Dealing with failed processing

A Lambda function consumes batches of records from Amazon Kinesis Data Streams. Occasionally, only a small number of records in a batch fail processing, causing successfully processed records to be retried unnecessarily.

Which configuration BEST minimizes duplicate processing?

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Learn the architectural foundations of cloud-native software. By the end of this chapter, you will be able to choose between monolith, microservices, serverless, and event-driven patterns; reason about tight vs loose coupling and synchronous vs asynchronous communication; manage state in stateless services; and design for resilience with retries, exponential backoff, circuit breakers, and dead-letter queues.

Exercise 1: Architecture styles and communication patterns Exercise 2: Communication patterns Exercise 3: Message ordering Exercise 4: Classify communication mechanisms Exercise 5: Coordinating services and managing state Exercise 6: Building workflows Exercise 7: Which architecture pattern is the best fit?Exercise 8: Handling duplicate events safely Exercise 9: Designing fault-tolerant and resilient applications on AWS Exercise 10: Dealing with temporary errors Exercise 11: Implementing exponential backoff with jitter Exercise 12: Dealing with downstream service failures

Build, secure, and operate APIs on Amazon API Gateway. By the end of this chapter, you will be able to pick the right API type (REST, HTTP, or WebSocket); configure Lambda integrations with validation and transformations; deploy APIs across stages using CloudFormation, SAM, and CDK; and protect them with Amazon Cognito, IAM, Lambda authorizers, encryption, and throttling.

Exercise 1: Designing APIs on AWS Exercise 2: Choosing the right API type Exercise 3: Migrating an API Exercise 4: Classifying API Gateway types Exercise 5: API development Exercise 6: Which API Gateway capability Exercise 7: Fix a 502 from a Lambda proxy integration Exercise 8: Connect an API to Lambda with a proxy integration Exercise 9: Testing and deploying your APIs Exercise 10: Testing locally with SAM Exercise 11: Which infrastructure as code tool should you use?Exercise 12: Ordering the serverless CI/CD workflow Exercise 13: Protecting your APIs Exercise 14: Failing API requests Exercise 15: Custom authorization rules Exercise 16: Handling traffic spikes

Build decoupled, event-driven, and streaming workloads. By the end of this chapter, you will be able to decouple services with Amazon SQS queues (including FIFO and dead-letter queues) and Amazon SNS topics with subscription filtering; route events with Amazon EventBridge using rules, event patterns, and schemas; process high-throughput data with Amazon Kinesis Data Streams and Data Firehose; and react to table changes in real time with Amazon DynamoDB Streams.

Exercise 1: Decoupling using AWS Messaging Services Exercise 2: Increasing polling time on an SQS queue Exercise 3: Which messaging service Exercise 4: Classifying SQS queue types Exercise 5: Event-driven applications Exercise 6: Optimizing message processing Exercise 7: Dealing with failed events Exercise 8: Handling duplicate events Exercise 9: Streaming data Exercise 10: Scaling streaming data Exercise 11: Dealing with failed processing

Exercice actuel

Exercise 12: Kinesis Data Streams or SQS?Exercise 13: Streaming data with DynamoDB Exercise 14: Enable a DynamoDB stream with both item images Exercise 15: Minimizing duplicating records Exercise 16: Choosing an idempotency key