aws-cdk.aws-kinesisanalytics-flink 1.204.0

A CDK Construct Library for Kinesis Analytics Flink applications

Kinesis Analytics Flink

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AWS CDK v1 has reached End-of-Support on 2023-06-01. This package is no longer being updated, and users should migrate to AWS CDK v2.

For more information on how to migrate, see the Migrating to AWS CDK v2 guide.

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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")),
    runtime=flink.Runtime.FLINK_1_11
)

cloudwatch.Alarm(stack, "Alarm",
    metric=flink_app.metric_full_restarts(),
    evaluation_periods=1,
    threshold=3
)

app.synth()

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),
    runtime=flink.Runtime.FLINK_1_11
)

app.synth()

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",
    property_groups=flink.PropertyGroups(
        FlinkApplicationProperties={
            "input_stream_name": "my-input-kinesis-stream",
            "output_stream_name": "my-output-kinesis-stream"
        }
    ),
    # ...
    runtime=flink.Runtime.FLINK_1_13,
    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"),
    runtime=flink.Runtime.FLINK_1_13,
    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")
)