a day in the life of a request

Transcript

1. a day in the life of a request

2. None

3. hello!

4. why is it slow?

5. latency

6. t0 t1

7. Designs, Lessons and Advice from Building Large Distributed Systems, Jeff

   Dean

8. What is in the tail? 0 0.5 1 1.5 2

   2.5 3 3.5 4 4.5 5 0 20 40 60 80 100 0 20 40 60 80 100 Percentage of requests Latency (ms) ? Measuring and Optimizing Tail Latency, Kathryn McKinley

9. Benchmarking "Hello, World!", Dick Sites

10. Amdahl's law, Wikipedia

11. Example 2: Task Scheduling in Spark Driver W1 W2 W3

    5 SnailTrail, critical participation Window Conventional profiling Window % time SnailTrail, Hoffmann et al

12. CPU Flame Graphs, Brendan Gregg

13. Systems Performance by Brendan Gregg

14. None

15. <span>

16. The Gantt Chart: A Working Tool of Management, Henry Wallace

    Clark

17. Twitter Dot Com, Google Chrome

18. Symfony

19. Dapper, Google

20. func ProcessVideo(ctx, video) { ctx, span := trace.StartSpan(ctx, "ProcessVideo") defer

    span.End() video.Process() }

21. things this helps debug

22. Travis CI

23. func (rl *redisRateLimiter) RateLimit(...) { conn := rl.pool.Get() defer conn.Close()

    ctx, span := trace.StartSpan(ctx, "Redis.RateLimit") defer span.End() ... }

24. tx0 tx1 tx2 tx3 tx4 tx5 ... blocked

25. </span>

26. context propagation

27. Dapper, Google

28. X-Request-ID

29. SELECT COUNT(*) FROM likes WHERE artist = 'CHVRCHES'

30. SELECT COUNT(*) FROM likes WHERE artist = 'CHVRCHES' /*request_id:123e4567-e89b-12d3- a456-426655440000*/

    Marginalia, Basecamp

31. EXPLAIN ANALYZE SELECT COUNT(*) FROM likes WHERE artist = 'CHVRCHES'

    /*request_id:123e4567-e89b-12d3- a456-426655440000*/

32. Aggregate Buffers: shared hit=74 read=41 -> Index Only Scan using

    likes_artist_idx on likes Index Cond: (artist = 'CHRVRCHES'::text) Heap Fetches: 10000 Buffers: shared hit=74 read=41 Planning Time: 0.344 ms Execution Time: 5.182 ms

33. req, err := http.NewRequest("GET", serviceURL, nil) req.Header.Add("X-Request-ID", requestID) resp, err

    := client.Do(req)

34. Canopy, Facebook

35. sampling

36. Dapper, Google

37. sampling decision

38. Travis CI

39. finding interesting traces

40. Honeycomb

41. LightStep

42. group by customer

43. happy path can also be interesting!

44. visualization

45. Jaeger, Uber

46. where do we go from here?

47. aggregation

48. None

49. Canopy, Facebook

50. Canopy, Facebook

51. Pivot Tracing, Mace et al

52. Pivot Tracing, Mace et al

53. kernel tracing

54. Systems Performance by Brendan Gregg

55. Debugging Latency in Go 1.11, Jaana B. Dogan

56. eBPF

57. None

58. Performance Analysis of Cloud Applications, Google

59. Performance Analysis of Cloud Applications, Google

60. Benchmarking "Hello, World!", Dick Sites

61. Benchmarking "Hello, World!", Dick Sites

62. Go Dynamic Tools, Dmitry Vyukov, GopherCon 2015

63. Visualization: Statemaps The Hurricane’s Butterfly, Bryan Cantrill

64. Stacked statemaps across machines Visualizing Systems with Statemaps, Bryan Cantrill

65. adaptively improving tail latency

66. "long requests reveal themselves" ~ Kathryn McKinley

67. The Tail Longest 200 requests 15 0 20 40 60

    80 100 120 0 50 100 150 200 latency (ms) Top 200 requests Network and networking queueing time Idle time CPU time Dispatch queueing time latency Network & other Idle CPU work Queuing at worker not noise Network imperfections OS imperfections Long requests Overload }noise } Measuring and Optimizing Tail Latency, Kathryn McKinley

68. dealing with noise

69. speeding up work

70. recap • tail latency matters • tracing helps debug it

71. OpenCensus

72. the morning paper blog.acolyer.org

73. • Dapper, a Large-Scale Distributed Systems Tracing Infrastructure from Google,

    2010 • Scuba: Diving into Data at Facebook from Facebook, 2016 • Canopy: An End-to-End Performance Tracing And Analysis System from Facebook, 2017 • Performance Analysis of Cloud Applications from Google, 2018 • Systems Performance: Enterprise and the Cloud by Brendan Gregg, 2013 • The Tail at Scale by Jeff Dean and Luiz André Barroso, 2013 • Designs, Lessons and Advice from Building Large Distributed Systems by Jeff Dean, 2009 • Data Center Computers: Modern Challenges in CPU Design by Dick Sites, 2015 • Measuring and Optimizing Tail Latency by Kathryn McKinley, Strange Loop 2017 • Benchmarking "Hello, World!" by Dick Sites, 2018 • Pivot Tracing: Dynamic Causal Monitoring for Distributed Systems by Mace et al, 2015 • RobinHood: Tail Latency Aware Caching by Berger et al, 2018 • SnailTrail: Generalizing Critical Paths for Online Analysis of Distributed Dataflows by Hoffmann et al, 2018

74. thanks! @igorwhilefalse