use it in conjunction with the current health data (retrieved from the hot path) to spot trends that might soon cause health issues. In these cases, it might be necessary to raise an alert so that corrective action can be taken. + +### Correlating data +The data that instrumentation captures can provide a snapshot of the system state, but the purpose of analysis is to make this data actionable. For example: + +* What has caused an intense I/O loading at the system level at a specific time? +* Is it the result of a large number of database operations? +* Is this reflected in the database response times, the number of transactions per second, and application response times at the same juncture? + +If so, one remedial action that might reduce the load might be to shard the data over more servers. In addition, exceptions can arise as a result of a fault in any level of the system. An exception in one level often triggers another fault in the level above. + +For these reasons, you need to be able to correlate the different types of monitoring data at each level to produce an overall view of the state of the system and the applications that are running on it. You can then use this information to make decisions about whether the system is functioning acceptably or not, and determine what can be done to improve the quality of the system. + +As described in the section [Information for correlating data](#information-for-correlating-data), you must ensure that the raw instrumentation data includes sufficient context and activity ID information to support the required aggregations for correlating events. Additionally, this data might be held in different formats, and it might be necessary to parse this information to convert it into a standardized format for analysis. + +### Troubleshooting and diagnosing issues +Diagnosis requires the ability to determine the cause of faults or unexpected behavior, including performing root cause analysis. The information that's required typically includes: + +* Detailed information from event logs and traces, either for the entire system or for a specified subsystem during a specified time window. +* Complete stack traces resulting from exceptions and faults of any specified level that occur within the system or a specified subsystem during a specified period. +* Crash dumps for any failed processes either anywhere in the system or for a specified subsystem during a specified time window. +* Activity logs recording the operations that are performed either by all users or for selected users during a specified period. + +Analyzing data for troubleshooting purposes often requires a deep technical understanding of the system architecture and the various components that compose the solution. As a result, a large degree of manual intervention is often required to interpret the data, establish the cause of problems, and recommend an appropriate strategy to correct them. It might be appropriate simply to store a copy of this information in its original format and make it available for cold analysis by an expert. + +## Visualizing data and raising alerts +An important aspect of any monitoring system is the ability to present the data in such a way that an operator can quickly spot any trends or problems. Also important is the ability to quickly inform an operator if a significant event has occurred that might require attention. + +Data presentation can take several forms, including visualization by using dashboards, alerting, and reporting. + +### Visualization by using dashboards +The most common way to visualize data is to use dashboards that can display information as a series of charts, graphs, or some other illustration. These items can be parameterized, and an analyst should be able to select the important parameters (such as the time period) for any specific situation. + +Dashboards can be organized hierarchically. Top-level dashboards can give an overall view of each aspect of the system but enable an operator to drill down to the details. For example, a dashboard that depicts the overall disk I/O for the system should allow an analyst to view the I/O rates for each individual disk to ascertain whether one or more specific devices account for a disproportionate volume of traffic. Ideally, the dashboard should also display related information, such as the source of each request (the user or activity) that's generating this I/O. This information can then be used to determine whether (and how) to spread the load more evenly across devices, and whether the system would perform better if more devices were added. + +A dashboard might also use color-coding or some other visual cues to indicate values that appear anomalous or that are outside an expected range. Using the previous example: + +* A disk with an I/O rate that's approaching its maximum capacity over an extended period (a hot disk) can be highlighted in red. +* A disk with an I/O rate that periodically runs at its maximum limit over short periods (a warm disk) can be highlighted in yellow. +* A disk that's exhibiting normal usage can be displayed in green. + +Note that for a dashboard system to work effectively, it must have the raw data to work with. If you are building your own dashboard system, or using a dashboard developed by another organization, you must understand which instrumentation data you need to collect, at what levels of granularity, and how it should be formatted for the dashboard to consume. + +A good dashboard does not only display information, it also enables an analyst to pose ad hoc questions about that information. Some systems provide management tools that an operator can use to perform these tasks and explore the underlying data. Alternatively, depending on the repository that's used to hold this information, it might be possible to query this data directly, or import it into tools such as Microsoft Excel for further analysis and reporting. + +> [!NOTE] +> You should restrict access to dashboards to authorized personnel, because this information might be commercially sensitive. You should also protect the underlying data for dashboards to prevent users from changing it. +> +> + +### Raising alerts +Alerting is the process of analyzing the monitoring and instrumentation data and generating a notification if a significant event is detected. + +Alerting helps ensure that the system remains healthy, responsive, and secure. It's an important part of any system that makes performance, availability, and privacy guarantees to the users where the data might need to be acted on immediately. An operator might need to be notified of the event that triggered the alert. Alerting can also be used to invoke system functions such as autoscaling. + +Alerting usually depends on the following instrumentation data: + +* Security events. If the event logs indicate that repeated authentication and/or authorization failures are occurring, the system might be under attack and an operator should be informed. +* Performance metrics. The system must quickly respond if a particular performance metric exceeds a specified threshold. +* Availability information. If a fault is detected, it might be necessary to quickly restart one or more subsystems, or fail over to a backup resource. Repeated faults in a subsystem might indicate more serious concerns. + +Operators might receive alert information by using many delivery channels such as email, a pager device, or an SMS text message. An alert might also include an indication of how critical a situation is. Many alerting systems support subscriber groups, and all operators who are members of the same group can receive the same set of alerts. + +An alerting system should be customizable, and the appropriate values from the underlying instrumentation data can be provided as parameters. This approach enables an operator to filter data and focus on those thresholds or combinations of values that are of interest. Note that in some cases, the raw instrumentation data can be provided to the alerting system. In other situations, it might be more appropriate to supply aggregated data. (For example, an alert can be triggered if the CPU utilization for a node has exceeded 90 percent over the last 10 minutes). The details provided to the alerting system should also include any appropriate summary and context information. This data can help reduce the possibility that false-positive events will trip an alert. + +### Reporting +Reporting is used to generate an overall view of the system. It might incorporate historical data in addition to current information. Reporting requirements themselves fall into two broad categories: operational reporting and security reporting. + +Operational reporting typically includes the following aspects: + +* Aggregating statistics that you can use to understand resource utilization of the overall system or specified subsystems during a specified time window +* Identifying trends in resource usage for the overall system or specified subsystems during a specified period +* Monitoring the exceptions that have occurred throughout the system or in specified subsystems during a specified period +* Determining the efficiency of the application in terms of the deployed resources, and understanding whether the volume of resources (and their associated cost) can be reduced without affecting performance unnecessarily + +Security reporting is concerned with tracking customers' use of the system. It can include: + +* Auditing user operations. This requires recording the individual requests that each user performs, together with dates and times. The data should be structured to enable an administrator to quickly reconstruct the sequence of operations that a user performs over a specified period. +* Tracking resource use by user. This requires recording how each request for a user accesses the various resources that compose the system, and for how long. An administrator must be able to use this data to generate a utilization report by user over a specified period, possibly for billing purposes. + +In many cases, batch processes can generate reports according to a defined schedule. (Latency is not normally an issue.) But they should also be available for generation on an ad hoc basis if needed. As an example, if you are storing data in a relational database such as Azure SQL Database, you can use a tool such as SQL Server Reporting Services to extract and format data and present it as a set of reports. + +## Related patterns and guidance +* [Autoscaling guidance](../best-practices/auto-scaling.md) describes how to decrease management overhead by reducing the need for an operator to continually monitor the performance of a system and make decisions about adding or removing resources. +* [Health Endpoint Monitoring Pattern](https://msdn.microsoft.com/library/dn589789.aspx) describes how to implement functional checks within an application that external tools can access through exposed endpoints at regular intervals. +* [Priority Queue Pattern](https://msdn.microsoft.com/library/dn589794.aspx) shows how to prioritize queued messages so that urgent requests are received and can be processed before less urgent messages. + +## More information +* [Monitor, diagnose, and troubleshoot Microsoft Azure Storage](/azure/storage/storage-monitoring-diagnosing-troubleshooting) +* [Azure: Telemetry Basics and Troubleshooting](http://social.technet.microsoft.com/wiki/contents/articles/18146.windows-azure-telemetry-basics-and-troubleshooting.aspx) +* [Enabling Diagnostics in Azure Cloud Services and Virtual Machines](/azure/cloud-services/cloud-services-dotnet-diagnostics) +* [Azure Redis Cache](https://azure.microsoft.com/services/cache/), [Azure DocumentDB](https://azure.microsoft.com/services/documentdb/), and [HDInsight](https://azure.microsoft.com/services/hdinsight/) +* [How to use Service Bus queues](/azure/service-bus-messaging/service-bus-dotnet-get-started-with-queues) +* [SQL Server business intelligence in Azure Virtual Machines](/azure/virtual-machines/windows/sqlclassic/virtual-machines-windows-classic-ps-sql-bi) +* [Receive alert notifications](/azure/monitoring-and-diagnostics/insights-receive-alert-notifications) and [Track service health](/azure/monitoring-and-diagnostics/insights-service-health) +* [Application Insights](/azure/application-insights/app-insights-overview) + diff --git a/docs/best-practices/naming-conventions.md b/docs/best-practices/naming-conventions.md new file mode 100644 index 00000000000..f9297a691b6 --- /dev/null +++ b/docs/best-practices/naming-conventions.md @@ -0,0 +1,228 @@ +--- +title: Naming conventions for Azure resources +description: Naming conventions for Azure resources. How to name virtual machines, storage accounts, networks, virtual networks, subnets and other Azure entities +services: '' +documentationcenter: na +author: telmosampaio +manager: bennage +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: f5cbc794-6e51-4b36-b0cb-428c874c6950 +ms.service: guidance +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 10/31/2016 +ms.author: telmos + +--- +# Naming conventions + +[!INCLUDE [header](../_includes/header.md)] + +This article is a summary of the naming rules and restrictions for Azure resources +and a baseline set of recommendations for naming conventions. You can use these recommendations +as a starting point for your own conventions specific to your needs. + +The choice of a name for any resource in Microsoft Azure is important because: + +* It is difficult to change a name later. +* Names must meet the requirements of their specific resource type. + +Consistent naming conventions make resources easier to locate. They can also indicate the role of +a resource in a solution. + +The key to success with naming conventions is establishing and following them across your applications and organizations. + +## Naming subscriptions +When naming Azure subscriptions, verbose names make understanding the context +and purpose of each subscription clear. When working in an environment with many subscriptions, following a shared naming convention can improve clarity. + +A recommended pattern for naming subscriptions is: + +` ` + +* Company would usually be the same for each subscription. However, some companies may have + child companies within the organizational structure. These companies may be managed by a central IT + group. In these cases, they could be differentiated by having both the parent company name (*Contoso*) + and child company name (*North Wind*). +* Department is a name within the organization where a group of individuals work. This item within + the namespace as optional. +* Product line is a specific name for a product or function that is performed from within the department. + This is generally optional for internal-facing services and applications. However, it is highly recommended to use + for public-facing services that require easy separation and identification (such as for clear + separation of billing records). +* Environment is the name that describes the deployment lifecycle of the applications or services, + such as Dev, QA, or Prod. + +| Company | Department | Product Line or Service | Environment | Full Name | +| --- | --- | --- | --- | --- | +| Contoso |SocialGaming |AwesomeService |Production |Contoso SocialGaming AwesomeService Production | +| Contoso |SocialGaming |AwesomeService |Dev |Contoso SocialGaming AwesomeService Dev | +| Contoso |IT |InternalApps |Production |Contoso IT InternalApps Production | +| Contoso |IT |InternalApps |Dev |Contoso IT InternalApps Dev | + + + +## Use affixes to avoid ambiguity +When naming resources in Azure, it is recommended to use common prefixes or suffixes to identify the type and +context of the resource. While all the information about type, metadata, context, is available programmatically, +applying common affixes simplifies visual identification. When incorporating affixes into your naming convention, +it is important to clearly specify whether the affix is at the beginning of the name +(prefix) or at the end (suffix). + +For instance, here are two possible names for a service hosting a calculation engine: + +* SvcCalculationEngine (prefix) +* CalculationEngineSvc (suffix) + +Affixes can refer to different aspects that describe the particular resources. The following table +shows some examples typically used. + +| Aspect | Example | Notes | +| --- | --- | --- | +| Environment |dev, prod, QA |Identifies the environment for the resource | +| Location |uw (US West), ue (US East) |Identifies the region into which the resource is deployed | +| Instance |01, 02 |For resources that have more than one named instance (web servers, etc.). | +| Product or Service |service |Identifies the product, application, or service that the resource supports | +| Role |sql, web, messaging |Identifies the role of the associated resource | + +When developing a specific naming convention for your company or projects, it is importantly to +choose a common set of affixes and their position (suffix or prefix). + +## Naming Rules and Restrictions +Each resource or service type in Azure enforces a set of naming restrictions and scope; any naming convention +or pattern must adhere to the requisite naming rules and scope. For example, while the name of a VM maps to a DNS +name (and is thus required to be unique across all of Azure), the name of a VNET is scoped to the Resource Group that +it is created within. + +In general, avoid having any special characters (`-` or `_`) as the first or last character in any name. These characters will cause most validation rules to fail. + +| Category | Service or Entity | Scope | Length | Casing | Valid Characters | Suggested Pattern | Example | +| --- | --- | --- | --- | --- | --- | --- | --- | +| Resource Group |Resource Group |Global |1-64 |Case insensitive |Alphanumeric, underscore, and hyphen |`--rg` |`profx-prod-rg` | +| Resource Group |Availability Set |Resource Group |1-80 |Case insensitive |Alphanumeric, underscore, and hyphen |`--as` |`profx-sql-as` | +| General |Tag |Associated Entity |512 (name), 256 (value) |Case insensitive |Alphanumeric |`"key" : "value"` |`"department" : "Central IT"` | +| Compute |Virtual Machine |Resource Group |1-15 |Case insensitive |Alphanumeric, underscore, and hyphen |`--vm` |`profx-sql-vm1` | +| Storage |Storage account name (data) |Global |3-24 |Lower case |Alphanumeric |`` (use a function to calculate a unique guid for naming storage accounts) |`profxdata001` | +| Storage |Storage account name (disks) |Global |3-24 |Lower case |Alphanumeric |`st` |`profxsql001st0` | +| Storage |Container name |Storage account |3-63 |Lower case |Alphanumeric and dash |`` |`logs` | +| Storage |Blob name |Container |1-1024 |Case sensitive |Any URL char |`` |`` | +| Storage |Queue name |Storage account |3-63 |Lower case |Alphanumeric and dash |`--` |`awesomeservice-messages-001` | +| Storage |Table name |Storage account |3-63 |Case insensitive |Alphanumeric |`-` |`awesomeservice-logs` | +| Storage |File name |Storage account |3-63 |Lower case |Alphanumeric |`` |`` | +| Networking |Virtual Network (VNet) |Resource Group |2-64 |Case-insensitive |Alphanumeric, dash, underscore, and period |`-[section]-vnet` |`profx-vnet` | +| Networking |Subnet |Parent VNet |2-80 |Case-insensitive |Alphanumeric, underscore, dash, and period |`` |`web` | +| Networking |Network Interface |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`-nic` |`profx-sql1-nic1` | +| Networking |Network Security Group |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`--nsg` |`profx-app-nsg` | +| Networking |Network Security Group Rule |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`` |`sql-allow` | +| Networking |Public IP Address |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`-pip` |`profx-sql1-pip` | +| Networking |Load Balancer |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`-lb` |`profx-lb` | +| Networking |Load Balanced Rules Config |Load Balancer |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`` |`http` | +| Networking |Azure Application Gateway |Resource Group |1-80 |Case-insensitive |Alphanumeric, dash, underscore, and period |`-aag` |`profx-agw` | +| Networking |Traffic Manager Profile |Resource Group |1-63 |Case-insensitive |Alphanumeric, dash, and period |`` |`app1` | + + +## Organizing resources with tags +The Azure Resource Manager supports tagging entities with arbitrary +text strings to identify context and streamline automation. For example, the tag `"sqlVersion: "sql2014ee"` could identify VMs in a deployment running SQL Server 2014 Enterprise Edition for running an automated script against them. Tags should be used to augment and enhance context along side of the naming conventions chosen. + +> [!TIP] +> One other advantage of tags is that tags span resource groups, allowing you to link and correlate entities across +> disparate deployments. +> +> + +Each resource or resource group can have a maximum of **15** tags. The tag name is limited to 512 characters, and the tag +value is limited to 256 characters. + +For more information on resource tagging, refer to [Using tags to organize your Azure resources](/azure/azure-resource-manager/resource-group-using-tags/). + +Some of the common tagging use cases are: + +* **Billing**; Grouping resources and associating them with billing or charge back codes. +* **Service Context Identification**; Identify groups of resources across Resource Groups for common operations and grouping +* **Access Control and Security Context**; Administrative role identification based on portfolio, system, service, app, instance, etc. + +> [!TIP] +> Tag early - tag often. Better to have a baseline tagging scheme in place and adjust over time rather than having +> to retrofit after the fact. +> +> + +An example of some common tagging approaches: + +| Tag Name | Key | Example | Comment | +| --- | --- | --- | --- | +| Bill To / Internal Chargeback ID |billTo |`IT-Chargeback-1234` |An internal I/O or billing code | +| Operator or Directly Responsible Individual (DRI) |managedBy |`joe@contoso.com` |Alias or email address | +| Project Name |project-name |`myproject` |Name of the project or product line | +| Project Version |project-version |`3.4` |Version of the project or product line | +| Environment |environment |`` |Environmental identifier | +| Tier |tier |`Front End, Back End, Data` |Tier or role/context identification | +| Data Profile |dataProfile |`Public, Confidential, Restricted, Internal` |Sensitivity of data stored in the resource | + +## Tips and tricks +Some types of resources may require additional care on naming and conventions. + +### Virtual machines +Especially in larger topologies, carefully naming virtual machines streamlines identifying the +role and purpose of each machine, and enabling more predictable scripting. + +> [!WARNING] +> Every virtual machine in Azure has both an Azure resource name, and an operating +> system host name. +> If the resource name and host name are different, managing the VMs may be challenging and should be avoided. +> For example, if a virtual machine is created from a .vhd that already contains a +> configured operating system with a hostname. +> +> + +* [Microsoft NetBIOS Computer Naming Conventions](https://support.microsoft.com/en-us/help/188997/microsoft-netbios-computer-naming-conventions) + +### Storage accounts and storage entities +There are two primary use cases for storage accounts - backing disks for VMs, and storing +data in blobs, queues and tables. Storage accounts used for VM disks should follow the naming +convention of associating them with the parent VM name (and with the potential need for multiple +storage accounts for high-end VM SKUs, also apply a number suffix). + +> [!TIP] +> Storage accounts - whether for data or disks - should follow a naming convention that +> allows for multiple storage accounts to be leveraged (i.e. always using a numeric suffix). +> +> + +It possible to configure a custom domain name for accessing blob data in your Azure Storage account. +The default endpoint for the Blob service is `https://mystorage.blob.core.windows.net`. + +But if you map a custom domain (such as www.contoso.com) to the blob endpoint for your storage account, +you can also access blob data in your storage account by using that domain. For example, with a custom +domain name, `http://mystorage.blob.core.windows.net/mycontainer/myblob` could be accessed as +`http://www.contoso.com/mycontainer/myblob`. + +For more information about configuring this feature, refer to [Configure a custom domain name for your Blob storage endpoint](/azure/storage/storage-custom-domain-name/). + +For more information on naming blobs, containers and tables: + +* [Naming and Referencing Containers, Blobs, and Metadata](https://msdn.microsoft.com/library/dd135715.aspx) +* [Naming Queues and Metadata](https://msdn.microsoft.com/library/dd179349.aspx) +* [Naming Tables](https://msdn.microsoft.com/library/azure/dd179338.aspx) + +A blob name can contain any combination of characters, but reserved URL characters must be properly +escaped. Avoid blob names that end with a period (.), a forward slash (/), or a sequence or combination +of the two. By convention, the forward slash is the **virtual** directory separator. Do not use a backward +slash (\) in a blob name. The client APIs may allow it, but then fail to hash properly, and the +signatures will not match. + +It is not possible to modify the name of a storage account or container after it has been created. +If you want to use a new name, you must delete it and create a new one. + +> [!TIP] +> We recommend that you establish a naming convention for all storage accounts and types +> before embarking on the development of a new service or application. +> +> \ No newline at end of file diff --git a/docs/best-practices/retry-service-specific.md b/docs/best-practices/retry-service-specific.md new file mode 100644 index 00000000000..9a881137f35 --- /dev/null +++ b/docs/best-practices/retry-service-specific.md @@ -0,0 +1,1092 @@ +--- +title: Retry service specific guidance +description: Service specific guidance for setting the retry mechanism. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 159d6a0b-b929-4e7f-b297-f89b4af8a940 +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# Retry guidance for specific services +[!INCLUDE [header](../_includes/header.md)] + +Most Azure services and client SDKs include a retry mechanism. However, these differ because each service has different characteristics and requirements, and so each retry mechanism is tuned to a specific service. This guide summarizes the retry mechanism features for the majority of Azure services, and includes information to help you use, adapt, or extend the retry mechanism for that service. + +For general guidance on handling transient faults, and retrying connections and operations against services and resources, see [Retry guidance](./transient-faults.md). + +The following table summarizes the retry features for the Azure services described in this guidance. + +| **Service** | **Retry capabilities** | **Policy configuration** | **Scope** | **Telemetry features** | +| --- | --- | --- | --- | --- | +| **[Azure Storage](#azure-storage-retry-guidelines)** |Native in client |Programmatic |Client and individual operations |TraceSource | +| **[SQL Database with Entity Framework](#sql-database-using-entity-framework-6-retry-guidelines)** |Native in client |Programmatic |Global per AppDomain |None | +| **[SQL Database with ADO.NET](#azure-storage-retry-guidelines)** |Topaz* |Declarative and programmatic |Single statements or blocks of code |Custom | +| **[Service Bus](#service-bus-retry-guidelines)** |Native in client |Programmatic |Namespace Manager, Messaging Factory, and Client |ETW | +| **[Azure Redis Cache](#azure-redis-cache-retry-guidelines)** |Native in client |Programmatic |Client |TextWriter | +| **[DocumentDB](#documentdb-retry-guidelines)** |Native in service |Non-configurable |Global |TraceSource | +| **[Azure Search](#azure-storage-retry-guidelines)** |Native in client |Programmatic |Client |ETW or Custom | +| **[Active Directory](#azure-active-directory-retry-guidelines)** |Topaz* (with custom detection strategy) |Declarative and programmatic |Blocks of code |Custom | + +*Topaz in the friendly name for the Transient Fault Handling Application Block that is included in [Enterprise Library 6.0][entlib]. You can use a custom detection strategy with Topaz for most types of services, as described in this guidance. Default strategies for Topaz are shown in the section [Transient Fault Handling Application Block (Topaz) strategies](#transient-fault-handling-application-block-topaz-strategies) at the end of this guidance. Note that the block is now an open-sourced framework and is not directly supported by Microsoft. + +> [!NOTE] +> For most of the Azure built-in retry mechanisms, there is currently no way apply a different retry policy for different types of error or exception beyond the functionality include in the retry policy. Therefore, the best guidance available at the time of writing is to configure a policy that provides the optimum average performance and availability. One way to fine-tune the policy is to analyze log files to determine the type of transient faults that are occurring. For example, if the majority of errors are related to network connectivity issues, you might attempt an immediate retry rather than wait a long time for the first retry. +> +> + +## Azure Storage retry guidelines +Azure storage services include table and blob storage, files, and storage queues. + +### Retry mechanism +Retries occur at the individual REST operation level and are an integral part of the client API implementation. The client storage SDK uses classes that implement the [IExtendedRetryPolicy Interface](http://msdn.microsoft.com/library/microsoft.windowsazure.storage.retrypolicies.iextendedretrypolicy.aspx). + +There are different implementations of the interface. Storage clients can choose from policies specifically designed for accessing tables, blobs, and queues. Each implementation uses a different retry strategy that essentially defines the retry interval and other details. + +The built-in classes provide support for linear (constant delay) and exponential with randomization retry intervals. There is also a no retry policy for use when another process is handling retries at a higher level. However, you can implement your own retry classes if you have specific requirements not provided by the built-in classes. + +Alternate retries switch between primary and secondary storage service location if you are using read access geo-redundant storage (RA-GRS) and the result of the request is a retryable error. See [Azure Storage Redundancy Options](http://msdn.microsoft.com/library/azure/dn727290.aspx) for more information. + +### Policy configuration +Retry policies are configured programmatically. A typical procedure is to create and populate a **TableRequestOptions**, **BlobRequestOptions**, **FileRequestOptions**, or **QueueRequestOptions** instance. + +```csharp +TableRequestOptions interactiveRequestOption = new TableRequestOptions() +{ + RetryPolicy = new LinearRetry(TimeSpan.FromMilliseconds(500), 3), + // For Read-access geo-redundant storage, use PrimaryThenSecondary. + // Otherwise set this to PrimaryOnly. + LocationMode = LocationMode.PrimaryThenSecondary, + // Maximum execution time based on the business use case. Maximum value up to 10 seconds. + MaximumExecutionTime = TimeSpan.FromSeconds(2) +}; +``` + +The request options instance can then be set on the client, and all operations with the client will use the specified request options. + +```csharp +client.DefaultRequestOptions = interactiveRequestOption; +var stats = await client.GetServiceStatsAsync(); +``` + +You can override the client request options by passing a populated instance of the request options class as a parameter to operation methods. + +```csharp +var stats = await client.GetServiceStatsAsync(interactiveRequestOption, operationContext: null); +``` + +You use an **OperationContext** instance to specify the code to execute when a retry occurs and when an operation has completed. This code can collect information about the operation for use in logs and telemetry. + + // Set up notifications for an operation + var context = new OperationContext(); + context.ClientRequestID = "some request id"; + context.Retrying += (sender, args) => + { + /* Collect retry information */ + }; + context.RequestCompleted += (sender, args) => + { + /* Collect operation completion information */ + }; + var stats = await client.GetServiceStatsAsync(null, context); + +In addition to indicating whether a failure is suitable for retry, the extended retry policies return a **RetryContext** object that indicates the number of retries, the results of the last request, whether the next retry will happen in the primary or secondary location (see table below for details). The properties of the **RetryContext** object can be used to decide if and when to attempt a retry. For more details, see [IExtendedRetryPolicy.Evaluate Method](http://msdn.microsoft.com/library/microsoft.windowsazure.storage.retrypolicies.iextendedretrypolicy.evaluate.aspx). + +The following table shows the default settings for the built-in retry policies. + +| **Context** | **Setting** | **Default value** | **Meaning** | +| --- | --- | --- | --- | +| Table / Blob / File
    QueueRequestOptions |MaximumExecutionTime

    ServerTimeout




    LocationMode






    RetryPolicy |120 seconds

    None











    ExponentialPolicy |Maximum execution time for the request, including all potential retry attempts.
    Server timeout interval for the request (value is rounded to seconds). If not specified, it will use the default value for all requests to the server. Usually, the best option is to omit this setting so that the server default is used.
    If the storage account is created with the Read access geo-redundant storage (RA-GRS) replication option, you can use the location mode to indicate which location should receive the request. For example, if **PrimaryThenSecondary** is specified, requests are always sent to the primary location first. If a request fails, it is sent to the secondary location.
    See below for details of each option. | +| Exponential policy |maxAttempt
    deltaBackoff


    MinBackoff

    MaxBackoff |3
    4 seconds


    3 seconds

    30 seconds |Number of retry attempts.
    Back-off interval between retries. Multiples of this timespan, including a random element, will be used for subsequent retry attempts.
    Added to all retry intervals computed from deltaBackoff. This value cannot be changed.
    MaxBackoff is used if the computed retry interval is greater than MaxBackoff. This value cannot be changed. | +| Linear policy |maxAttempt
    deltaBackoff |3
    30 seconds |Number of retry attempts.
    Back-off interval between retries. | + +### Retry usage guidance +Consider the following guidelines when accessing Azure storage services using the storage client API: + +* Use the built-in retry policies from the Microsoft.WindowsAzure.Storage.RetryPolicies namespace where they are appropriate for your requirements. In most cases, these policies will be sufficient. +* Use the **ExponentialRetry** policy in batch operations, background tasks, or non-interactive scenarios. In these scenarios, you can typically allow more time for the service to recover—with a consequently increased chance of the operation eventually succeeding. +* Consider specifying the **MaximumExecutionTime** property of the **RequestOptions** parameter to limit the total execution time, but take into account the type and size of the operation when choosing a timeout value. +* If you need to implement a custom retry, avoid creating wrappers around the storage client classes. Instead, use the capabilities to extend the existing policies through the **IExtendedRetryPolicy** interface. +* If you are using read access geo-redundant storage (RA-GRS) you can use the **LocationMode** to specify that retry attempts will access the secondary read-only copy of the store should the primary access fail. However, when using this option you must ensure that your application can work successfully with data that may be stale if the replication from the primary store has not yet completed. + +Consider starting with following settings for retrying operations. These are general purpose settings, and you should monitor the operations and fine tune the values to suit your own scenario. + +| **Context** | **Sample target E2E
    max latency** | **Retry policy** | **Settings** | **Values** | **How it works** | +| --- | --- | --- | --- | --- | --- | +| Interactive, UI,
    or foreground |2 seconds |Linear |maxAttempt
    deltaBackoff |3
    500 ms |Attempt 1 - delay 500 ms
    Attempt 2 - delay 500 ms
    Attempt 3 - delay 500 ms | +| Background
    or batch |30 seconds |Exponential |maxAttempt
    deltaBackoff |5
    4 seconds |Attempt 1 - delay ~3 sec
    Attempt 2 - delay ~7 sec
    Attempt 3 - delay ~15 sec | + +### Telemetry +Retry attempts are logged to a **TraceSource**. You must configure a **TraceListener** to capture the events and write them to a suitable destination log. You can use the **TextWriterTraceListener** or **XmlWriterTraceListener** to write the data to a log file, the **EventLogTraceListener** to write to the Windows Event Log, or the **EventProviderTraceListener** to write trace data to the ETW subsystem. You can also configure auto-flushing of the buffer, and the verbosity of events that will be logged (for example, Error, Warning, Informational, and Verbose). For more information, see [Client-side Logging with the .NET Storage Client Library](http://msdn.microsoft.com/library/azure/dn782839.aspx). + +Operations can receive an **OperationContext** instance, which exposes a **Retrying** event that can be used to attach custom telemetry logic. For more information, see [OperationContext.Retrying Event](http://msdn.microsoft.com/library/microsoft.windowsazure.storage.operationcontext.retrying.aspx). + +### Examples +The following code example shows how to create two **TableRequestOptions** instances with different retry settings; one for interactive requests and one for background requests. The example then sets these two retry policies on the client so that they apply for all requests, and also sets the interactive strategy on a specific request so that it overrides the default settings applied to the client. + +```csharp +using System; +using System.Threading.Tasks; +using Microsoft.WindowsAzure.Storage; +using Microsoft.WindowsAzure.Storage.RetryPolicies; +using Microsoft.WindowsAzure.Storage.Table; + +namespace RetryCodeSamples +{ + class AzureStorageCodeSamples + { + private const string connectionString = "UseDevelopmentStorage=true"; + + public async static Task Samples() + { + var storageAccount = CloudStorageAccount.Parse(connectionString); + + TableRequestOptions interactiveRequestOption = new TableRequestOptions() + { + RetryPolicy = new LinearRetry(TimeSpan.FromMilliseconds(500), 3), + // For Read-access geo-redundant storage, use PrimaryThenSecondary. + // Otherwise set this to PrimaryOnly. + LocationMode = LocationMode.PrimaryThenSecondary, + // Maximum execution time based on the business use case. Maximum value up to 10 seconds. + MaximumExecutionTime = TimeSpan.FromSeconds(2) + }; + + TableRequestOptions backgroundRequestOption = new TableRequestOptions() + { + // Client has a default exponential retry policy with 4 sec delay and 3 retry attempts + // Retry delays will be approximately 3 sec, 7 sec, and 15 sec + MaximumExecutionTime = TimeSpan.FromSeconds(30), + // PrimaryThenSecondary in case of Read-access geo-redundant storage, else set this to PrimaryOnly + LocationMode = LocationMode.PrimaryThenSecondary + }; + + var client = storageAccount.CreateCloudTableClient(); + // Client has a default exponential retry policy with 4 sec delay and 3 retry attempts + // Retry delays will be approximately 3 sec, 7 sec, and 15 sec + // ServerTimeout and MaximumExecutionTime are not set + + { + // Set properties for the client (used on all requests unless overridden) + // Different exponential policy parameters for background scenarios + client.DefaultRequestOptions = backgroundRequestOption; + // Linear policy for interactive scenarios + client.DefaultRequestOptions = interactiveRequestOption; + } + + { + // set properties for a specific request + var stats = await client.GetServiceStatsAsync(interactiveRequestOption, operationContext: null); + } + + { + // Set up notifications for an operation + var context = new OperationContext(); + context.ClientRequestID = "some request id"; + context.Retrying += (sender, args) => + { + /* Collect retry information */ + }; + context.RequestCompleted += (sender, args) => + { + /* Collect operation completion information */ + }; + var stats = await client.GetServiceStatsAsync(null, context); + } + } + } +} +``` + +### More information +* [Azure Storage Client Library Retry Policy Recommendations](https://azure.microsoft.com/blog/2014/05/22/azure-storage-client-library-retry-policy-recommendations/) +* [Storage Client Library 2.0 – Implementing Retry Policies](http://gauravmantri.com/2012/12/30/storage-client-library-2-0-implementing-retry-policies/) + +## SQL Database using Entity Framework 6 retry guidelines +SQL Database is a hosted SQL database available in a range of sizes and as both a standard (shared) and premium (non-shared) service. Entity Framework is an object-relational mapper that enables .NET developers to work with relational data using domain-specific objects. It eliminates the need for most of the data-access code that developers usually need to write. + +### Retry mechanism +Retry support is provided when accessing SQL Database using Entity Framework 6.0 and higher through a mechanism called [Connection Resiliency / Retry Logic](http://msdn.microsoft.com/data/dn456835.aspx). A full specification is available in the [.NET Entity Framework wiki](https://entityframework.codeplex.com/wikipage?title=Connection%20Resiliency%20Spec) on Codeplex. The main features of the retry mechanism are: + +* The primary abstraction is the **IDbExecutionStrategy** interface. This interface: + * Defines synchronous and asynchronous **Execute*** methods. + * Defines classes that can be used directly or can be configured on a database context as a default strategy, mapped to provider name, or mapped to a provider name and server name. When configured on a context, retries occur at the level of individual database operations, of which there might be several for a given context operation. + * Defines when to retry a failed connection, and how. +* It includes several built-in implementations of the **IDbExecutionStrategy** interface: + * Default - no retrying. + * Default for SQL Database (automatic) - no retrying, but inspects exceptions and wraps them with suggestion to use the SQL Database strategy. + * Default for SQL Database - exponential (inherited from base class) plus SQL Database detection logic. +* It implements an exponential back-off strategy that includes randomization. +* The built-in retry classes are stateful and are not thread safe. However, they can be reused after the current operation is completed. +* If the specified retry count is exceeded, the results are wrapped in a new exception. It does not bubble up the current exception. + +### Policy configuration +Retry support is provided when accessing SQL Database using Entity Framework 6.0 and higher. Retry policies are configured programmatically. The configuration cannot be changed on a per-operation basis. + +When configuring a strategy on the context as the default, you specify a function that creates a new strategy on demand. The following code shows how you can create a retry configuration class that extends the **DbConfiguration** base class. + +```csharp +public class BloggingContextConfiguration : DbConfiguration +{ + public BlogConfiguration() + { + // Set up the execution strategy for SQL Database (exponential) with 5 retries and 4 sec delay + this.SetExecutionStrategy( + "System.Data.SqlClient", () => new SqlAzureExecutionStrategy(5, TimeSpan.FromSeconds(4))); + } +} +``` + +You can then specify this as the default retry strategy for all operations using the **SetConfiguration** method of the **DbConfiguration** instance when the application starts. By default, EF will automatically discover and use the configuration class. + + DbConfiguration.SetConfiguration(new BloggingContextConfiguration()); + +You can specify the retry configuration class for a context by annotating the context class with a **DbConfigurationType** attribute. However, if you have only one configuration class, EF will use it without the need to annotate the context. + + [DbConfigurationType(typeof(BloggingContextConfiguration))] + public class BloggingContext : DbContext + { ... + +If you need to use different retry strategies for specific operations, or disable retries for specific operations, you can create a configuration class that allows you to suspend or swap strategies by setting a flag in the **CallContext**. The configuration class can use this flag to switch strategies, or disable the strategy you provide and use a default strategy. For more information, see [Suspend Execution Strategy](http://msdn.microsoft.com/dn307226#transactions_workarounds) in the page Limitations with Retrying Execution Strategies (EF6 onwards). + +Another technique for using specific retry strategies for individual operations is to create an instance of the required strategy class and supply the desired settings through parameters. You then invoke its **ExecuteAsync** method. + + var executionStrategy = new SqlAzureExecutionStrategy(5, TimeSpan.FromSeconds(4)); + var blogs = await executionStrategy.ExecuteAsync( + async () => + { + using (var db = new BloggingContext("Blogs")) + { + // Acquire some values asynchronously and return them + } + }, + new CancellationToken() + ); + +The simplest way to use a **DbConfiguration** class is to locate it in the same assembly as the **DbContext** class. However, this is not appropriate when the same context is required in different scenarios, such as different interactive and background retry strategies. If the different contexts execute in separate AppDomains, you can use the built-in support for specifying configuration classes in the configuration file or set it explicitly using code. If the different contexts must execute in the same AppDomain, a custom solution will be required. + +For more information, see [Code-Based Configuration (EF6 onwards)](http://msdn.microsoft.com/data/jj680699.aspx). + +The following table shows the default settings for the built-in retry policy when using EF6. + +![Retry guidance table](./images/retry-service-specific/RetryServiceSpecificGuidanceTable4.png) + +### Retry usage guidance +Consider the following guidelines when accessing SQL Database using EF6: + +* Choose the appropriate service option (shared or premium). A shared instance may suffer longer than usual connection delays and throttling due to the usage by other tenants of the shared server. If predictable performance and reliable low latency operations are required, consider choosing the premium option. +* A fixed interval strategy is not recommended for use with Azure SQL Database. Instead, use an exponential back-off strategy because the service may be overloaded, and longer delays allow more time for it to recover. +* Choose a suitable value for the connection and command timeouts when defining connections. Base the timeout on both your business logic design and through testing. You may need to modify this value over time as the volumes of data or the business processes change. Too short a timeout may result in premature failures of connections when the database is busy. Too long a timeout may prevent the retry logic working correctly by waiting too long before detecting a failed connection. The value of the timeout is a component of the end-to-end latency, although you cannot easily determine how many commands will execute when saving the context. You can change the default timeout by setting the **CommandTimeout** property of the **DbContext** instance. +* Entity Framework supports retry configurations defined in configuration files. However, for maximum flexibility on Azure you should consider creating the configuration programmatically within the application. The specific parameters for the retry policies, such as the number of retries and the retry intervals, can be stored in the service configuration file and used at runtime to create the appropriate policies. This allows the settings to be changed within requiring the application to be restarted. + +Consider starting with following settings for retrying operations. You cannot specify the delay between retry attempts (it is fixed and generated as an exponential sequence). You can specify only the maximum values, as shown here; unless you create a custom retry strategy. These are general purpose settings, and you should monitor the operations and fine tune the values to suit your own scenario. + +| **Context** | **Sample target E2E
    max latency** | **Retry policy** | **Settings** | **Values** | **How it works** | +| --- | --- | --- | --- | --- | --- | +| Interactive, UI,
    or foreground |2 seconds |Exponential |MaxRetryCount
    MaxDelay |3
    750 ms |Attempt 1 - delay 0 sec
    Attempt 2 - delay 750 ms
    Attempt 3 – delay 750 ms | +| Background
    or batch |30 seconds |Exponential |MaxRetryCount
    MaxDelay |5
    12 seconds |Attempt 1 - delay 0 sec
    Attempt 2 - delay ~1 sec
    Attempt 3 - delay ~3 sec
    Attempt 4 - delay ~7 sec
    Attempt 5 - delay 12 sec | + +> [!NOTE] +> The end-to-end latency targets assume the default timeout for connections to the service. If you specify longer connection timeouts, the end-to-end latency will be extended by this additional time for every retry attempt. +> +> + +### Examples +The following code example defines a simple data access solution that uses Entity Framework. It sets a specific retry strategy by defining an instance of a class named **BlogConfiguration** that extends **DbConfiguration**. + +```csharp +using System; +using System.Collections.Generic; +using System.Data.Entity; +using System.Data.Entity.SqlServer; +using System.Threading.Tasks; + +namespace RetryCodeSamples +{ + public class BlogConfiguration : DbConfiguration + { + public BlogConfiguration() + { + // Set up the execution strategy for SQL Database (exponential) with 5 retries and 12 sec delay. + // These values could be loaded from configuration rather than being hard-coded. + this.SetExecutionStrategy( + "System.Data.SqlClient", () => new SqlAzureExecutionStrategy(5, TimeSpan.FromSeconds(12))); + } + } + + // Specify the configuration type if more than one has been defined. + // [DbConfigurationType(typeof(BlogConfiguration))] + public class BloggingContext : DbContext + { + // Definition of content goes here. + } + + class EF6CodeSamples + { + public async static Task Samples() + { + // Execution strategy configured by DbConfiguration subclass, discovered automatically or + // or explicitly indicated through configuration or with an attribute. Default is no retries. + using (var db = new BloggingContext("Blogs")) + { + // Add, edit, delete blog items here, then: + await db.SaveChangesAsync(); + } + } + } +} +``` + +More examples of using the Entity Framework retry mechanism can be found in [Connection Resiliency / Retry Logic](http://msdn.microsoft.com/data/dn456835.aspx). + +### More information +* [Azure SQL Database Performance and Elasticity Guide](http://social.technet.microsoft.com/wiki/contents/articles/3507.windows-azure-sql-database-performance-and-elasticity-guide.aspx) + +## SQL Database using ADO.NET retry guidelines +SQL Database is a hosted SQL database available in a range of sizes and as both a standard (shared) and premium (non-shared) service. + +### Retry mechanism +SQL Database has no built-in support for retries when accessed using ADO.NET. However, the return codes from requests can be used to determine why a request failed. The page [Azure SQL Database Throttling](http://msdn.microsoft.com/library/dn338079.aspx) explains how throttling can prevent connections, the return codes for specific situations, and how you can handle these and retry operations. + +You can use the Transient Fault Handling Application Block (Topaz) with the Nuget package EnterpriseLibrary.TransientFaultHandling.Data (class **SqlAzureTransientErrorDetectionStrategy**) to implement a retry mechanism for SQL Database. + +The block also provides the **ReliableSqlConnection** class, which implements the old ADO.NET 1.0 API (**IDbConnection** instead of **DbConnection**) and performs retries and connection management internally. While convenient, this requires you to use a different set of methods for invoking operations with retries, and so is not a simple direct replacement. It does not support asynchronous execution, which is recommended when implementing and using Azure services. In addition, because this class uses ADO.NET 1.0, it does not benefit from the recent improvements and updates to ADO.NET. + +### Policy configuration (SQL Database using ADO.NET) +The Transient Fault Handling Application Block supports both file-based and programmatic configuration. In general, you should use programmatic configuration for maximum flexibility (see the notes in the following section for more information). The following code, which would be executed once at application startup, creates and populates a **RetryManager** with a list of four retry strategies suitable for use with Azure SQL Database. It also sets the default strategies for the **RetryManager**. These are the strategies that will be used for connections and commands if an alternative is not specified when creating a connection or command. + +```csharp +RetryManager.SetDefault(new RetryManager( + new List { new ExponentialBackoff(name: "default", retryCount: 3, + minBackoff: TimeSpan.FromMilliseconds(100), + maxBackoff: TimeSpan.FromSeconds(30), + deltaBackoff: TimeSpan.FromSeconds(1), + firstFastRetry: true), + new ExponentialBackoff(name: "default sql connection", retryCount: 3, + minBackoff: TimeSpan.FromMilliseconds(100), + maxBackoff: TimeSpan.FromSeconds(30), + deltaBackoff: TimeSpan.FromSeconds(1), + firstFastRetry: true), + new ExponentialBackoff(name: "default sql command", retryCount: 3, + minBackoff: TimeSpan.FromMilliseconds(100), + maxBackoff: TimeSpan.FromSeconds(30), + deltaBackoff: TimeSpan.FromSeconds(1), + firstFastRetry: true), + new ExponentialBackoff(name: "alt sql", retryCount: 5, + minBackoff: TimeSpan.FromMilliseconds(100), + maxBackoff: TimeSpan.FromSeconds(30), + deltaBackoff: TimeSpan.FromSeconds(1), + firstFastRetry: true), }, + "default", + new Dictionary { + { + RetryManagerSqlExtensions.DefaultStrategyConnectionTechnologyName, "default sql connection" + }, + { + RetryManagerSqlExtensions.DefaultStrategyCommandTechnologyName, "default sql command"} + })); +``` + +For information about how you can use the retry policies you have configured when you access Azure SQL Database, see the [Examples](#examples) section below. + +Default strategies for the Transient Fault Handling Application Block are shown in the section [Transient Fault Handling Application Block (Topaz) strategies](#transient-fault-handling-application-block-topaz-strategies) at the end of this guidance. + +### Retry usage guidance +Consider the following guidelines when accessing SQL Database using ADO.NET: + +* Choose the appropriate service option (shared or premium). A shared instance may suffer longer than usual connection delays and throttling due to the usage by other tenants of the shared server. If more predictable performance and reliable low latency operations are required, consider choosing the premium option. +* Ensure that you perform retries at the appropriate level or scope to avoid non-idempotent operations causing inconsistency in the data. Ideally, all operations should be idempotent so that they can be repeated without causing inconsistency. Where this is not the case, the retry should be performed at a level or scope that allows all related changes to be undone if one operation fails; for example, from within a transactional scope. For more information, see [Cloud Service Fundamentals Data Access Layer – Transient Fault Handling](http://social.technet.microsoft.com/wiki/contents/articles/18665.cloud-service-fundamentals-data-access-layer-transient-fault-handling.aspx#Idempotent_Guarantee). +* A fixed interval strategy is not recommended for use with Azure SQL Database except for interactive scenarios where there are only a few retries at very short intervals. Instead, consider using an exponential back-off strategy for the majority of scenarios. +* Choose a suitable value for the connection and command timeouts when defining connections. Too short a timeout may result in premature failures of connections when the database is busy. Too long a timeout may prevent the retry logic working correctly by waiting too long before detecting a failed connection. The value of the timeout is a component of the end-to-end latency; it is effectively added to the retry delay specified in the retry policy for every retry attempt. +* Close the connection after a certain number of retries, even when using an exponential back off retry logic, and retry the operation on a new connection. Retrying the same operation multiple times on the same connection can be a factor that contributes to connection problems. For an example of this technique, see [Cloud Service Fundamentals Data Access Layer – Transient Fault Handling](http://social.technet.microsoft.com/wiki/contents/articles/18665.cloud-service-fundamentals-data-access-layer-transient-fault-handling.aspx). +* When connection pooling is in use (the default) there is a chance that the same connection will be chosen from the pool, even after closing and reopening a connection. If this is the case, a technique to resolve it is to call the **ClearPool** method of the **SqlConnection** class to mark the connection as not reusable. However, you should do this only after several connection attempts have failed, and only when encountering the specific class of transient failures such as SQL timeouts (error code -2) related to faulty connections. +* If the data access code uses transactions initiated as **TransactionScope** instances, the retry logic should reopen the connection and initiate a new transaction scope. For this reason, the retryable code block should encompass the entire scope of the transaction. +* The Transient Fault Handling Application Block supports retry configurations entirely defined in configuration files. However, for maximum flexibility on Azure you should consider creating the configuration programmatically within the application. The specific parameters for the retry policies, such as the number of retries and the retry intervals, can be stored in the service configuration file and used at runtime to create the appropriate policies. This allows the settings to be changed within requiring the application to be restarted. + +Consider starting with following settings for retrying operations. These are general purpose settings, and you should monitor the operations and fine tune the values to suit your own scenario. + +| **Context** | **Sample target E2E
    max latency** | **Retry strategy** | **Settings** | **Values** | **How it works** | +| --- | --- | --- | --- | --- | --- | +| Interactive, UI,
    or foreground |2 sec |FixedInterval |Retry count
    Retry interval
    First fast retry |3
    500 ms
    true |Attempt 1 - delay 0 sec
    Attempt 2 - delay 500 ms
    Attempt 3 - delay 500 ms | +| Background
    or batch |30 sec |ExponentialBackoff |Retry count
    Min back-off
    Max back-off
    Delta back-off
    First fast retry |5
    0 sec
    60 sec
    2 sec
    false |Attempt 1 - delay 0 sec
    Attempt 2 - delay ~2 sec
    Attempt 3 - delay ~6 sec
    Attempt 4 - delay ~14 sec
    Attempt 5 - delay ~30 sec | + +> [!NOTE] +> The end-to-end latency targets assume the default timeout for connections to the service. If you specify longer connection timeouts, the end-to-end latency will be extended by this additional time for every retry attempt. +> +> + +### Examples +This section describes how you can use the Transient Fault Handling Application Block to access Azure SQL Database using a set of retry policies you have configured in the **RetryManager** (as shown in the previous section [Policy configuration](#policy-configuration). The simplest approach to using the block is through the **ReliableSqlConnection** class, or by calling the extension methods such as **OpenWithRetry** on a connection (see [The Transient Fault Handling Application Block](http://msdn.microsoft.com/library/hh680934.aspx) for more information). + +However, in the current version of the Transient Fault Handling Application Block these approaches do not indigenously support asynchronous operations against SQL Database. Good practice demands that you use only asynchronous techniques to access Azure services such as SQL Database, and so you should consider the following techniques to use the Transient Fault Handling Application Block with SQL Database. + +You can use the simplified asynchronous support in version 5 of the C# language to create asynchronous versions of the methods provided by the block. For example, the following code shows how you might create an asynchronous version of the **ExecuteReaderWithRetry** extension method. The changes and additions to the original code are highlighted. The source code for Topaz is available on Codeplex at [Transient Fault Handling Application Block ("Topaz")](http://topaz.codeplex.com/SourceControl/latest). + +```csharp +public async static Task ExecuteReaderWithRetryAsync(this SqlCommand command, RetryPolicy cmdRetryPolicy, +RetryPolicy conRetryPolicy) +{ + GuardConnectionIsNotNull(command); + + // Check if retry policy was specified, if not, use the default retry policy. + return await (cmdRetryPolicy ?? RetryPolicy.NoRetry).ExecuteAsync(async () => + { + var hasOpenConnection = await EnsureValidConnectionAsync(command, conRetryPolicy).ConfigureAwait(false); + + try + { + return await command.ExecuteReaderAsync().ConfigureAwait(false); + } + catch (Exception) + { + if (hasOpenConnection && command.Connection != null && command.Connection.State == ConnectionState.Open) + { + command.Connection.Close(); + } + + throw; + } + }).ConfigureAwait(false); +} +``` + +This new asynchronous extension method can be used in the same way as the synchronous versions included in the block. + +```csharp +var sqlCommand = sqlConnection.CreateCommand(); +sqlCommand.CommandText = "[some query]"; + +var retryPolicy = + RetryManager.Instance.GetRetryPolicy("alt sql"); +using (var reader = await sqlCommand.ExecuteReaderWithRetryAsync(retryPolicy)) +{ + // Do something with the values +} +``` + +However, this approach deals only with individual operations or commands, and not with blocks of statements where there can be properly defined transactional boundaries. In addition, it does not address the situation of removing faulty connections from the connection pool so that they are not selected for subsequent attempts. A synchronous example of resolving these issues can be found in [Cloud Service Fundamentals Data Access Layer – Transient Fault Handling](http://social.technet.microsoft.com/wiki/contents/articles/18665.cloud-service-fundamentals-data-access-layer-transient-fault-handling.aspx#Timeouts_amp_Connection_Management). In addition to retrying arbitrary sequences of database instructions, it clears the connection pool to remove invalid connections, and instruments the entire process. While the code shown in this example is synchronous, it is relatively easy to convert it to asynchronous code. + +### More information +For detailed information about using the Transient Fault Handling Application Block, see: + +* [Using the Transient Fault Handling Application Block with SQL Azure](http://msdn.microsoft.com/library/hh680899.aspx) +* [Perseverance, Secret of All Triumphs: Using the Transient Fault Handling Application Block](http://msdn.microsoft.com/library/dn440719.aspx) +* [Cloud Service Fundamentals Data Access Layer – Transient Fault Handling](http://social.technet.microsoft.com/wiki/contents/articles/18665.cloud-service-fundamentals-data-access-layer-transient-fault-handling.aspx) + +For general guidance on getting the most from SQL Database, see [Azure SQL Database Performance and Elasticity Guide](http://social.technet.microsoft.com/wiki/contents/articles/3507.windows-azure-sql-database-performance-and-elasticity-guide.aspx). + + +## Service Bus retry guidelines +Service Bus is a cloud messaging platform that provides loosely coupled message exchange with improved scale and resiliency for components of an application, whether hosted in the cloud or on-premises. + +### Retry mechanism +Service Bus implements retries using implementations of the [RetryPolicy](http://msdn.microsoft.com/library/microsoft.servicebus.retrypolicy.aspx) base class. All of the Service Bus clients expose a **RetryPolicy** property that can be set to one of the implementations of the **RetryPolicy** base class. The built-in implementations are: + +* The [RetryExponential Class](http://msdn.microsoft.com/library/microsoft.servicebus.retryexponential.aspx). This exposes properties that control the back-off interval, the retry count, and the **TerminationTimeBuffer** property that is used to limit the total time for the operation to complete. +* The [NoRetry Class](http://msdn.microsoft.com/library/microsoft.servicebus.noretry.aspx). This is used when retries at the Service Bus API level are not required, such as when retries are managed by another process as part of a batch or multiple step operation. + +Service Bus actions can return a range of exceptions, as listed in [Appendix: Messaging Exceptions](http://msdn.microsoft.com/library/hh418082.aspx). The list provides information about which if these indicate that retrying the operation is appropriate. For example, a [ServerBusyException](http://msdn.microsoft.com/library/microsoft.servicebus.messaging.serverbusyexception.aspx) indicates that the client should wait for a period of time, then retry the operation. The occurrence of a **ServerBusyException** also causes Service Bus to switch to a different mode, in which an extra 10-second delay is added to the computed retry delays. This mode is reset after a short period. + +The exceptions returned from Service Bus expose the **IsTransient** property that indicates if the client should retry the operation. The built-in **RetryExponential** policy relies on the **IsTransient** property in the **MessagingException** class, which is the base class for all Service Bus exceptions. If you create custom implementations of the **RetryPolicy** base class you could use a combination of the exception type and the **IsTransient** property to provide more fine-grained control over retry actions. For example, you could detect a **QuotaExceededException** and take action to drain the queue before retrying sending a message to it. + +### Policy configuration +Retry policies are set programmatically, and can be set as a default policy for a **NamespaceManager** and for a **MessagingFactory**, or individually for each messaging client. To set the default retry policy for a messaging session you set the **RetryPolicy** of the **NamespaceManager**. + + namespaceManager.Settings.RetryPolicy = new RetryExponential(minBackoff: TimeSpan.FromSeconds(0.1), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); + +Note that this code uses named parameters for clarity. Alternatively you can omit the names because none of the parameters is optional. + + namespaceManager.Settings.RetryPolicy = new RetryExponential(TimeSpan.FromSeconds(0.1), + TimeSpan.FromSeconds(30), TimeSpan.FromSeconds(2), TimeSpan.FromSeconds(5), 3); + +To set the default retry policy for all clients created from a messaging factory, you set the **RetryPolicy** of the **MessagingFactory**. + + messagingFactory.RetryPolicy = new RetryExponential(minBackoff: TimeSpan.FromSeconds(0.1), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); + +To set the retry policy for a messaging client, or to override its default policy, you set its **RetryPolicy** property using an instance of the required policy class: + +```csharp +client.RetryPolicy = new RetryExponential(minBackoff: TimeSpan.FromSeconds(0.1), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); +``` + +The retry policy cannot be set at the individual operation level. It applies to all operations for the messaging client. +The following table shows the default settings for the built-in retry policy. + +![Retry guidance table](./images/retry-service-specific/RetryServiceSpecificGuidanceTable7.png) + +### Retry usage guidance +Consider the following guidelines when using Service Bus: + +* When using the built-in **RetryExponential** implementation, do not implement a fallback operation as the policy reacts to Server Busy exceptions and automatically switches to an appropriate retry mode. +* Service Bus supports a feature called Paired Namespaces, which implements automatic failover to a backup queue in a separate namespace if the queue in the primary namespace fails. Messages from the secondary queue can be sent back to the primary queue when it recovers. This feature helps to address transient failures. For more information, see [Asynchronous Messaging Patterns and High Availability](http://msdn.microsoft.com/library/azure/dn292562.aspx). + +Consider starting with following settings for retrying operations. These are general purpose settings, and you should monitor the operations and fine tune the values to suit your own scenario. + +![Retry guidance table](./images/retry-service-specific/RetryServiceSpecificGuidanceTable8.png) + +### Telemetry +Service Bus logs retries as ETW events using an **EventSource**. You must attach an **EventListener** to the event source to capture the events and view them in Performance Viewer, or write them to a suitable destination log. You could use the [Semantic Logging Application Block](http://msdn.microsoft.com/library/dn775006.aspx) to do this. The retry events are of the following form: + +```text +Microsoft-ServiceBus-Client/RetryPolicyIteration +ThreadID="14,500" +FormattedMessage="[TrackingId:] RetryExponential: Operation Get:https://retry-tests.servicebus.windows.net/TestQueue/?api-version=2014-05 at iteration 0 is retrying after 00:00:00.1000000 sleep because of Microsoft.ServiceBus.Messaging.MessagingCommunicationException: The remote name could not be resolved: 'retry-tests.servicebus.windows.net'.TrackingId:6a26f99c-dc6d-422e-8565-f89fdd0d4fe3, TimeStamp:9/5/2014 10:00:13 PM." +trackingId="" +policyType="RetryExponential" +operation="Get:https://retry-tests.servicebus.windows.net/TestQueue/?api-version=2014-05" +iteration="0" +iterationSleep="00:00:00.1000000" +lastExceptionType="Microsoft.ServiceBus.Messaging.MessagingCommunicationException" +exceptionMessage="The remote name could not be resolved: 'retry-tests.servicebus.windows.net'.TrackingId:6a26f99c-dc6d-422e-8565-f89fdd0d4fe3,TimeStamp:9/5/2014 10:00:13 PM" +``` + +### Examples +The following code example shows how to set the retry policy for: + +* A namespace manager. The policy applies to all operations on that manager, and cannot be overridden for individual operations. +* A messaging factory. The policy applies to all clients created from that factory, and cannot be overridden when creating individual clients. +* An individual messaging client. After a client has been created, you can set the retry policy for that client. The policy applies to all operations on that client. + +```csharp +using System; +using System.Threading.Tasks; +using Microsoft.ServiceBus; +using Microsoft.ServiceBus.Messaging; + +namespace RetryCodeSamples +{ + class ServiceBusCodeSamples + { + private const string connectionString = + @"Endpoint=sb://[my-namespace].servicebus.windows.net/; + SharedAccessKeyName=RootManageSharedAccessKey; + SharedAccessKey=C99..........Mk="; + + public async static Task Samples() + { + const string QueueName = "TestQueue"; + + ServiceBusEnvironment.SystemConnectivity.Mode = ConnectivityMode.Http; + + var namespaceManager = NamespaceManager.CreateFromConnectionString(connectionString); + + // The namespace manager will have a default exponential policy with 10 retry attempts + // and a 3 second delay delta. + // Retry delays will be approximately 0 sec, 3 sec, 9 sec, 25 sec and the fixed 30 sec, + // with an extra 10 sec added when receiving a ServiceBusyException. + + { + // Set different values for the retry policy, used for all operations on the namespace manager. + namespaceManager.Settings.RetryPolicy = + new RetryExponential( + minBackoff: TimeSpan.FromSeconds(0), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); + + // Policies cannot be specified on a per-operation basis. + if (!await namespaceManager.QueueExistsAsync(QueueName)) + { + await namespaceManager.CreateQueueAsync(QueueName); + } + } + + + var messagingFactory = MessagingFactory.Create( + namespaceManager.Address, namespaceManager.Settings.TokenProvider); + // The messaging factory will have a default exponential policy with 10 retry attempts + // and a 3 second delay delta. + // Retry delays will be approximately 0 sec, 3 sec, 9 sec, 25 sec and the fixed 30 sec, + // with an extra 10 sec added when receiving a ServiceBusyException. + + { + // Set different values for the retry policy, used for clients created from it. + messagingFactory.RetryPolicy = + new RetryExponential( + minBackoff: TimeSpan.FromSeconds(1), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); + + + // Policies cannot be specified on a per-operation basis. + var session = await messagingFactory.AcceptMessageSessionAsync(); + } + + + { + var client = messagingFactory.CreateQueueClient(QueueName); + // The client inherits the policy from the factory that created it. + + + // Set different values for the retry policy on the client. + client.RetryPolicy = + new RetryExponential( + minBackoff: TimeSpan.FromSeconds(0.1), + maxBackoff: TimeSpan.FromSeconds(30), + maxRetryCount: 3); + + + // Policies cannot be specified on a per-operation basis. + var session = await client.AcceptMessageSessionAsync(); + } + } + } +} +``` + +### More information +* [Asynchronous Messaging Patterns and High Availability](http://msdn.microsoft.com/library/azure/dn292562.aspx) + +## Azure Redis Cache retry guidelines +Azure Redis Cache is a fast data access and low latency cache service based on the popular open source Redis Cache. It is secure, managed by Microsoft, and is accessible from any application in Azure. + +The guidance in this section is based on using the StackExchange.Redis client to access the cache. A list of other suitable clients can be found on the [Redis website](http://redis.io/clients), and these may have different retry mechanisms. + +Note that the StackExchange.Redis client uses multiplexing through a single connection. The recommended usage is to create an instance of the client at application startup and use this instance for all operations against the cache. For this reason, the connection to the cache is made only once, and so all of the guidance in this section is related to the retry policy for this initial connection—and not for each operation that accesses the cache. + +### Retry mechanism +The StackExchange.Redis client uses a connection manager class that is configured through a set of options. These options include a **ConnectRetry** property that specifies the number of times a failed connection to the cache will be retried. However, the retry policy in used only for the initial connect action, and it does not wait between retries. + +### Policy configuration +Retry policies are configured programmatically by setting the options for the client before connecting to the cache. This can be done by creating an instance of the **ConfigurationOptions** class, populating its properties, and passing it to the **Connect** method. + +```csharp +var options = new ConfigurationOptions { EndPoints = { "localhost" }, + ConnectRetry = 3, + ConnectTimeout = 2000 }; +ConnectionMultiplexer redis = ConnectionMultiplexer.Connect(options, writer); +``` + +Note that the **ConnectTimeout** property specifies the maximum waiting time in milliseconds), not the delay between retries. + +Alternatively, you can specify the options as a string, and pass this to the **Connect** method. + +```csharp + var options = "localhost,connectRetry=3,connectTimeout=2000"; + ConnectionMultiplexer redis = ConnectionMultiplexer.Connect(options, writer); +``` + +It is also possible to specify options directly when you connect to the cache. + +```csharp +var conn = ConnectionMultiplexer.Connect("redis0:6380,redis1:6380,connectRetry=3"); +``` + +The following table shows the default settings for the built-in retry policy. + +| **Context** | **Setting** | **Default value**
    (v 1.0.331) | **Meaning** | +| --- | --- | --- | --- | +| ConfigurationOptions |ConnectRetry

    ConnectTimeout

    SyncTimeout |3

    Maximum 5000 ms plus SyncTimeout
    1000 |The number of times to repeat connect attempts during the initial connection operation.
    Timeout (ms) for connect operations. Not a delay between retry attempts.
    Time (ms) to allow for synchronous operations. | + +> [!NOTE] +> SyncTimeout contributes to the end-to-end latency of an operation. However, in general, using synchronous operations is not recommended. For more information see [Pipelines and Multiplexers](http://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/PipelinesMultiplexers.md). +> +> + +### Retry usage guidance +Consider the following guidelines when using Azure Redis Cache: + +* The StackExchange Redis client manages its own retries, but only when establishing a connection to the cache when the application first starts. You can configure the connection timeout and the number of retry attempts to establish this connection, but the retry policy does not apply to operations against the cache. +* The retry mechanism has no delay between retry attempts. It simply retries a failed connection after the specified connection timeout expires, and for the specified number of times. +* Instead of using a large number of retry attempts, consider falling back by accessing the original data source instead. + +### Telemetry +You can collect information about connections (but not other operations) using a **TextWriter**. + +```csharp +var writer = new StringWriter(); +... +ConnectionMultiplexer redis = ConnectionMultiplexer.Connect(options, writer); +``` + +An example of the output this generates is shown below. + +```text +localhost:6379,connectTimeout=2000,connectRetry=3 +1 unique nodes specified +Requesting tie-break from localhost:6379 > __Booksleeve_TieBreak... +Allowing endpoints 00:00:02 to respond... +localhost:6379 faulted: SocketFailure on PING +localhost:6379 failed to nominate (Faulted) +> UnableToResolvePhysicalConnection on GET +No masters detected +localhost:6379: Standalone v2.0.0, master; keep-alive: 00:01:00; int: Connecting; sub: Connecting; not in use: DidNotRespond +localhost:6379: int ops=0, qu=0, qs=0, qc=1, wr=0, sync=1, socks=2; sub ops=0, qu=0, qs=0, qc=0, wr=0, socks=2 +Circular op-count snapshot; int: 0 (0.00 ops/s; spans 10s); sub: 0 (0.00 ops/s; spans 10s) +Sync timeouts: 0; fire and forget: 0; last heartbeat: -1s ago +resetting failing connections to retry... +retrying; attempts left: 2... +... +``` + +### Examples +The following code example shows how you can configure the connection timeout setting and the number of retries when initializing the StackExchange.Redis client to access Azure Redis Cache at application startup. Note that the connection timeout is the period of time that you are willing to wait for connection to the cache; it is not the delay between retry attempts. + +This example shows how to set the configuration using an instance of the **ConfigurationOptions**. + +```csharp +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using StackExchange.Redis; + +namespace RetryCodeSamples +{ + class CacheRedisCodeSamples + { + public async static Task Samples() + { + var writer = new StringWriter(); + + { + try + { + // Using object-based configuration. + var options = new ConfigurationOptions + { + EndPoints = { "localhost" }, + ConnectRetry = 3, + ConnectTimeout = 2000 // The maximum waiting time (ms), not the delay for retries. + }; + ConnectionMultiplexer redis = ConnectionMultiplexer.Connect(options, writer); + + // Store a reference to the multiplexer for use in the application. + } + catch + { + Console.WriteLine(writer.ToString()); + throw; + } + } + } + } +} +``` + +This example shows how to set the configuration by specifying the options as a string. + +```csharp +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using System.Threading.Tasks; +using StackExchange.Redis; + +namespace RetryCodeSamples +{ + class CacheRedisCodeSamples + { + public async static Task Samples() + { + var writer = new StringWriter(); + + { + try + { + // Using string-based configuration. + var options = "localhost,connectRetry=3,connectTimeout=2000"; + ConnectionMultiplexer redis = ConnectionMultiplexer.Connect(options, writer); + + // Store a reference to the multiplexer for use in the application. + } + catch + { + Console.WriteLine(writer.ToString()); + throw; + } + } + } + } +} +``` + +For more examples, see [Configuration](http://github.com/StackExchange/StackExchange.Redis/blob/master/Docs/Configuration.md#configuration) on the project website. + +### More information +* [Redis website](http://redis.io/) + +## DocumentDB retry guidelines +DocumentDB is a fully-managed document database-as-a-service with rich query and indexing capabilities over a schema-free JSON data model. It offers configurable and reliable performance, native JavaScript transactional processing, and is built for the cloud with elastic scale. + +### Retry mechanism +The `DocumentClient` class automatically retries failed attempts. To set the number of retries and the maximum wait time, configure [ConnectionPolicy.RetryOptions]. Exceptions that the client raises are either beyond the retry policy or are not transient errors. + +If DocumentDB throttles the client, it returns an HTTP 429 error. Check the status code in the `DocumentClientException`. + +### Policy configuration +The following table shows the default settings for the `RetryOptions` class. + +| Setting | Default value | Description | +| --- | --- | --- | +| MaxRetryAttemptsOnThrottledRequests |9 |The maximum number of retries if the request fails because DocumentDB applied rate limiting on the client. | +| MaxRetryWaitTimeInSeconds |30 |The maximum retry time in seconds. | + +### Example +```csharp +DocumentClient client = new DocumentClient(new Uri(endpoint), authKey); ; +var options = client.ConnectionPolicy.RetryOptions; +options.MaxRetryAttemptsOnThrottledRequests = 5; +options.MaxRetryWaitTimeInSeconds = 15; +``` + +### Telemetry +Retry attempts are logged as unstructured trace messages through a .NET **TraceSource**. You must configure a **TraceListener** to capture the events and write them to a suitable destination log. + +For example, if you add the following to your App.config file, traces will be generated in a text file in the same location as the executable: + +``` + + + + + + + + + + + + + + +``` + + +## Azure Search retry guidelines +Azure Search can be used to add powerful and sophisticated search capabilities to a website or application, quickly and easily tune search results, and construct rich and fine-tuned ranking models. + +### Retry mechanism +Retry behavior in the Azure Search SDK is controlled by the `SetRetryPolicy` method on the [SearchServiceClient] and [SearchIndexClient] classes. The default policy retries with exponential backoff when Azure Search returns a 5xx or 408 (Request Timeout) response. + +### Telemetry +Trace with ETW or by registering a custom trace provider. For more information, see the [AutoRest documentation][autorest]. + +## Azure Active Directory retry guidelines +Azure Active Directory (AD) is a comprehensive identity and access management cloud solution that combines core directory services, advanced identity governance, security, and application access management. Azure AD also offers developers an identity management platform to deliver access control to their applications, based on centralized policy and rules. + +### Retry mechanism +There is no built-in retry mechanism for Azure Active Directory in the Active Directory Authentication Library (ADAL). You can use the Transient Fault Handling Application Block to implement a retry strategy that contains a custom detection mechanism for the exceptions returned by Active Directory. + +### Policy configuration (Azure Active Directory) +When using the Transient Fault Handling Application Block with Azure Active Directory you create a **RetryPolicy** instance based on a class that defines the detection strategy you want to use. + +```csharp +var policy = new RetryPolicy(new ExponentialBackoff(retryCount: 5, + minBackoff: TimeSpan.FromSeconds(0), + maxBackoff: TimeSpan.FromSeconds(60), + deltaBackoff: TimeSpan.FromSeconds(2))); +``` + +You then call the **ExecuteAction** or **ExecuteAsync** method of the retry policy, passing in the operation you want to execute. + +```csharp +var result = await policy.ExecuteAsync(() => authContext.AcquireTokenAsync(resourceId, clientId, uc)); +``` + +The detection strategy class receives exceptions when a failure occurs, and must detect whether this is likely to be a transient fault or a more permanent failure. Typically it will do this by examining the exception type and status code. For example, a Service Unavailable response indicates that a retry attempt should be made. The Transient Fault Handling Application Block does not include a detection strategy class that is suitable for use with the ADAL client, but an example of a custom detection strategy is provided in the [Examples](#examples) section below. Using a custom detection strategy is no different from using one supplied with the block. + +Default strategies for the Transient Fault Handling Application Block are shown in the section [Transient Fault Handling Application Block (Topaz) strategies](#transient-fault-handling-application-block-topaz-strategies) at the end of this guidance. + +### Retry usage guidance +Consider the following guidelines when using Azure Active Directory: + +* If you are using the REST API for Azure Active Directory, you should retry the operation only if the result is an error in the 5xx range (such as 500 Internal Server Error, 502 Bad Gateway, 503 Service Unavailable, and 504 Gateway Timeout). Do not retry for any other errors. +* If you are using the Active Directory Authentication Library (ADAL), HTTP codes are not readily accessible. You will need to create a custom detection strategy that includes logic to check the properties of the ADAL-specific exceptions. See the [Examples](#examples) section below. +* An exponential back-off policy is recommended for use in batch scenarios with Azure Active Directory. + +Consider starting with following settings for retrying operations. These are general purpose settings, and you should monitor the operations and fine tune the values to suit your own scenario. + +| **Context** | **Sample target E2E
    max latency** | **Retry strategy** | **Settings** | **Values** | **How it works** | +| --- | --- | --- | --- | --- | --- | +| Interactive, UI,
    or foreground |2 sec |FixedInterval |Retry count
    Retry interval
    First fast retry |3
    500 ms
    true |Attempt 1 - delay 0 sec
    Attempt 2 - delay 500 ms
    Attempt 3 - delay 500 ms | +| Background or
    batch |60 sec |ExponentialBackoff |Retry count
    Min back-off
    Max back-off
    Delta back-off
    First fast retry |5
    0 sec
    60 sec
    2 sec
    false |Attempt 1 - delay 0 sec
    Attempt 2 - delay ~2 sec
    Attempt 3 - delay ~6 sec
    Attempt 4 - delay ~14 sec
    Attempt 5 - delay ~30 sec | + +### Examples +The following code example shows how you can use the Transient Fault Handling Application Block (Topaz) to define a custom transient error detection strategy suitable for use with the ADAL client. The code creates a new **RetryPolicy** instance based on a custom detection strategy of type **AdalDetectionStrategy**, as defined in the code listing below. Custom detection strategies for Topaz implement the **ITransientErrorDetectionStrategy** interface and return true if a retry should be attempted, or **false** if the failure appears to be non-transient and a retry should not be attempted. + + using System; + using System.Linq; + using System.Net; + using System.Threading.Tasks; + using Microsoft.Practices.TransientFaultHandling; + using Microsoft.IdentityModel.Clients.ActiveDirectory; + + namespace RetryCodeSamples + { + class ActiveDirectoryCodeSamples + { + public async static Task Samples() + { + var authority = "[some authority]"; + var resourceId = “[some resource id]”; + var clientId = “[some client id]”; + + var authContext = new AuthenticationContext(authority); + + var uc = new UserCredential(“[user]", "[password]"); + + // Use Topaz with a custom detection strategy to manage retries. + var policy = + new RetryPolicy( + new ExponentialBackoff( + retryCount: 5, + minBackoff: TimeSpan.FromSeconds(0), + maxBackoff: TimeSpan.FromSeconds(60), + deltaBackoff: TimeSpan.FromSeconds(2))); + + var result = await policy.ExecuteAsync(() => authContext.AcquireTokenAsync(resourceId, clientId, uc)); + + // Get the access token + var accessToken = result.AccessToken; + + // Use the result, probably to authorize an API call. + } + } + + // TODO: This is sample code that needs validation from the WAAD team! + // based on existing detection strategies + public class AdalDetectionStrategy : ITransientErrorDetectionStrategy + { + private static readonly WebExceptionStatus[] webExceptionStatus = + new[] + { + WebExceptionStatus.ConnectionClosed, + WebExceptionStatus.Timeout, + WebExceptionStatus.RequestCanceled + }; + + private static readonly HttpStatusCode[] httpStatusCodes = + new[] + { + HttpStatusCode.InternalServerError, + HttpStatusCode.GatewayTimeout, + HttpStatusCode.ServiceUnavailable, + HttpStatusCode.RequestTimeout + }; + + public bool IsTransient(Exception ex) + { + var adalException = ex as AdalException; + if (adalException == null) + { + return false; + } + + if (adalException.ErrorCode == AdalError.ServiceUnavailable) + { + return true; + } + + var innerWebException = adalException.InnerException as WebException; + if (innerWebException != null) + { + if (webExceptionStatus.Contains(innerWebException.Status)) + { + return true; + } + + if (innerWebException.Status == WebExceptionStatus.ProtocolError) + { + var response = innerWebException.Response as HttpWebResponse; + return response != null && httpStatusCodes.Contains(response.StatusCode); + } + } + + return false; + } + } + } + +For information about retrying Active Directory Graph API operations and the error codes returned see: + +* [Code Sample: Retry Logic](http://msdn.microsoft.com/library/azure/dn448547.aspx) +* [Azure AD Graph Error Codes](http://msdn.microsoft.com/library/azure/hh974480.aspx) + +### More information +* [Implementing a Custom Detection Strategy](http://msdn.microsoft.com/library/hh680940.aspx) (Topaz) +* [Implementing a Custom Retry Strategy](http://msdn.microsoft.com/library/hh680943.aspx) (Topaz) +* [Token Issuance and Retry Guidelines](http://msdn.microsoft.com/library/azure/dn168916.aspx) + +## General REST and retry guidelines +Consider the following when accessing Azure or third party services: + +* Use a systematic approach to managing retries, perhaps as reusable code, so that you can apply a consistent methodology across all clients and all solutions. +* Consider using a retry framework such as the Transient Fault Handling Application Block to manage retries if the target service or client has no built-in retry mechanism. This will help you implement a consistent retry behavior, and it may provide a suitable default retry strategy for the target service. However, you may need to create custom retry code for services that have non-standard behavior, that do not rely on exceptions to indicate transient failures, or if you want to use a **Retry-Response** reply to manage retry behavior. +* The transient detection logic will depend on the actual client API you use to invoke the REST calls. Some clients, such as the newer **HttpClient** class, will not throw exceptions for completed requests with a non-success HTTP status code. This improves performance but prevents the use of the Transient Fault Handling Application Block. In this case you could wrap the call to the REST API with code that produces exceptions for non-success HTTP status codes, which can then be processed by the block. Alternatively, you can use a different mechanism to drive the retries. +* The HTTP status code returned from the service can help to indicate whether the failure is transient. You may need to examine the exceptions generated by a client or the retry framework to access the status code or to determine the equivalent exception type. The following HTTP codes typically indicate that a retry is appropriate: + * 408 Request Timeout + * 500 Internal Server Error + * 502 Bad Gateway + * 503 Service Unavailable + * 504 Gateway Timeout +* If you base your retry logic on exceptions, the following typically indicate a transient failure where no connection could be established: + * WebExceptionStatus.ConnectionClosed + * WebExceptionStatus.ConnectFailure + * WebExceptionStatus.Timeout + * WebExceptionStatus.RequestCanceled +* In the case of a service unavailable status, the service might indicate the appropriate delay before retrying in the **Retry-After** response header or a different custom header (as in the DocumentDB service). Services might also send additional information as custom headers, or embedded in the content of the response. The Transient Fault Handling Application Block cannot use the standard or any custom “retry-after” headers. +* Do not retry for status codes representing client errors (errors in the 4xx range) except for a 408 Request Timeout. +* Thoroughly test your retry strategies and mechanisms under a range of conditions, such as different network states and varying system loadings. + +### Retry strategies +The following are the typical types of retry strategy intervals: + +* **Exponential**: A retry policy that performs a specified number of retries, using a randomized exponential back off approach to determine the interval between retries. For example: + + var random = new Random(); + + var delta = (int)((Math.Pow(2.0, currentRetryCount) - 1.0) * + random.Next((int)(this.deltaBackoff.TotalMilliseconds * 0.8), + (int)(this.deltaBackoff.TotalMilliseconds * 1.2))); + var interval = (int)Math.Min(checked(this.minBackoff.TotalMilliseconds + delta), + this.maxBackoff.TotalMilliseconds); + retryInterval = TimeSpan.FromMilliseconds(interval); +* **Incremental**: A retry strategy with a specified number of retry attempts and an incremental time interval between retries. For example: + + retryInterval = TimeSpan.FromMilliseconds(this.initialInterval.TotalMilliseconds + + (this.increment.TotalMilliseconds * currentRetryCount)); +* **LinearRetry**: A retry policy that performs a specified number of retries, using a specified fixed time interval between retries. For example: + + retryInterval = this.deltaBackoff; + +### More information +* [Circuit breaker strategies](http://msdn.microsoft.com/library/dn589784.aspx) + +## Transient Fault Handling Application Block (Topaz) strategies +The Transient Fault Handling Application Block has the following default strategies. + +| **Strategy** | **Setting** | **Default value** | **Meaning** | +| --- | --- | --- | --- | +| **Exponential** |retryCount
    minBackoff

    maxBackoff

    deltaBackoff

    fastFirstRetry |10
    1 second

    30 seconds

    10 seconds

    true |The number of retry attempts.
    The minimum back-off time. The higher of this value or the computed back-off will be used as the retry delay.
    The minimum back-off time. The lower of this value or the computed back-off will be used as the retry delay.
    The value used to calculate a random delta for the exponential delay between retries.
    Whether the first retry attempt will be made immediately. | +| **Incremental** |retryCount
    initialInterval
    increment

    fastFirstRetry
    |10
    1 second
    1 second

    true |The number of retry attempts.
    The initial interval that will apply for the first retry.
    The incremental time value that will be used to calculate the progressive delay between retries.
    Whether the first retry attempt will be made immediately. | +| **Linear (fixed interval)** |retryCount
    retryInterval
    fastFirstRetry
    |10
    1 second
    true |The number of retry attempts.
    The delay between retries.
    Whether first retry attempt will be made immediately. | + +For examples of using the Transient Fault Handling Application Block, see the Examples sections earlier in this guidance for Azure SQL Database using ADO.NET and Azure Active Directory. + + + +[autorest]: https://github.com/Azure/autorest/tree/master/docs +[ConnectionPolicy.RetryOptions]: https://msdn.microsoft.com/library/azure/microsoft.azure.documents.client.connectionpolicy.retryoptions.aspx +[entlib]: http://msdn.microsoft.com/library/dn440719.aspx +[SearchIndexClient]: https://msdn.microsoft.com/library/azure/microsoft.azure.search.searchindexclient.aspx +[SearchServiceClient]: https://msdn.microsoft.com/library/microsoft.azure.search.searchserviceclient.aspx diff --git a/docs/best-practices/toc.md b/docs/best-practices/toc.md new file mode 100644 index 00000000000..5e2c62e1905 --- /dev/null +++ b/docs/best-practices/toc.md @@ -0,0 +1,12 @@ +# Best Practices +## [API design](./api-design.md) +## [API implementation](./api-implementation.md) +## [Autoscaling](./auto-scaling.md) +## [Background jobs](./background-jobs.md) +## [Caching](./caching.md) +## [Content Delivery Network](./cdn.md) +## [Data partitioning](./data-partitioning.md) +## [Monitoring and diagnostics](./monitoring.md) +## [Naming conventions](./naming-conventions.md) +## [Retry guidance for specific services](./retry-service-specific.md) +## [Transient fault handling](./transient-faults.md) diff --git a/docs/best-practices/transient-faults.md b/docs/best-practices/transient-faults.md new file mode 100644 index 00000000000..9a5066b3409 --- /dev/null +++ b/docs/best-practices/transient-faults.md @@ -0,0 +1,116 @@ +--- +title: Retry general guidance +description: Guidance on retry for transient fault handling. +services: '' +documentationcenter: na +author: dragon119 +manager: christb +editor: '' +tags: '' + +pnp.series.title: Best Practices + +ms.assetid: 5115cbf2-ecdd-42a4-8191-886de7bbadef +ms.service: best-practice +ms.devlang: na +ms.topic: article +ms.tgt_pltfrm: na +ms.workload: na +ms.date: 07/13/2016 +ms.author: masashin + +--- +# Transient fault handling +[!INCLUDE [header](../_includes/header.md)] + +All applications that communicate with remote services and resources must be sensitive to transient faults. This is especially the case for applications that run in the cloud, where the nature of the environment and connectivity over the Internet means these types of faults are likely to be encountered more often. Transient faults include the momentary loss of network connectivity to components and services, the temporary unavailability of a service, or timeouts that arise when a service is busy. These faults are often self-correcting, and if the action is repeated after a suitable delay it is likely succeed. + +This document covers general guidance for transient fault handling. For information about handling transient faults when using Microsoft Azure services, see [Azure service-specific retry guidelines](./retry-service-specific.md). + +## Why do transient faults occur in the cloud? +Transient faults can occur in any environment, on any platform or operating system, and in any kind of application. In solutions that run on local, on-premises infrastructure, performance and availability of the application and its components is typically maintained through expensive and often under-used hardware redundancy, and components and resources are located close to each another. While this makes a failure less likely, it can still result in transient faults - and even an outage through unforeseen events such as external power supply or network issues, or other disaster scenarios. + +Cloud hosting, including private cloud systems, can offer a higher overall availability by using shared resources, redundancy, automatic failover, and dynamic resource allocation across a huge number of commodity compute nodes. However, the nature of these environments can mean that transient faults are more likely to occur. There are several reasons for this: + +* Many resources in a cloud environment are shared, and access to these resources is subject to throttling in order to protect the resource. Some services will refuse connections when the load rises to a specific level, or a maximum throughput rate is reached, in order to allow processing of existing requests and to maintain performance of the service for all users. Throttling helps to maintain the quality of service for neighbors and other tenants using the shared resource. +* Cloud environments are built using vast numbers of commodity hardware units. They deliver performance by dynamically distributing the load across multiple computing units and infrastructure components, and deliver reliability by automatically recycling or replacing failed units. This dynamic nature means that transient faults and temporary connection failures may occasionally occur. +* There are often more hardware components, including network infrastructure such as routers and load balancers, between the application and the resources and services it uses. This additional infrastructure can occasionally introduce additional connection latency and transient connection faults. +* Network conditions between the client and the server may be variable, especially when communication crosses the Internet. Even in on-premises locations, very heavy traffic loads may slow communication and cause intermittent connection failures. + +## Challenges +Transient faults can have a huge impact on the perceived availability of an application, even if it has been thoroughly tested under all foreseeable circumstances. To ensure that cloud-hosted applications operate reliably, they must be able to respond to the following challenges: + +* The application must be able to detect faults when they occur, and determine if these faults are likely to be transient, more long-lasting, or are terminal failures. Different resources are likely to return different responses when a fault occurs, and these responses may also vary depending on the context of the operation; for example, the response for an error when reading from storage may be different from response for an error when writing to storage. Many resources and services have well-documented transient failure contracts. However, where such information is not available, it may be difficult to discover the nature of the fault and whether it is likely to be transient. +* The application must be able to retry the operation if it determines that the fault is likely to be transient and keep track of the number of times the operation was retried. +* The application must use an appropriate strategy for the retries. This strategy specifies the number of times it should retry, the delay between each attempt, and the actions to take after a failed attempt. The appropriate number of attempts and the delay between each one are often difficult to determine, and vary based on the type of resource as well as the current operating conditions of the resource and the application itself. + +## General guidelines +The following guidelines will help you to design a suitable transient fault handing mechanism for your applications: + +* **Determine if there is a built-in retry mechanism:** + * Many services provide an SDK or client library that contains a transient fault handling mechanism. The retry policy it uses is typically tailored to the nature and requirements of the target service. Alternatively, REST interfaces for services may return information that is useful in determining whether a retry is appropriate, and how long to wait before the next retry attempt. + * Use the built-in retry mechanism where one is available unless you have specific and well-understood requirements that mean a different retry behavior is more appropriate. +* **Determine if the operation is suitable for retrying**: + * You should only retry operations where the faults are transient (typically indicated by the nature of the error), and if there is at least some likelihood that the operation will succeed when reattempted. There is no point in reattempting operations that indicate an invalid operation such as a database update to an item that does not exist, or requests to a service or resource that has suffered a fatal error + * In general, you should implement retries only where the full impact of this can be determined, and the conditions are well understood and can be validated. If not, leave it to the calling code to implement retries. Remember that the errors returned from resources and services outside your control may evolve over time, and you may need to revisit your transient fault detection logic. + * When you create services or components, consider implementing error codes and messages that will help clients determine whether they should retry failed operations. In particular, indicate if the client should retry the operation (perhaps by returning an **isTransient** value) and suggest a suitable delay before the next retry attempt. If you build a web service, consider returning custom errors defined within your service contracts. Even though generic clients may not be able to read these, they will be useful when building custom clients. +* **Determine an appropriate retry count and interval:** + * It is vital to optimize the retry count and the interval to the type of use case. If you do not retry a sufficient number of times, the application will be unable to complete the operation and is likely to experience a failure. If you retry too many times, or with too short an interval between tries, the application can potentially hold resources such as threads, connections, and memory for long periods, which will adversely affect the health of the application. + * The appropriate values for the time interval and the number of retry attempts depend on the type of operation being attempted. For example, if the operation is part of a user interaction, the interval should be short and only a few retries attempted to avoid making users wait for a response (which holds open connections and can reduce availability for other users). If the operation is part of a long running or critical workflow, where cancelling and restarting the process is expensive or time-consuming, it is appropriate to wait longer between attempts and retry more times. + * Determining the appropriate intervals between retries is the most difficult part of designing a successful strategy. Typical strategies use the following types of retry interval: + * **Exponential back-off**. The application waits a short time before the first retry, and then exponentially increasing times between each subsequent retry. For example, it may retry the operation after 3 seconds, 12 seconds, 30 seconds, and so on. + * **Incremental intervals**. The application waits a short time before the first retry, and then incrementally increasing times between each subsequent retry. For example, it may retry the operation after 3 seconds, 7 seconds, 13 seconds, and so on. + * **Regular intervals**. The application waits for the same period of time between each attempt. For example, it may retry the operation every 3 seconds. + * **Immediate retry**. Sometimes a transient fault is extremely short, perhaps caused by an event such as a network packet collision or a spike in a hardware component. In this case, retrying the operation immediately is appropriate because it may succeed if the fault has cleared in the time it takes the application to assemble and send the next request. However, there should never be more than one immediate retry attempt, and you should switch to alternative strategies, such as such as exponential back-off or fallback actions, if the immediate retry fails. + * **Randomization**. Any of the retry strategies listed above may include a randomization to prevent multiple instances of the client sending subsequent retry attempts at the same time. For example, one instance may retry the operation after 3 seconds, 11 seconds, 28 seconds, and so on while another instance may retry the operation after 4 seconds, 12 seconds, 26 seconds, and so on. Randomization is a useful technique that may be combined with other strategies. + * As a general guideline, use an exponential back-off strategy for background operations, and immediate or regular interval retry strategies for interactive operations. In both cases, you should choose the delay and the retry count so that the maximum latency for all retry attempts is within the required end-to-end latency requirement. + * Take into account the combination of all the factors that contribute to the overall maximum timeout for a retried operation. These factors include the time taken for a failed connection to produce a response (typically set by a timeout value in the client) as well as the delay between retry attempts and the maximum number of retries. The total of all these times can result in very large overall operation times, especially when using an exponential delay strategy where the interval between retries grows rapidly after each failure. If a process must meet a specific service level agreement (SLA), the overall operation time, including all timeouts and delays, must be within that defined in the SLA + * Over-aggressive retry strategies, which have too short intervals or too may retries, can have an adverse effect on the target resource or service. This may prevent the resource or service from recovering from its overloaded state, and it will continue to block or refuse requests. This results in a vicious circle where more and more requests are sent to the resource or service, and consequently its ability to recover is further reduced. + * Take into account the timeout of the operations when choosing the retry intervals to avoid launching a subsequent attempt immediately (for example, if the timeout period is similar to the retry interval). Also consider if you need to keep the total possible period (the timeout plus the retry intervals) to below a specific total time. Operations that have unusually short or very long timeouts may influence how long to wait, and how often to retry the operation. + * Use the type of the exception and any data it contains, or the error codes and messages returned from the service, to optimize the interval and the number of retries. For example, some exceptions or error codes (such as the HTTP code 503 Service Unavailable with a Retry-After header in the response) may indicate how long the error might last, or that the service has failed and will not respond to any subsequent attempt. +* **Avoid anti-patterns**: + * In the vast majority of cases, you should avoid implementations that include duplicated layers of retry code. Avoid designs that include cascading retry mechanisms, or that implement retry at every stage of an operation that involves a hierarchy of requests, unless you have specific requirements that demand this. In these exceptional circumstances, use policies that prevent excessive numbers of retries and delay periods, and make sure you understand the consequences. For example, if one component makes a request to another, which then accesses the target service, and you implement retry with a count of three on both calls there will be nine retry attempts in total against the service. Many services and resources implement a built-in retry mechanism and you should investigate how you can disable or modify this if you need to implement retries at a higher level. + * Never implement an endless retry mechanism. This is likely to prevent the resource or service recovering from overload situations, and cause throttling and refused connections to continue for a longer period. Use a finite number or retries, or implement a pattern such as [Circuit Breaker](http://msdn.microsoft.com/library/dn589784.aspx) to allow the service to recover. + * Never perform an immediate retry more than once. + * Avoid using a regular retry interval, especially when you have a large number of retry attempts, when accessing services and resources in Azure. The optimum approach is this scenario is an exponential back-off strategy with a circuit-breaking capability. + * Prevent multiple instances of the same client, or multiple instances of different clients, from sending retries at the same times. If this is likely to occur, introduce randomization into the retry intervals. +* **Test your retry strategy and implementation:** + * Ensure you fully test your retry strategy implementation under as wide a set of circumstances as possible, especially when both the application and the target resources or services it uses are under extreme load. To check behavior during testing, you can: + * Inject transient and non-transient faults into the service. For example, send invalid requests or add code that detects test requests and responds with different types of errors. For an example using TestApi, see [Fault Injection Testing with TestApi](http://msdn.microsoft.com/magazine/ff898404.aspx) and [Introduction to TestApi – Part 5: Managed Code Fault Injection APIs](http://blogs.msdn.com/b/ivo_manolov/archive/2009/11/25/9928447.aspx). + * Create a mock of the resource or service that returns a range of errors that the real service may return. Ensure you cover all the types of error that your retry strategy is designed to detect. + * Force transient errors to occur by temporarily disabling or overloading the service if it is a custom service that you created and deployed (you should not, of course, attempt to overload any shared resources or shared services within Azure). + * For HTTP-based APIs, consider using the FiddlerCore library in your automated tests to change the outcome of HTTP requests, either by adding extra roundtrip times or by changing the response (such as the HTTP status code, headers, body, or other factors). This enables deterministic testing of a subset of the failure conditions, whether transient faults or other types of failure. For more information, see [FiddlerCore](http://www.telerik.com/fiddler/fiddlercore). For examples of how to use the library, particularly the **HttpMangler** class, examine the [source code for the Azure Storage SDK](https://github.com/Azure/azure-storage-net/tree/master/Test). + * Perform high load factor and concurrent tests to ensure that the retry mechanism and strategy works correctly under these conditions, and does not have an adverse effect on the operation of the client or cause cross-contamination between requests. +* **Manage retry policy configurations:** + * A *retry policy* is a combination of all of the elements of your retry strategy. It defines the detection mechanism that determines whether a fault is likely to be transient, the type of interval to use (such as regular, exponential back-off, and randomization), the actual interval value(s), and the number of times to retry. + * Retries must be implemented in many places within even the simplest application, and in every layer of more complex applications. Rather than hard-coding the elements of each policy at multiple locations, consider using a central point for storing all the policies. For example, store the values such as the interval and retry count in application configuration files, read them at runtime, and programmatically build the retry policies. This makes it easier to manage the settings, and to modify and fine tune the values in order to respond to changing requirements and scenarios. However, design the system to store the values rather than rereading a configuration file every time, and ensure suitable defaults are used if the values cannot be obtained from configuration. + * In an Azure Cloud Services application, consider storing the values that are used to build the retry policies at runtime in the service configuration file so that they can be changed without needing to restart the application. + * Take advantage of built-in or default retry strategies available in the client APIs you use, but only where they are appropriate for your scenario. These strategies are typically general-purpose. In some scenarios they may be all that is required, but in other scenarios they may not offer the full range of options to suit your specific requirements. You must understand how the settings will affect your application through testing to determine the most appropriate values. +* **Log and track transient and non-transient faults:** + * As part of your retry strategy, include exception handling and other instrumentation that logs when retry attempts are made. While an occasional transient failure and retry are to be expected, and do not indicate a problem, regular and increasing numbers of retries are often an indicator of an issue that may cause a failure, or is currently impacting application performance and availability. + * Log transient faults as Warning entries rather than Error entries so that monitoring systems do not detect them as application errors that may trigger false alerts. + * Consider storing a value in your log entries that indicates if the retries were caused by throttling in the service, or by other types of faults such as connection failures, so that you can differentiate them during analysis of the data. An increase in the number of throttling errors is often an indicator of a design flaw in the application or the need to switch to a premium service that offers dedicated hardware. + * Consider measuring and logging the overall time taken for operations that include a retry mechanism. This is a good indicator of the overall effect of transient faults on user response times, process latency, and the efficiency of the application use cases. Also log the number of retries occurred in order to understand the factors that contributed to the response time. + * Consider implementing a telemetry and monitoring system that can raise alerts when the number and rate of failures, the average number of retries, or the overall times taken for operations to succeed, is increasing. +* **Manage operations that continually fail:** + + * There will be circumstances where the operation continues to fail at every attempt, and it is vital to consider how you will handle this situation: + * Although a retry strategy will define the maximum number of times that an operation should be retried, it does not prevent the application repeating the operation again, with the same number of retries. For example, if an order processing service fails with a fatal error that puts it out of action permanently, the retry strategy may detect a connection timeout and consider it to be a transient fault. The code will retry the operation a specified number of times and then give up. However, when another customer places an order, the operation will be attempted again - even though it is sure to fail every time. + * To prevent continual retries for operations that continually fail, consider implementing the [Circuit Breaker pattern](http://msdn.microsoft.com/library/dn589784.aspx). In this pattern, if the number of failures within a specified time window exceeds the threshold, requests are returned to the caller immediately as errors, without attempting to access the failed resource or service. + * The application can periodically test the service, on an intermittent basis and with very long intervals between requests, to detect when it becomes available. An appropriate interval will depend on the scenario, such as the criticality of the operation and the nature of the service, and might be anything between a few minutes and several hours. At the point where the test succeeds, the application can resume normal operations and pass requests to the newly recovered service. + * In the meantime, it may be possible to fall back to another instance of the service (perhaps in a different datacenter or application), use a similar service that offers compatible (perhaps simpler) functionality, or perform some alternative operations in the hope that the service will become available soon. For example, it may be appropriate to store requests for the service in a queue or data store and replay them later. Otherwise you might be able to redirect the user to an alternative instance of the application, degrade the performance of the application but still offer acceptable functionality, or just return a message to the user indicating that the application is not available at present. +* **Other considerations** + + * When deciding on the values for the number of retries and the retry intervals for a policy, consider if the operation on the service or resource is part of a long-running or multi-step operation. It may be difficult or expensive to compensate all the other operational steps that have already succeeded when one fails. In this case, a very long interval and a large number of retries may be acceptable as long as it does not block other operations by holding or locking scarce resources. + * Consider if retrying the same operation may cause inconsistencies in data. If some parts of a multi-step process are repeated, and the operations are not idempotent, it may result in an inconsistency. For example, an operation that increments a value, if repeated, will produce an invalid result. Repeating an operation that sends a message to a queue may cause an inconsistency in the message consumer if it cannot detect duplicate messages. To prevent this, ensure that you design each step as an idempotent operation. For more information about idempotency, see [Idempotency Patterns](http://blog.jonathanoliver.com/2010/04/idempotency-patterns/). + * Consider the scope of the operations that will be retried. For example, it may be easier to implement retry code at a level that encompasses several operations, and retry them all if one fails. However, doing this may result in idempotency issues or unnecessary rollback operations. + * If you choose a retry scope that encompasses several operations, take into account the total latency of all of them when determining the retry intervals, when monitoring the time taken, and before raising alerts for failures. + * Consider how your retry strategy may affect neighbors and other tenants in a shared application, or when using shared resources and services. Aggressive retry policies can cause an increasing number of transient faults to occur for these other users and for applications that share the resources and services. Likewise, your application may be affected by the retry policies implemented by other users of the resources and services. For mission-critical applications, you may decide to use premium services that are not shared. This provides you with much more control over the load and consequent throttling of these resources and services, which can help to justify the additional cost. + +## More information +* [Azure service-specific retry guidelines](./retry-service-specific.md) +* [The Transient Fault Handling Application Block](http://msdn.microsoft.com/library/hh680934.aspx) +* [Circuit Breaker Pattern](http://msdn.microsoft.com/library/dn589784.aspx) +* [Compensating Transaction Pattern](http://msdn.microsoft.com/library/dn589804.aspx) +* [Idempotency Patterns](http://blog.jonathanoliver.com/2010/04/idempotency-patterns/) + diff --git a/docs/blueprints/_images/blueprints/arch-iaas-hybrid-expressroute-firewalls.png b/docs/blueprints/_images/blueprints/arch-iaas-hybrid-expressroute-firewalls.png new file mode 100644 index 0000000000000000000000000000000000000000..c3e538d0bff0241613c1b277e94e85695d8c6767 GIT binary patch literal 66729 zcmY&=1yEc|(>CtzPVnFm+}+)R6Wrb1Ssap}!3pjVG&n4<5ZqlB65KuLzq#+t{pzne 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