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Failure management

PDFRSS

Certain parts of a serverless application are dictated by asynchronous calls to various components in an event-driven fashion, such as by pub/sub and other patterns. When asynchronous calls fail, they should be captured and retried whenever possible. Otherwise, data loss can occur, resulting in a degraded customer experience.

Use a dead-letter queue mechanism to retain, investigate, and retry failed transactions.

• AWS Lambda allows failed transactions to be sent to a dedicated Amazon SQS dead-letter queue on a per function basis.

• Amazon Kinesis Data Streams and Amazon DynamoDB Streams retry the entire batch of items. Repeated errors block processing of the affected shard until the error is resolved or the items expire.

• Within AWS Lambda, you can configure Maximum Retry Attempts, Maximum Record Age and Destination on Failure to respectively control retry while processing data records, and effectively remove poison-pill messages from the batch by sending its metadata to an Amazon SQS dead-letter queue for further analysis.

AWS SDKs provide back-off and retry mechanisms by default when talking to other AWS services that are sufficient in most cases. However, review and tune them to suit your needs, especially HTTP keepalive, connection, and socket timeouts. Whenever possible, use Step Functions to minimize the amount of custom try/catch, back-off, and retry logic within your Serverless applications. For example, you can use a Step Functions integration to save failed state runs and their state into a DLQ. For more information on costs trade-offs, see the cost optimization pillar section.

Figure 20: Step Functions state machine with DLQ step

Partial failures can occur in non-atomic operations, such as PutRecords (Kinesis) and BatchWriteItem (DynamoDB), since they return successful if at least one record has been ingested successfully. Always inspect the response when using such operations, and programmatically deal with partial failures. When consuming from Kinesis or DynamoDB Streams use Lambda error handling controls, such as maximum record age, maximum retry attempts, DLQ on failure, and Bisect batch on function error, to build additional resiliency into your application. For synchronous parts that are transaction-based and depend on certain guarantees and requirements, rolling back failed transactions as described by the Saga pattern also can be achieved by using Step Functions state machines, which will decouple and simplify the logic of your application.

Figure 21: Step Functions state machine Saga pattern

Choose the Step Functions type based on your workload. For short-running synchronous and asynchronous high-volume workloads, use Step Functions - Sync Express. If you need to automate long-running workflows and want to have additional durability and audit go with Step Functions Standard.