As our systems grow, so does our software architecture complexity. As we scale and add more and more components, the interactions occurring between them become very complex and we start to lose visibility into the system. Traditional monitoring tools such as metrics and distributed logging still have their place, but they often fail to provide visibility across services. This is where distributed tracing thrives. During the talk we’ll explore what distributed tracing is, what open tools we can use to facilitate it and some lessons learned while implementing distributed tracing at Uber and how it helps us build big and impactful systems.

2. Distributed tracing for big
systems
Nikolay Stoitsev

5. Reliability

6. Observability

7. Monitoring

8. Distributed logging

9. Business metrics

11. Distributed tracing?

12. opentracing.io

13. opentracing.io

14. opentracing.io

15. Distributed context propagation

16. https://research.google.com/pubs/pub36356.html

17. Low overhead

18. Application-level transparency

19. Scalability

20. Store context in thread-local storage

21. Async callbacks

22. RPC instrumentation

24. http://zipkin.io/pages/architecture.html

25. opentracing.io

26. opentracing.io

27. opentracing.io
Trace

28. opentracing.io
Span

29. ChildOf
opentracing.io

30. Defines the data model

31. Defines the OpenTracing API

32. Cool but how to start?

33. Step 0
TChannel & Hyperbahn
https://github.com/uber/tchannel

34. https://youtu.be/CwPwP3UaqrY

36. Finagle
https://twitter.github.io/finagle

37. Step 1

39. https://github.com/jaegertracing

41. Sampling Strategy

42. Sample everything

43. Probabilistic approach

44. Rate limiting approach

45. Client libraries

47. Missing key-value logging API

48. Missing “child of” and “follows from”

51. Go
Java
Node.js
Python

52. Jersey
Dropwizard
Apache http clients

56. Demo
https://github.com/jaegertracing/jaeger/tree/master/examples/hotrod

57. https://eng.uber.com/distributed-tracing/

58. git clone git@github.com:jaegertracing/jaeger.git

59. Thanks!