Recomendados
Más contenido relacionado
Destacado
Destacado (18)
Similar a Understanding Microservice Performance
Similar a Understanding Microservice Performance (20)
Último
Último (20)
Understanding Microservice Performance
- 2. The performance of a distributed system is the combined performance of its collaborating services and their communication links
- 4. Who am I? ▸ CTO @ Skipjaq ▸ ML-driven performance optimisation ▸ Co-founder of SpringSource ▸ Once upon a time I… ▸ Contributed to Spring Framework ▸ Wrote a book about Spring ▸ Talked a lot about Spring
- 5. Who am I? ▸ I’m on Twitter: ▸ @robertharrop ▸ I’m on Github: ▸ github.com/robharrop ▸ I write about maths and performance ▸ https://robharrop.github.io If you have questions after the session, {grab, tweet} me.
- 6. Agenda
- 7. After this talk you will know how to: ▸ Measure performance correctly ▸ Find potential performance disasters ▸ Identify the best candidates for optimisation ▸ Model complex micro services systems ▸ Forecast system scalability
- 10. How fast can I do a thing, and how many things can I do every period? What do we mean by performance?
- 11. What measures performance? Latency (how fast) and Throughput (how many)
- 12. Throughput ▸ The rate of processing: x per y ▸ Requests per second ▸ Records per minute ▸ Messages per second ▸ Tasks per day
- 13. Latency ▸ Time taken… for something ▸ Service time? ▸ First byte? ▸ First response complete? ▸ Last byte? ▸ Render? ▸ Moral of the story: define what you mean by latency
- 14. Measuring
- 15. This is where everything goes wrong
- 16. Crib Sheet ▸ Record timestamped requests with observed latency and success/error ▸ Throughput ▸ Min, max, mean ▸ Varying time windows (10s, 30s, 1m, 5m, …) ▸ Latency ▸ Min, max, 95th, 99th, 99.9th and other tail percentiles ▸ Mean just means meaningless
- 17. We need to talk about latency ▸ Latency isn’t exponentially-distributed ▸ And it certainly isn’t normally-distributed ▸ Latency distributions have heavy tails ▸ Latency distributions are multi-modal ▸ Customers see tail latencies way more than you think ▸ Don’t let percentiles trick you ▸ Understand what latency means to your business
- 19. Tail Latencies
- 20. Is my customer getting good service?
- 21. What does this mean in reality?
- 22. Is my customer getting good service? 95th percentile 42 requests
- 23. Most customers will see tail latencies
- 25. Are latency and throughput useful when considered in isolation?
- 27. Little’s Law
- 28. Queueing Theory - Little’s Law
- 29. Queueing Theory - M/M/1 Queue
- 30. What can we conclude? ▸ Latency and throughput work in tandem ▸ At high throughput, latency degrades considerably
- 31. High utilisation is an early-warning sign
- 32. Latency Stacking
- 33. Service latencies stack ▸ For simple cases (feed-forward networks), latencies are additive ▸ Analytical models are available ▸ http://robharrop.github.io/maths/performance/2016/03/15/queue-networks.html ▸ For most interesting cases this cannot be assumed ▸ Simulation is the best option ▸ Pretty Damn Quick (PDQ) is a great tool, but requires a chunk of effort ▸ Guesstimate is great for quick and dirty models
- 37. Amdahl’s Law
- 38. How much improvement can we get from an optimisation?
- 39. Amdahl’s Law ▸ Theoretical improvement in latency given a fixed workload Theoretical max system speedup Speedup of part under optimisation Percentage of execution time in part under optimisation
- 40. Amdahl’s Law in the Limit ▸ Theoretical max is limited by parts of the system not under improvement Theoretical max system speedup Percentage of execution time in part under optimisation Speedup of part under optimisation
- 41. Thinking about Amdahl’s Law
- 42. Maximum theoretical system speedup [1.11] [1.67] [1.25] [1.43]
- 43. Drive optimisation choices by service utilisation
- 45. Can increasing capacity reduce performance?
- 46. TL;DR - Yes
- 47. Crosstalk overhead Universal Scalability Law Contention overhead Relative capacity Number of users
- 50. Measure crosstalk and target it for optimisation
- 53. Summary ▸ Measurements are critical ▸ Garbage in, garbage out ▸ Monitor utilisation for early-warning of disaster ▸ Little’s Law ▸ Monitor latency per-user, not just per-request ▸ Select optimisation targets carefully ▸ Amdahl’s Law ▸ Monitor crosstalk to forecast scalability ▸ Universal Scalability Law
- 54. Reading List and Q&A ▸ Release It! - Michael Nygard ▸ Systems Performance - Brendan Gregg ▸ Guerrilla Capacity Planning - Dr. Neil Gunther ▸ Practical Scalability Analysis - Baron Schwartz