Kafka 用于许多涉及高容量、实时或流数据的场景——消息传递、数据管道、事件驱动架构、流处理、日志聚合等。理解这些使用场景有助于明确 Kafka 的适用范围。
常见的使用场景
✓ MESSAGING / event streaming → decoupled pub/sub between services at scale
✓ DATA PIPELINES / integration → reliably stream data between systems (databases, services,
data warehouses, analytics) — a central data "backbone"
✓ EVENT-DRIVEN ARCHITECTURE → services emit and react to events; event sourcing (events as
the source of truth)
✓ STREAM PROCESSING → real-time processing/analytics on event streams (Kafka Streams, Flink)
✓ LOG AGGREGATION → collect logs/metrics from many services into one stream
✓ ACTIVITY TRACKING → user activity, clickstreams, telemetry at high volume
✓ CHANGE DATA CAPTURE (CDC) → stream database changes to other systems
✓ METRICS / monitoring → real-time metrics collection and processing
