| Package | Hyperscale |
|---|---|
| Version | 0.1.0 |
| Web | https://hyperscale.dev |
| Download | https://pypi.org/project/hyperscale/ |
| Source | https://github.com/hyper-light/hyperscale |
| Keywords | performance, testing, async, distributed, graph, DAG, workflow |
Hyperscale is a Python performance and scalable unit/integration testing framework that makes creating and running complex test workflows easy.
These workflows are written as directed acrylic graphs in Python, where each graph is specified as a collection of Python classes referred to as stages. Each Stage may then specify async Python methods which are then wrapped in Python decorators (referred to as hooks), which that Stage will then execute. The hook wrapping a method tells Hyperscale both what the action does and when to execute it. In combination, stages and hooks allow you to craft test workflows that can mimic real-world user behavior, optimize framework performance, or interact with a variety of Hyperscale's powerful integrations.
Understanding how your application performs under load can provide valuable insights - allowing you to spot issues with latency, memory usage, and stability. However, performance test tools providing these insights are often difficult to use and lack the ability to simulate complex user interaction at scale. Hyperscale was built to solve these problems by allowing developers and test engineers to author performance tests as sophisticated and scalable workflows. Hyperscale adheres to the following tenants:
Regardless of whether running on your personal laptop or distributed across a cluster, Hyperscale is fast, capable of generating millions of requests or interactions per minute and without consuming excessive memory. Hyperscale pushes the limits of Python to achieve this, embracing the latest in Python async and multiprocessing language features to achieve optimal execution performance.
Authoring, managing, and running test workflows is easy. Hyperscale includes integrations with Git to facilitate easy management of collections of graphs via Projects, the ability to generate flexible starter test templates, and an API that is both fast and intuitive to understand. Distributed use almost exactly mirrors local operation, reducing the learning curve for more complex deployments.
Hyperscale ships with support for HTTP, HTTP2, Websockets, and UDP out of the box. GraphQL, GRPC, and Playwright are available simply by installing the (optional) dependency packages. Hyperscale offers JSON and CSV results output by default, with 28 additional results reporting options readily available by likewise installing the required dependencies.
Likewise, Hyperscale offers a comprehensive plugin system. You can easily write a plugin to test your Postgresql database or integrate a third party service, with CLI-generated templates to guide you and full type hints support throughout. Unlike other frameworks, no additional compilation or build steps are required - just write your plugin, import it, and include it in the appropriate Stage in your test graph.
Hyperscale has been tested on Python versions 3.8.6+, though we recommend using Python 3.10+. You should likewise have the latest LTS version of OpenSSL, build-essential, and other common Unix dependencies installed (if running on a Unix-based OS).
Warning: Hyperscale has currently only been tested on the latest LTS versions of Ubuntu and Debian. Other official OS support is coming Mar. 2023.
To install Hyperscale run:
pip install hyperscale
Verify the installation was was successful by running the command:
hyperscale --help
which should output
Get started by running Hyperscale's:
hyperscale graph create <path/to/graph_name.py>
command in an empty directory to generate a basic test from a template. For example, run:
hyperscale graph create example.py
which will output the following:
and generate the the test below in the specified example.py file:
from hyperscale import (
Setup,
Execute,
action,
Analyze,
JSONConfig,
Submit,
depends,
)
class SetupStage(Setup):
batch_size=1000
total_time='1m'
@depends(SetupStage)
class ExecuteHTTPStage(Execute):
@action()
async def http_get(self):
return await self.client.http.get('https://<url_here>')
@depends(ExecuteHTTPStage)
class AnalyzeStage(Analyze):
pass
@depends(AnalyzeStage)
class SubmitJSONResultsStage(Submit):
config=JSONConfig(
events_filepath='./events.json',
metrics_filepath='./metrics.json'
)We'll explain this graph below, but for now - replace the string 'https://<url_here>' with 'https://httpbin.org/get'.
Before running our test, if on a Unix system, we may need to set the maximum number of open files above its current limit. This can be done by running:
ulimit -n 256000
note that you can provide any number here, as long as it is greater than the batch_size specified in the SetupStage Stage. With that, we're ready run our first test by executing:
hyperscale graph run example.py
Hyperscale will load the test graph file, parse/validate/setup the stages specified, then begin executing your test:
The test will take a minute or two to run, but once complete you should see:
You have officially created and run your first test graph!
Local development requires at-minimum Python 3.8.6, though 3.10.0+ is recommended. To setup your environment run:
python3 -m venv ~/.hyperscale && \
source ~/.hyperscale/bin/activate && \
git clone https://github.com/hyper-light/hyperscale.git && \
cd hyperscale && \
pip install --no-cache -r requirements.in && \
python setup.py develop
To develop or work with any of the additional provided engines, references the dependency tables below.
Much of Hyperscale's extensibility comes in the form of both extensive integrations/options and plugin capabilities for four main framework features:
Engines are the underlying protocol or library integrations required for Hyperscale to performance test your application (for example HTTP, UDP, Playwright). Hyperscale currently supports the following Engines, with additional install requirements shown if necessary:
| Engine | Additional Install Option | Dependencies |
|---|---|---|
| HTTP | N/A | N/A |
| HTTP2 | N/A | N/A |
| HTTP3 (unstable) | pip install hyperscale[http3] | aioquic |
| UDP | N/A | N/A |
| Websocket | N/A | N/A |
| GRPC | pip install hyperscale[grpc] | protobuf |
| GraphQL | pip install hyperscale[graphql] | graphql-core |
| GraphQL-HTTP2 | pip install hyperscale[graphql] | graphql-core |
| Playwright | pip install hyperscale[playwright] && playwright install | playwright |
Personas are responsible for scheduling when @action() or @task() hooks execute over the specified Execute stage's test duration. No additional install dependencies are required for Personas, and the following personas are currently supported out-of-box:
| Persona | Setup Config Name | Description |
|---|---|---|
| Approximate Distribution (unstable) | approximate-distribution | Hyperscale automatically adjusts the batch size after each batch spawns according to the concurrency at the current distribution step. This Persona is only available to and is selected by default if a Variant of an Experiment is assigned a distribution. |
| Batched | batched | Executes each action or task hook in batches of the specified size, with an optional wait between each batch spawning |
| Constant Arrival Rate | constant-arrival | Hyperscale automatically adjusts the batch size after each batch spawns based upon the number of hooks that have completed, attempting to achieve batch_size completions per batch |
| Constant Spawn Rate | constant-spawn | Like Batched, but cycles through actions before waiting batch_interval time. |
| Default | N/A | Cycles through all action/task hooks in the Execute stage, resulting in a (mostly) even distribution of execution |
| No-Wait | no-wait | Cycles through all action/task hooks in the Execute stage with no memory usage or other waits. WARNING: This persona may cause OOM. |
| Ramped | ramped | Starts at a batch size of batch_gradient * batch_size. Batch size increases by the gradient each batch with an optional wait between each batch spawning |
| Ramped Interval | ramped-interval | Executes batch_size hooks before waiting batch_gradient * batch_interval time. Interval increases by the gradient each batch |
| Sorted | sorted | Executes each action/task hook in batches of the specified size and in the order provided to each hook's (optional) order parameter |
| Weighted | weighted | Executes action/task hooks in batches of the specified size, with each batch being generated from a sampled distribution based upon that action's weight |
Algorithms are used by Hyperscale Optimize stages to calculate maximal test config options like batch_size, batch_gradient, and/or batch_interval. All out-of-box supported algorithms use scikit-learn and include:
| Algorithm | Setup Config Name | Description |
|---|---|---|
| SHG | shg | Uses scikit-learn's Simple Global Homology (SHGO) global optimization algorithm |
| Dual Annealing | dual-annealing | Uses scikit-learn's Dual Annealing global optimization algorithm |
| Differential Evolution | diff-evolution | Uses scikit-learn's Differential Evolution global optimization algorithm |
| Point Optimizer (unstable) | point-optimizer | Uses a custom least-squares algorithm. Can only be used by assigning a distribution to a Variant stage for an Experiment. |
Reporters are the integrations Hyperscale uses for submitting aggregated and unaggregated results (for example, to a MySQL database via the MySQL reporter). Hyperscale currently supports the following Reporters, with additional install requirements shown if necessary:
| Engine | Additional Install Option | Dependencies |
|---|---|---|
| AWS Lambda | pip install hyperscale[aws] | boto3 |
| AWS Timestream | pip install hyperscale[aws] | boto3 |
| Big Query | pip install hyperscale[google] | google-cloud-bigquery |
| Big Table | pip install hyperscale[google] | google-cloud-bigtable |
| Cassandra | pip install hyperscale[cassandra] | cassandra-driver |
| Cloudwatch | pip install hyperscale[aws] | boto3 |
| CosmosDB | pip install hyperscale[azure] | azure-cosmos |
| CSV | N/A | N/A |
| Datadog | pip install hyperscale[datadog] | datadog |
| DogStatsD | pip install hyperscale[statsd] | aio_statsd |
| Google Cloud Storage | pip install hyperscale[google] | google-cloud-storage |
| Graphite | pip install hyperscale[statsd] | aio_statsd |
| Honeycomb | pip install hyperscale[honeycomb] | libhoney |
| InfluxDB | pip install hyperscale[influxdb] | influxdb_client |
| JSON | N/A | N/A |
| Kafka | pip install hyperscale[kafka] | aiokafka |
| MongoDB | pip install hyperscale[mongodb] | motor |
| MySQL | pip install hyperscale[sql] | aiomysql, sqlalchemy |
| NetData | pip install hyperscale[statsd] | aio_statsd |
| New Relic | pip install hyperscale[newrelic] | newrelic |
| Postgresql | pip install hyperscale[sql] | aiopg, psycopg2-binary, sqlalchemy |
| Prometheus | pip install hyperscale[prometheus] | prometheus-client, prometheus-client-api |
| Redis | pip install hyperscale[redis] | redis, aioredis |
| S3 | pip install hyperscale[aws] | boto3 |
| Snowflake | pip install hyperscale[snowflake] | snowflake-connector-python, sqlalchemy |
| SQLite3 | pip install hyperscale[sql] | sqlalchemy |
| StatsD | pip install hyperscale[statsd] | aio_statsd |
| Telegraf | pip install hyperscale[statsd] | aio_statsd |
| TelegrafStatsD | pip install hyperscale[statsd] | aio_statsd |
| TimescaleDB | pip install hyperscale[sql] | aiopg, psycopg2-binary, sqlalchemy |
| XML | pip install hyperscale[xml] | dicttoxml |
Hyperscale's official and full documentation is currently being written and will be linked here soon!
This software is licensed under the MIT License. See the LICENSE file in the top distribution directory for the full license text.
Hyperscale will be open to general contributions starting Fall, 2023 (once the distributed rewrite and general testing is complete). Until then, feel free to use Hyperscale on your local machine and report any bugs or issues you find!
Hyperscale has adopted and follows the Contributor Covenant code of conduct. If you observe behavior that violates those rules please report to:
| Name | ||
|---|---|---|
| Sean Corbett | [email protected] | @sc_codeum |