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A CDK Construct Library for Kinesis Analytics Flink applications

Project description

Kinesis Analytics Flink


cdk-constructs: Experimental

The APIs of higher level constructs in this module are experimental and under active development. They are subject to non-backward compatible changes or removal in any future version. These are not subject to the Semantic Versioning model and breaking changes will be announced in the release notes. This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

This package provides constructs for creating Kinesis Analytics Flink applications. To learn more about using using managed Flink applications, see the AWS developer guide.

Creating Flink Applications

To create a new Flink application, use the Application construct:

import path as path
import aws_cdk.core as core
import aws_cdk.aws_kinesisanalytics_flink as flink
import aws_cdk.aws_cloudwatch as cloudwatch

app = core.App()
stack = core.Stack(app, "FlinkAppTest")

flink_app = flink.Application(stack, "App",
    code=flink.ApplicationCode.from_asset(path.join(__dirname, "code-asset")),

cloudwatch.Alarm(stack, "Alarm",


The code property can use fromAsset as shown above to reference a local jar file in s3 or fromBucket to reference a file in s3.

import path as path
import aws_cdk.aws_s3_assets as assets
import aws_cdk.core as core
import aws_cdk.aws_kinesisanalytics_flink as flink

app = core.App()
stack = core.Stack(app, "FlinkAppCodeFromBucketTest")

asset = assets.Asset(stack, "CodeAsset",
    path=path.join(__dirname, "code-asset")
bucket = asset.bucket
file_key = asset.s3_object_key

flink.Application(stack, "App",
    code=flink.ApplicationCode.from_bucket(bucket, file_key),


The propertyGroups property provides a way of passing arbitrary runtime properties to your Flink application. You can use the aws-kinesisanalytics-runtime library to retrieve these properties.

# bucket: s3.Bucket

flink_app = flink.Application(self, "Application",
            "input_stream_name": "my-input-kinesis-stream",
            "output_stream_name": "my-output-kinesis-stream"
    # ...
    code=flink.ApplicationCode.from_bucket(bucket, "my-app.jar")

Flink applications also have specific configuration for passing parameters when the Flink job starts. These include parameters for checkpointing, snapshotting, monitoring, and parallelism.

# bucket: s3.Bucket

flink_app = flink.Application(self, "Application",
    code=flink.ApplicationCode.from_bucket(bucket, "my-app.jar"),
    checkpointing_enabled=True,  # default is true
    checkpoint_interval=Duration.seconds(30),  # default is 1 minute
    min_pause_between_checkpoints=Duration.seconds(10),  # default is 5 seconds
    log_level=flink.LogLevel.ERROR,  # default is INFO
    metrics_level=flink.MetricsLevel.PARALLELISM,  # default is APPLICATION
    auto_scaling_enabled=False,  # default is true
    parallelism=32,  # default is 1
    parallelism_per_kpu=2,  # default is 1
    snapshots_enabled=False,  # default is true
    log_group=logs.LogGroup(self, "LogGroup")

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