aws-cdk.aws-lambda-event-sources 1.98.0

Event sources for AWS Lambda

AWS Lambda Event Sources

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An event source mapping is an AWS Lambda resource that reads from an event source and invokes a Lambda function. You can use event source mappings to process items from a stream or queue in services that don't invoke Lambda functions directly. Lambda provides event source mappings for the following services. Read more about lambda event sources here.

This module includes classes that allow using various AWS services as event sources for AWS Lambda via the high-level lambda.addEventSource(source) API.

NOTE: In most cases, it is also possible to use the resource APIs to invoke an AWS Lambda function. This library provides a uniform API for all Lambda event sources regardless of the underlying mechanism they use.

The following code sets up a lambda function with an SQS queue event source -

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
fn = lambda_.Function(self, "MyFunction")

queue = sqs.Queue(self, "MyQueue")
event_source = fn.add_event_source(SqsEventSource(queue))

event_source_id = event_source.event_source_id

The eventSourceId property contains the event source id. This will be a token that will resolve to the final value at the time of deployment.

SQS

Amazon Simple Queue Service (Amazon SQS) allows you to build asynchronous workflows. For more information about Amazon SQS, see Amazon Simple Queue Service. You can configure AWS Lambda to poll for these messages as they arrive and then pass the event to a Lambda function invocation. To view a sample event, see Amazon SQS Event.

To set up Amazon Simple Queue Service as an event source for AWS Lambda, you first create or update an Amazon SQS queue and select custom values for the queue parameters. The following parameters will impact Amazon SQS's polling behavior:

• visibilityTimeout: May impact the period between retries.
• receiveMessageWaitTime: Will determine long poll duration. The default value is 20 seconds.
• batchSize: Determines how many records are buffered before invoking your lambda function.
• maxBatchingWindow: The maximum amount of time to gather records before invoking the lambda. This increases the likelihood of a full batch at the cost of delayed processing.
• enabled: If the SQS event source mapping should be enabled. The default is true.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_sqs as sqs
from aws_cdk.aws_lambda_event_sources import SqsEventSource
from aws_cdk.core import Duration


queue = sqs.Queue(self, "MyQueue",
    visibility_timeout=Duration.seconds(30), # default,
    receive_message_wait_time=Duration.seconds(20)
)

lambda_.add_event_source(SqsEventSource(queue,
    batch_size=10, # default
    max_batching_window=Duration.minutes(5)
))

S3

You can write Lambda functions to process S3 bucket events, such as the object-created or object-deleted events. For example, when a user uploads a photo to a bucket, you might want Amazon S3 to invoke your Lambda function so that it reads the image and creates a thumbnail for the photo.

You can use the bucket notification configuration feature in Amazon S3 to configure the event source mapping, identifying the bucket events that you want Amazon S3 to publish and which Lambda function to invoke.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_s3 as s3
from aws_cdk.aws_lambda_event_sources import S3EventSource


bucket = s3.Bucket(...)

lambda_.add_event_source(S3EventSource(bucket,
    events=[s3.EventType.OBJECT_CREATED, s3.EventType.OBJECT_REMOVED],
    filters=[NotificationKeyFilter(prefix="subdir/")]
))

SNS

You can write Lambda functions to process Amazon Simple Notification Service notifications. When a message is published to an Amazon SNS topic, the service can invoke your Lambda function by passing the message payload as a parameter. Your Lambda function code can then process the event, for example publish the message to other Amazon SNS topics, or send the message to other AWS services.

This also enables you to trigger a Lambda function in response to Amazon CloudWatch alarms and other AWS services that use Amazon SNS.

For an example event, see Appendix: Message and JSON Formats and Amazon SNS Sample Event. For an example use case, see Using AWS Lambda with Amazon SNS from Different Accounts.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_sns as sns
from aws_cdk.aws_lambda_event_sources import SnsEventSource


topic = sns.Topic(...)
dead_letter_queue = sqs.Queue(self, "deadLetterQueue")

lambda_.add_event_source(SnsEventSource(topic,
    filter_policy={...},
    dead_letter_queue=dead_letter_queue
))

When a user calls the SNS Publish API on a topic that your Lambda function is subscribed to, Amazon SNS will call Lambda to invoke your function asynchronously. Lambda will then return a delivery status. If there was an error calling Lambda, Amazon SNS will retry invoking the Lambda function up to three times. After three tries, if Amazon SNS still could not successfully invoke the Lambda function, then Amazon SNS will send a delivery status failure message to CloudWatch.

DynamoDB Streams

You can write Lambda functions to process change events from a DynamoDB Table. An event is emitted to a DynamoDB stream (if configured) whenever a write (Put, Delete, Update) operation is performed against the table. See Using AWS Lambda with Amazon DynamoDB for more information about configuring Lambda function event sources with DynamoDB.

To process events with a Lambda function, first create or update a DynamoDB table and enable a stream specification. Then, create a DynamoEventSource and add it to your Lambda function. The following parameters will impact Amazon DynamoDB's polling behavior:

• batchSize: Determines how many records are buffered before invoking your lambda function - could impact your function's memory usage (if too high) and ability to keep up with incoming data velocity (if too low).
• bisectBatchOnError: If a batch encounters an error, this will cause the batch to be split in two and have each new smaller batch retried, allowing the records in error to be isolated.
• maxBatchingWindow: The maximum amount of time to gather records before invoking the lambda. This increases the likelihood of a full batch at the cost of delayed processing.
• maxRecordAge: The maximum age of a record that will be sent to the function for processing. Records that exceed the max age will be treated as failures.
• onFailure: In the event a record fails after all retries or if the record age has exceeded the configured value, the record will be sent to SQS queue or SNS topic that is specified here
• parallelizationFactor: The number of batches to concurrently process on each shard.
• retryAttempts: The maximum number of times a record should be retried in the event of failure.
• startingPosition: Will determine where to being consumption, either at the most recent ('LATEST') record or the oldest record ('TRIM_HORIZON'). 'TRIM_HORIZON' will ensure you process all available data, while 'LATEST' will ignore all records that arrived prior to attaching the event source.
• tumblingWindow: The duration in seconds of a processing window when using streams.
• enabled: If the DynamoDB Streams event source mapping should be enabled. The default is true.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_dynamodb as dynamodb
import aws_cdk.aws_lambda as lambda_
import aws_cdk.aws_sqs as sqs
from aws_cdk.aws_lambda_event_sources import DynamoEventSource, SqsDlq


table = dynamodb.Table(...,
    partition_key=, ...,
    stream=dynamodb.StreamViewType.NEW_IMAGE
)

dead_letter_queue = sqs.Queue(self, "deadLetterQueue")def (self):
    passlambda_.Function(...)
functionadd_event_source(DynamoEventSource(table,
    starting_position=lambda_.StartingPosition.TRIM_HORIZON,
    batch_size=5,
    bisect_batch_on_error=True,
    on_failure=SqsDlq(dead_letter_queue),
    retry_attempts=10
))

Kinesis

You can write Lambda functions to process streaming data in Amazon Kinesis Streams. For more information about Amazon Kinesis, see Amazon Kinesis Service. To learn more about configuring Lambda function event sources with kinesis and view a sample event, see Amazon Kinesis Event.

To set up Amazon Kinesis as an event source for AWS Lambda, you first create or update an Amazon Kinesis stream and select custom values for the event source parameters. The following parameters will impact Amazon Kinesis's polling behavior:

• batchSize: Determines how many records are buffered before invoking your lambda function - could impact your function's memory usage (if too high) and ability to keep up with incoming data velocity (if too low).
• bisectBatchOnError: If a batch encounters an error, this will cause the batch to be split in two and have each new smaller batch retried, allowing the records in error to be isolated.
• maxBatchingWindow: The maximum amount of time to gather records before invoking the lambda. This increases the likelihood of a full batch at the cost of possibly delaying processing.
• maxRecordAge: The maximum age of a record that will be sent to the function for processing. Records that exceed the max age will be treated as failures.
• onFailure: In the event a record fails and consumes all retries, the record will be sent to SQS queue or SNS topic that is specified here
• parallelizationFactor: The number of batches to concurrently process on each shard.
• retryAttempts: The maximum number of times a record should be retried in the event of failure.
• startingPosition: Will determine where to being consumption, either at the most recent ('LATEST') record or the oldest record ('TRIM_HORIZON'). 'TRIM_HORIZON' will ensure you process all available data, while 'LATEST' will ignore all records that arrived prior to attaching the event source.
• tumblingWindow: The duration in seconds of a processing window when using streams.
• enabled: If the DynamoDB Streams event source mapping should be enabled. The default is true.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
import aws_cdk.aws_kinesis as kinesis
from aws_cdk.aws_lambda_event_sources import KinesisEventSource


stream = kinesis.Stream(self, "MyStream")

my_function.add_event_source(KinesisEventSource(stream,
    batch_size=100, # default
    starting_position=lambda_.StartingPosition.TRIM_HORIZON
))

Kafka

You can write Lambda functions to process data either from Amazon MSK or a self managed Kafka cluster.

The following code sets up Amazon MSK as an event source for a lambda function. Credentials will need to be configured to access the MSK cluster, as described in Username/Password authentication.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
import aws_cdk.aws_lambda as msk
from aws_cdk.aws_secretmanager import Secret
from aws_cdk.aws_lambda_event_sources import ManagedKafkaEventSource


# Your MSK cluster
cluster = msk.Cluster.from_cluster_arn(self, "Cluster", "arn:aws:kafka:us-east-1:0123456789019:cluster/SalesCluster/abcd1234-abcd-cafe-abab-9876543210ab-4")

# The Kafka topic you want to subscribe to
topic = "some-cool-topic"

# The secret that allows access to your MSK cluster
# You still have to make sure that it is associated with your cluster as described in the documentation
secret = Secret(self, "Secret", secret_name="AmazonMSK_KafkaSecret")

my_function.add_event_source(ManagedKafkaEventSource(
    cluster=cluster,
    topic=topic,
    secret=secret,
    batch_size=100, # default
    starting_position=lambda_.StartingPosition.TRIM_HORIZON
))

The following code sets up a self managed Kafka cluster as an event source. Username and password based authentication will need to be set up as described in Managing access and permissions.

# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
from aws_cdk.aws_secretmanager import Secret
from aws_cdk.aws_lambda_event_sources import SelfManagedKafkaEventSource


# The list of Kafka brokers
bootstrap_servers = ["kafka-broker:9092"]

# The Kafka topic you want to subscribe to
topic = "some-cool-topic"

# The secret that allows access to your self hosted Kafka cluster
secret = Secret(self, "Secret", ...)

my_function.add_event_source(SelfManagedKafkaEventSource(
    bootstrap_servers=bootstrap_servers,
    topic=topic,
    secret=secret,
    batch_size=100, # default
    starting_position=lambda_.StartingPosition.TRIM_HORIZON
))

If your self managed Kafka cluster is only reachable via VPC also configure vpc vpcSubnets and securityGroup.

Roadmap

Eventually, this module will support all the event sources described under Supported Event Sources in the AWS Lambda Developer Guide.