Latency and Event Tracing with Spring Cloud Data Flow
summary
Combines Sleuth/Zipkin with Spring Cloud Data Flow to trace events end-to-end: three Spring Cloud Stream apps (source → processor → sink) deployed via the Data Flow GUI, using the Rabbit binder to carry trace headers. Adds Sleuth deps and @NewSpan annotations, then Zipkin aggregates traces; default sampling is 10% (configurable to 100%). Demo shows a 691 ms trace, identifies the transform as ~593 ms and getSymbol as ~350 ms, fixes with finer spans and tags, exposes retries and a 1s queue delay under load — solution works with minimal code.
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chapters (22)
- 0:00Introduction and background
- 1:12Schwab’s client-first focus
- 2:15Event processing and rubber duck analogy
- 4:53Spring Cloud Data Flow components
- 6:16Telemetry and tracing with Sleuth and Zipkin
- 9:06Demo requirements and setup
- 11:35Building the stream with Spring Cloud Data Flow
- 13:19Deploying the stream
- 17:51Identifying latency issues
- 18:53Manual latency tracing drawbacks
- 20:58Introducing Sleuth for tracing
- 22:43Deploying Sleuth and Zipkin
- 23:46Analyzing traces in Zipkin
- 26:16Pinpointing slow methods
- 28:03Adding custom tags to spans
- 29:29Debugging errors with Zipkin
- 31:39Understanding retry logic
- 33:25Investigating slow traces during peak volume
- 36:11Advanced Sleuth features
- 39:05Limitations of Sleuth and Zipkin
- 41:11Conclusion and hiring announcement
- 42:49Q&A session
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