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Reactive programming at scale
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- 2. Paul Downey ● Directorof UI & APIs for demand advertising ● Based in Dublin, Ireland ● Email: paul.downey@teamaol.com John McClean ● Systems Architect Demand Side Forecasting ● Based in Dublin, Ireland. ● Twitter: cyclops_aol ● Medium: https://medium.com/@johnmcclean ● Email: john.mcclean@teamaol.com About Speakers
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- 4. RTB Ad Cycle 100ms 20,000,000,000+
- 5. ● Responsive ● Scale ●Resilient ● Capacity to process lots of data System Requirements
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- 8. Race Conditions Deadlocks Contention Non-Deterministic State ThreadStarvation Livelocks
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- 11. Java Concurrency InTheory 11
- 12. Java Concurrency InPractice 12
- 13. Why is itlike this? 13
- 14. Why is itlike this? 14
- 15. Can we fixthis? 15
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- 17. What is Reactive? 17 FunctionalReactive Programming? Actor based concurrency?
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- 19. Is it somethingnew? 19 1997
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- 21. Is it somethingnew? 21
- 22. Well established onthe front end 22
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- 25. Functional programming 25 Less buggycode Easier to parallelise Easier to optimise
- 26. Reactive programming 26 Reactive pipelinemanages flow What not How
- 27. Reactive programming 27 What todo Change each element by multiplying by 100 Remove all elements over 550 Print out each remaining element
- 28. Reactive programming 28 Composition Change eachelement by multiplying by 100 Remove all elements over 550 Print out each remaining element map(e -> e*100) forEach(System.out::println) filter(e->e<551)
- 29. Reactive programming 29 .map(e ->e*100) .forEach(System.out::println) .filter(e->e<551) Example ReactiveSeq.of(6,5,1,2)
- 30. A Reactive Pipeline 30 firststage second stage terminal stage
- 31. A Reactive Pipeline 31 firststage (map) second stage (filter) terminal stage (forEach)
- 32. A Reactive Pipeline 32 firststage (map) second stage (filter) terminal stage (forEach) Data is pushed through the pipeline 500 2 1 5 600
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- 34. Forecasting 34 Campaign Planning andOptimization Query data across petabytes data Accuracy & Responsiveness
- 35. Price / VolumeCurve
- 36. Every Day WeProcess 36 Bid Request Records Impression Records Viewability Records 20B 2B 2B
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- 39. What we needto do
- 40. A Reactive Pipeline 40 firststage (map) second stage (filter) terminal stage (forEach) Data is pushed through the pipeline 500 2 1 5 600
- 41. Scaling Up 41 first stage (map) secondstage (filter) terminal stage (forEach) core 1 first stage (map) second stage (filter) terminal stage (forEach) core 2 first stage (map) second stage (filter) terminal stage (forEach) core 3 first stage (map) second stage (filter) terminal stage (forEach) core 4
- 42. Scaling Up forI/O 42 first stage (map) second stage (filter) terminal stage (forEach) future 1 future 2 future 3 future 4 future 5 future 6 future 7 future 8 future 9 future 10 future 11
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- 44. What would the codelook like?
- 45. Sequential Stream 45 .map(e ->e*100) .forEach(System.out::println) .filter(e->e<551) Example ReactiveSeq.of(6,5,1,2) .futureOperations(executor)
- 46. Async I/O 46 .map(e ->e*100) .filter(e->e<551) Example FutureW.of(()->loadData(6)) //non-blocking, FutureW executes asynchronously .forEach(System.out::println)
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- 48. What we needto do
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