DynamoDB Streams and Amazon Lambda triggers - Amazon DynamoDB
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DynamoDB Streams and Amazon Lambda triggers

Amazon DynamoDB is integrated with Amazon Lambda so that you can create triggers—pieces of code that automatically respond to events in DynamoDB Streams. With triggers, you can build applications that react to data modifications in DynamoDB tables.

If you enable DynamoDB Streams on a table, you can associate the stream Amazon Resource Name (ARN) with an Amazon Lambda function that you write. All mutation actions to that DynamoDB table can then be captured as an item on the stream. For example, you can set a trigger so that when an item in a table is modified a new record immediately appears in that table's stream.

The Amazon Lambda service polls the stream for new records four times per second. When new stream records are available, your Lambda function is synchronously invoked. You can subscribe up to two Lambda functions to the same DynamoDB stream.

The Lambda function can send a notification, initiate a workflow, or perform many other actions that you specify. You can write a Lambda function to simply copy each stream record to persistent storage, such as Amazon S3 File Gateway (Amazon S3), and create a permanent audit trail of write activity in your table. Or suppose that you have a mobile gaming app that writes to a GameScores table. Whenever the TopScore attribute of the GameScores table is updated, a corresponding stream record is written to the table's stream. This event could then trigger a Lambda function that posts a congratulatory message on a social media network. This function could also be written to ignore any stream records that are not updates to GameScores, or that do not modify the TopScore attribute.

If your function returns an error, Lambda retries the batch until it processes successfully or the data expires. You can also configure Lambda to retry with a smaller batch, limit the number of retries, discard records once they become too old, and other options.

As performance best practices, the Lambda function needs to be short lived. To avoid introducing unnecessary processing delays, it also should not execute complex logic. For a high velocity stream in particular, it is better to trigger an asynchronous post-processing step function workflows than synchronous long running Lambdas.

For more information about Amazon Lambda, see the Amazon Lambda Developer Guide.