Message queues and asynchronous processing in Serverless architecture are important means to achieve decoupling and scalability:
Message queue services:
1. Amazon SQS
- Standard queues: At-least-once delivery, maximum throughput
- FIFO queues: Strict ordering, exactly-once delivery
- Applicable scenarios: Task queues, asynchronous processing, service decoupling
2. Amazon SNS
- Pub-sub: One-to-many message distribution
- Multi-protocol support: Supports HTTP, Email, SMS, Lambda, etc.
- Applicable scenarios: Notification systems, event broadcasting
3. Amazon Kinesis
- Real-time stream processing: Process real-time data streams
- Data partitioning: Supports data partitioning and parallel processing
- Applicable scenarios: Log collection, real-time analysis, IoT data
Asynchronous processing patterns:
1. Task queue pattern
- Producer-consumer: Producers send tasks to queues, consumers process asynchronously
- Work queues: Multiple consumers process tasks in parallel
- Priority queues: Support task priorities
2. Pub-sub pattern
- Event-driven: Publish events, multiple subscribers respond
- Topic subscriptions: Classify messages through topics
- Filtering rules: Filter messages based on rules
3. Stream processing pattern
- Real-time processing: Process data streams in real-time
- Window computing: Aggregate data based on time windows
- State management: Maintain processing state
Best practices:
1. Message design
- Message format: Use structured formats like JSON
- Message size: Control message size, avoid too large
- Message versioning: Support message version evolution
2. Error handling
- Retry mechanism: Implement exponential backoff retry
- Dead Letter Queue: Send failed messages to DLQ
- Monitoring and alerting: Monitor queue depth and processing latency
3. Performance optimization
- Batch processing: Process messages in batches for efficiency
- Concurrency control: Control consumer concurrency
- Resource optimization: Reasonably configure function resources
Candidates should be able to share message queue and asynchronous processing experience from actual projects.