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CDK Monitoring Constructs

NPM version Maven Central PyPI version NuGet version Gitpod Ready-to-Code Mergify

Easy-to-use CDK constructs for monitoring your AWS infrastructure with Amazon CloudWatch.

  • Easily add commonly-used alarms using predefined properties
  • Generate concise CloudWatch dashboards that indicate your alarms
  • Extend the library with your own extensions or custom metrics
  • Consume the library in multiple supported languages

Installation

TypeScript

https://www.npmjs.com/package/cdk-monitoring-constructs

In your package.json:

{
  "dependencies": {
    "cdk-monitoring-constructs": "^5.0.0",

    // peer dependencies of cdk-monitoring-constructs
    "@aws-cdk/aws-apigatewayv2-alpha": "^2.65.0-alpha.0",
    "@aws-cdk/aws-redshift-alpha": "^2.65.0-alpha.0",
    "@aws-cdk/aws-synthetics-alpha": "^2.65.0-alpha.0",
    "aws-cdk-lib": "^2.65.0",
    "constructs": "^10.0.5"

    // ...your other dependencies...
  }
}
Java

See https://mvnrepository.com/artifact/io.github.cdklabs/cdkmonitoringconstructs

Python

See https://pypi.org/project/cdk-monitoring-constructs/

C#

See https://www.nuget.org/packages/Cdklabs.CdkMonitoringConstructs/

Features

You can browse the documentation at https://constructs.dev/packages/cdk-monitoring-constructs/

Item Monitoring Alarms Notes
AWS API Gateway (REST API) (.monitorApiGateway()) TPS, latency, errors Latency, error count/rate, low/high TPS To see metrics, you have to enable Advanced Monitoring
AWS API Gateway V2 (HTTP API) (.monitorApiGatewayV2HttpApi()) TPS, latency, errors Latency, error count/rate, low/high TPS To see route level metrics, you have to enable Advanced Monitoring
AWS AppSync (GraphQL API) (.monitorAppSyncApi()) TPS, latency, errors Latency, error count/rate, low/high TPS
AWS Billing (.monitorBilling()) AWS account cost Total cost (anomaly) Requires enabling the Receive Billing Alerts option in AWS Console / Billing Preferences
AWS Certificate Manager (.monitorCertificate()) Certificate expiration Days until expiration
AWS CloudFront (.monitorCloudFrontDistribution()) TPS, traffic, latency, errors Error rate, low/high TPS
AWS CloudWatch Logs (.monitorLog()) Patterns present in the log group Minimum incoming logs
AWS CloudWatch Synthetics Canary (.monitorSyntheticsCanary()) Latency, error count/rate Error count/rate, latency
AWS CodeBuild (.monitorCodeBuildProject()) Build counts (total, successful, failed), failed rate, duration Failed build count/rate, duration
AWS DocumentDB (.monitorDocumentDbCluster()) CPU, throttling, read/write latency, transactions, cursors CPU
AWS DynamoDB (.monitorDynamoTable()) Read and write capacity provisioned / used Consumed capacity, throttling, latency, errors
AWS DynamoDB Global Secondary Index (.monitorDynamoTableGlobalSecondaryIndex()) Read and write capacity, indexing progress, throttled events
AWS EC2 (.monitorEC2Instances()) CPU, disk operations, network
AWS EC2 Auto Scaling Groups (.monitorAutoScalingGroup()) Group size, instance status
AWS ECS (.monitorFargateService(), .monitorEc2Service(), .monitorSimpleFargateService(), monitorSimpleEc2Service(), .monitorQueueProcessingFargateService(), .monitorQueueProcessingEc2Service()) System resources and task health Unhealthy task count, running tasks count, CPU/memory usage, and bytes processed by load balancer (if any) Use for ecs-patterns load balanced ec2/fargate constructs (NetworkLoadBalancedEc2Service, NetworkLoadBalancedFargateService, ApplicationLoadBalancedEc2Service, ApplicationLoadBalancedFargateService)
AWS ElastiCache (.monitorElastiCacheCluster()) CPU/memory usage, evictions and connections CPU, memory, items count
AWS Glue (.monitorGlueJob()) Traffic, job status, memory/CPU usage Failed/killed task count/rate
AWS Kinesis Data Analytics (.monitorKinesisDataAnalytics) Up/Downtime, CPU/memory usage, KPU usage, checkpoint metrics, and garbage collection metrics Downtime, full restart count
AWS Kinesis Data Stream (.monitorKinesisDataStream()) Put/Get/Incoming Record/s and Throttling Throttling, throughput, iterator max age
AWS Kinesis Firehose (.monitorKinesisFirehose()) Number of records, requests, latency, throttling Throttling
AWS Lambda (.monitorLambdaFunction()) Latency, errors, iterator max age Latency, errors, throttles, iterator max age Optional Lambda Insights metrics (opt-in) support
AWS Load Balancing (.monitorNetworkLoadBalancer(), .monitorFargateApplicationLoadBalancer(), .monitorFargateNetworkLoadBalancer(), .monitorEc2ApplicationLoadBalancer(), .monitorEc2NetworkLoadBalancer()) System resources and task health Unhealthy task count, running tasks count, (for Fargate/Ec2 apps) CPU/memory usage Use for FargateService or Ec2Service backed by a NetworkLoadBalancer or ApplicationLoadBalancer
AWS OpenSearch/Elasticsearch (.monitorOpenSearchCluster(), .monitorElasticsearchCluster()) Indexing and search latency, disk/memory/CPU usage Indexing and search latency, disk/memory/CPU usage, cluster status, KMS keys
AWS RDS (.monitorRdsCluster()) Query duration, connections, latency, disk/CPU usage Connections, disk and CPU usage
AWS Redshift (.monitorRedshiftCluster()) Query duration, connections, latency, disk/CPU usage Query duration, connections, disk and CPU usage
AWS S3 Bucket (.monitorS3Bucket()) Bucket size and number of objects
AWS SecretsManager (.monitorSecretsManager()) Max secret count, min secret sount, secret count change Min/max secret count or change in secret count
AWS SecretsManager Secret (.monitorSecretsManagerSecret()) Days since last rotation Days since last change or rotation
AWS SNS Topic (.monitorSnsTopic()) Message count, size, failed notifications Failed notifications, min/max published messages
AWS SQS Queue (.monitorSqsQueue(), .monitorSqsQueueWithDlq()) Message count, age, size Message count, age, DLQ incoming messages
AWS Step Functions (.monitorStepFunction(), .monitorStepFunctionActivity(), monitorStepFunctionLambdaIntegration(), .monitorStepFunctionServiceIntegration()) Execution count and breakdown per state Duration, failed, failed rate, aborted, throttled, timed out executions
AWS Web Application Firewall (.monitorWebApplicationFirewallAclV2()) Allowed/blocked requests Blocked requests count/rate
Custom metrics (.monitorCustom()) Addition of custom metrics into the dashboard (each group is a widget) Supports anomaly detection

Getting started

Create a facade

Important note: Please, do NOT import anything from the /dist/lib package. This is unsupported and might break any time.

  1. Create an instance of MonitoringFacade, which is the main entrypoint.
  2. Call methods on the facade like .monitorLambdaFunction() and chain them together to define your monitors. You can also use methods to add your own widgets, headers of various sizes, and more.

For examples of monitoring different resources, refer to the unit tests.

export interface MonitoringStackProps extends DeploymentStackProps {
  // ...
}

// This could be in the same stack as your resources, as a nested stack, or a separate stack as you see fit
export class MonitoringStack extends DeploymentStack {
  constructor(parent: App, name: string, props: MonitoringStackProps) {
    super(parent, name, props);

    const monitoring = new MonitoringFacade(this, "Monitoring", {
      // Defaults are provided for these, but they can be customized as desired
      metricFactoryDefaults: { ... },
      alarmFactoryDefaults: { ... },
      dashboardFactory: { ... },
    });

    // Monitor your resources
    monitoring
      .addLargeHeader("Storage")
      .monitorDynamoTable({ /* Monitor a DynamoDB table */ })
      .monitorDynamoTable({ /* and a different table */ })
      .monitorLambdaFunction({ /* and a Lambda function */ })
      .monitorCustom({ /* and some arbitrary metrics in CloudWatch */ })
      // ... etc.
  }
}

Customize actions

Alarms should have an action setup, otherwise they are not very useful. Currently, we support notifying an SNS topic.

const onAlarmTopic = new Topic(this, "AlarmTopic");

const monitoring = new MonitoringFacade(this, "Monitoring", {
  // ...other props
  alarmFactoryDefaults: {
    // ....other props
    action: new SnsAlarmActionStrategy({ onAlarmTopic }),
  },
});

You can override the default topic for any alarm like this:

monitoring
  .monitorSomething(something, {
    addSomeAlarm: {
      Warning: {
        // ...other props
        threshold: 42,
        actionOverride: new SnsAlarmActionStrategy({ onAlarmTopic }),
      }
    }
  });

Custom metrics

For simply adding some custom metrics, you can use .monitorCustom() and specify your own title and metric groups. Each metric group will be rendered as a single graph widget, and all widgets will be placed next to each other. All the widgets will have the same size, which is chosen based on the number of groups to maximize dashboard space usage.

Custom metric monitoring can be created for simple metrics, simple metrics with anomaly detection and search metrics. The first two also support alarming.

Below we are listing a couple of examples. Let us assume that there are three existing metric variables: m1, m2, m3. They can either be created by hand (new Metric({...})) or (preferably) by using metricFactory (that can be obtained from facade). The advantage of using the shared metricFactory is that you do not need to worry about period, etc.

// create metrics manually
const m1 = new Metric(/* ... */);
const metricFactory = monitoringFacade.createMetricFactory();

// create metrics using metric factory
const m1 = metricFactory.createMetric(/* ... */);

Example: metric with anomaly detection

In this case, only one metric is supported. Multiple metrics cannot be rendered with anomaly detection in a single widget due to a CloudWatch limitation.

monitorCustom({
  title: "Metric with anomaly detection",
  metrics: [
    {
      metric: m1,
      anomalyDetectionStandardDeviationToRender: 3
    }
  ]
})

Adding an alarm:

monitorCustom({
  title: "Metric with anomaly detection and alarm",
  metrics: [
    {
      metric: m1,
      alarmFriendlyName: "MetricWithAnomalyDetectionAlarm",
      anomalyDetectionStandardDeviationToRender: 3,
      addAlarmOnAnomaly: {
        Warning: {
          standardDeviationForAlarm: 4,
          alarmWhenAboveTheBand: true,
          alarmWhenBelowTheBand: true
        }
      }
    }
  ]
})

Example: search metrics

monitorCustom({
  title: "Metric search",
  metrics: [
    {
      searchQuery: "My.Prefix.",
      dimensionsMap: {
        FirstDimension: "FirstDimensionValue",
        // Allow any value for the given dimension (pardon the weird typing to satisfy DimensionsMap)
        SecondDimension: undefined as unknown as string
      }
      statistic: MetricStatistic.SUM,
    }
  ]
})

Search metrics do not support setting an alarm, which is a CloudWatch limitation.

Route53 Health Checks

Route53 has strict requirements as to which alarms are allowed to be referenced in Health Checks. You adjust the metric for an alarm sot hat it can be used in a Route53 Health Checks as follows:

monitoring
  .monitorSomething(something, {
    addSomeAlarm: {
      Warning: {
        // ...other props
        metricAdjuster: Route53HealthCheckMetricAdjuster.INSTANCE,
      }
    }
  });

This will ensure the alarm can be used on a Route53 Health Check or otherwise throw an Error indicating why the alarm can't be used. In order to easily find your Route53 Health Check alarms later on, you can apply a custom tag to them as follows:

import { CfnHealthCheck } from "aws-cdk-lib/aws-route53";

monitoring
  .monitorSomething(something, {
    addSomeAlarm: {
      Warning: {
        // ...other props
        customTags: ["route53-health-check"],
        metricAdjuster: Route53HealthCheckMetricAdjuster.INSTANCE,
      }
    }
  });

const alarms = monitoring.createdAlarmsWithTag("route53-health-check");

const healthChecks = alarms.map(({ alarm }) => {
  const id = getHealthCheckConstructId(alarm);

  return new CfnHealthCheck(scope, id, {
    healthCheckConfig: {
      // ...other props
      type: "CLOUDWATCH_METRIC",
      alarmIdentifier: {
        name: alarm.alarmName,
        region: alarm.stack.region,
      },
    },
  });
});

Custom monitoring segments

If you want even more flexibility, you can create your own segment.

This is a general procedure on how to do it:

  1. Extend the Monitoring class
  2. Override the widgets() method (and/or similar ones)
  3. Leverage the metric factory and alarm factory provided by the base class (you can create additional factories, if you will)
  4. Add all alarms to .addAlarm() so they are visible to the user and being placed on the alarm summary dashboard

Both of these monitoring base classes are dashboard segments, so you can add them to your monitoring by calling .addSegment() on the MonitoringFacade.

Custom dashboards

If you want even more flexibility, you can take complete control over dashboard generation by leveraging dynamic dashboarding features. This allows you to create an arbitrary number of dashboards while configuring each of them separately. You can do this in three simple steps:

  1. Create a dynamic dashboard factory
  2. Create IDynamicDashboardSegment implementations
  3. Add Dynamic Segments to your MonitoringFacade

Create a dynamic dashboard factory

The below code sample will generate two dashboards with the following names:

  • ExampleDashboards-HostedService
  • ExampleDashboards-Infrastructure
// create the dynamic dashboard factory.
const factory = new DynamicDashboardFactory(stack, "DynamicDashboards", {
  dashboardNamePrefix: "ExampleDashboards",
  dashboardConfigs: [
    // 'name' is the minimum required configuration
    { name: "HostedService" },
    // below is an example of additional dashboard-specific config options
    {
      name: "Infrastructure",
      range: Duration.hours(3),
      periodOverride: PeriodOverride.AUTO,
      renderingPreference: DashboardRenderingPreference.BITMAP_ONLY
    },
  ],
});

Create IDynamicDashboardSegment implementations

For each construct you want monitored, you will need to create an implementation of an IDynamicDashboardSegment. The following is a basic reference implementation as an example:

export enum DashboardTypes {
  HostedService = "HostedService",
  Infrastructure = "Infrastructure",
}

class ExampleSegment implements IDynamicDashboardSegment {
  widgetsForDashboard(name: string): IWidget[] {
    // this logic is what's responsible for allowing your dynamic segment to return
    // different widgets for different dashboards
    switch (name) {
      case DashboardTypes.HostedService:
        return [new TextWidget({ markdown: "This shows metrics for your service hosted on AWS Infrastructure" })];
      case DashboardTypes.Infrastructure:
        return [new TextWidget({ markdown: "This shows metrics for the AWS Infrastructure supporting your hosted service" })];
      default:
        throw new Error("Unexpected dashboard name!");
    }
  }
}

Add Dynamic Segments to MonitoringFacade

When you have instances of an IDynamicDashboardSegment to use, they can be added to your dashboard like this:

monitoring.addDynamicSegment(new ExampleSegment());

Now, this widget will be added to both dashboards and will show different content depending on the dashboard. Using the above example code, two dashboards will be generated with the following content:

  • Dashboard Name: "ExampleDashboards-HostedService"
    • Content: "This shows metrics for your service hosted on AWS Infrastructure"
  • Dashboard Name: "ExampleDashboards-Infrastructure"
    • Content: "This shows metrics for the AWS Infrastructure supporting your hosted service"

Monitoring scopes

You can monitor complete CDK construct scopes using an aspect. It will automatically discover all monitorable resources within the scope recursively and add them to your dashboard.

monitoring.monitorScope(stack, {
  // With optional configuration
  lambda: {
    props: {
      addLatencyP50Alarm: {
        Critical: { maxLatency: Duration.seconds(10) },
      },
    },
  },

  // Some resources that aren't dependent on nodes (e.g. general metrics across instances/account) may be included
  // by default, which can be explicitly disabled.
  billing: { enabled: false },
  ec2: { enabled: false },
  elasticCache: { enabled: false },
});

Contributing/Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.