Heldinhow Kafka Streams
Overview
Kafka Streams is a client library for building real-time streaming applications. This skill should be invoked when building real-time data processing pipelines, event processing systems, or stream transformations.
Core Principles
- Stream Processing: Process data in real-time as it arrives
- Topology: Define source, processor, and sink operations
- State Management: Handle stateful operations with state stores
- Windowing: Process data in time windows for aggregations
Preparation Checklist
- Set up Kafka cluster
- Define input and output topics
- Plan processing topology
- Choose state management approach
Step-by-Step Process
- Setup: Configure Kafka cluster and topics
- Create: Build Kafka Streams application
- Define: Create processing topology
- Transform: Implement maps, filters, joins
- Aggregate: Use windowing for time-based aggregations
- Deploy: Run as distributed application
Do's and Don'ts
- ✅ Do use exactly-once processing
- ✅ Do handle state store backup
- ✅ Do plan for rebalancing
- ❌ Don't ignore processing semantics
- ❌ Don't skip error handling
- ❌ Don't forget to close resources
Anti-Patterns
- No State: Always using stateless processing
- Large Windows: Using excessively large windows
- No Error Handling: Ignoring failed records
- Resource Leaks: Not closing Kafka producers/consumers