aws-cdk.aws-msk-alpha 2.165.0a0

The CDK Construct Library for AWS::MSK

Amazon Managed Streaming for Apache Kafka Construct Library

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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 This means that while you may use them, you may need to update your source code when upgrading to a newer version of this package.

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Amazon MSK is a fully managed service that makes it easy for you to build and run applications that use Apache Kafka to process streaming data.

The following example creates an MSK Cluster.

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "Cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc
)

Allowing Connections

To control who can access the Cluster, use the .connections attribute. For a list of ports used by MSK, refer to the MSK documentation.

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "Cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc
)

cluster.connections.allow_from(
    ec2.Peer.ipv4("1.2.3.4/8"),
    ec2.Port.tcp(2181))
cluster.connections.allow_from(
    ec2.Peer.ipv4("1.2.3.4/8"),
    ec2.Port.tcp(9094))

Cluster Endpoints

You can use the following attributes to get a list of the Kafka broker or ZooKeeper node endpoints

# cluster: msk.Cluster

CfnOutput(self, "BootstrapBrokers", value=cluster.bootstrap_brokers)
CfnOutput(self, "BootstrapBrokersTls", value=cluster.bootstrap_brokers_tls)
CfnOutput(self, "BootstrapBrokersSaslScram", value=cluster.bootstrap_brokers_sasl_scram)
CfnOutput(self, "BootstrapBrokerStringSaslIam", value=cluster.bootstrap_brokers_sasl_iam)
CfnOutput(self, "ZookeeperConnection", value=cluster.zookeeper_connection_string)
CfnOutput(self, "ZookeeperConnectionTls", value=cluster.zookeeper_connection_string_tls)

Importing an existing Cluster

To import an existing MSK cluster into your CDK app use the .fromClusterArn() method.

cluster = msk.Cluster.from_cluster_arn(self, "Cluster", "arn:aws:kafka:us-west-2:1234567890:cluster/a-cluster/11111111-1111-1111-1111-111111111111-1")

Client Authentication

MSK supports the following authentication mechanisms.

TLS

To enable client authentication with TLS set the certificateAuthorityArns property to reference your ACM Private CA. More info on Private CAs.

import aws_cdk.aws_acmpca as acmpca

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "Cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc,
    encryption_in_transit=msk.EncryptionInTransitConfig(
        client_broker=msk.ClientBrokerEncryption.TLS
    ),
    client_authentication=msk.ClientAuthentication.tls(
        certificate_authorities=[
            acmpca.CertificateAuthority.from_certificate_authority_arn(self, "CertificateAuthority", "arn:aws:acm-pca:us-west-2:1234567890:certificate-authority/11111111-1111-1111-1111-111111111111")
        ]
    )
)

SASL/SCRAM

Enable client authentication with SASL/SCRAM:

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc,
    encryption_in_transit=msk.EncryptionInTransitConfig(
        client_broker=msk.ClientBrokerEncryption.TLS
    ),
    client_authentication=msk.ClientAuthentication.sasl(
        scram=True
    )
)

SASL/IAM

Enable client authentication with IAM:

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc,
    encryption_in_transit=msk.EncryptionInTransitConfig(
        client_broker=msk.ClientBrokerEncryption.TLS
    ),
    client_authentication=msk.ClientAuthentication.sasl(
        iam=True
    )
)

SASL/IAM + TLS

Enable client authentication with IAM as well as enable client authentication with TLS by setting the certificateAuthorityArns property to reference your ACM Private CA. More info on Private CAs.

import aws_cdk.aws_acmpca as acmpca

# vpc: ec2.Vpc

cluster = msk.Cluster(self, "Cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc,
    encryption_in_transit=msk.EncryptionInTransitConfig(
        client_broker=msk.ClientBrokerEncryption.TLS
    ),
    client_authentication=msk.ClientAuthentication.sasl_tls(
        iam=True,
        certificate_authorities=[
            acmpca.CertificateAuthority.from_certificate_authority_arn(self, "CertificateAuthority", "arn:aws:acm-pca:us-west-2:1234567890:certificate-authority/11111111-1111-1111-1111-111111111111")
        ]
    )
)

Logging

You can deliver Apache Kafka broker logs to one or more of the following destination types: Amazon CloudWatch Logs, Amazon S3, Amazon Kinesis Data Firehose.

To configure logs to be sent to an S3 bucket, provide a bucket in the logging config.

# vpc: ec2.Vpc
# bucket: s3.IBucket

cluster = msk.Cluster(self, "cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V2_8_1,
    vpc=vpc,
    logging=msk.BrokerLogging(
        s3=msk.S3LoggingConfiguration(
            bucket=bucket
        )
    )
)

When the S3 destination is configured, AWS will automatically create an S3 bucket policy that allows the service to write logs to the bucket. This makes it impossible to later update that bucket policy. To have CDK create the bucket policy so that future updates can be made, the @aws-cdk/aws-s3:createDefaultLoggingPolicy feature flag can be used. This can be set in the cdk.json file.

{
  "context": {
    "@aws-cdk/aws-s3:createDefaultLoggingPolicy": true
  }
}

Storage Mode

You can configure an MSK cluster storage mode using the storageMode property.

Tiered storage is a low-cost storage tier for Amazon MSK that scales to virtually unlimited storage, making it cost-effective to build streaming data applications.

Visit Tiered storage to see the list of compatible Kafka versions and for more details.

# vpc: ec2.Vpc
# bucket: s3.IBucket


cluster = msk.Cluster(self, "cluster",
    cluster_name="myCluster",
    kafka_version=msk.KafkaVersion.V3_6_0,
    vpc=vpc,
    storage_mode=msk.StorageMode.TIERED
)