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Let's Get to the Rapids
- 1. Let’s Get to the Rapids Understanding Java 8 Stream Performance JavaDay Kyiv November 2015 @mauricenaftalin
- 2. @mauricenaftalin Maurice Naftalin Developer, designer, architect, teacher, learner, writer
- 3. @mauricenaftalin Maurice Naftalin Developer, designer, architect, teacher, learner, writer JavaOne Rock Star Java Champion
- 6. Repeat offender: Maurice Naftalin Java 5 Java 8
- 8. Agenda – Background – Java 8 Streams – Parallelism – Microbenchmarking – Case study – Conclusions
- 9. Streams – Why? • Bring functional style to Java • Exploit hardware parallelism – “explicit but unobtrusive”
- 10. Streams – Why? • Intention: replace loops for aggregate operations 7
- 11. Streams – Why? • Intention: replace loops for aggregate operations List<Person> people = … Set<City> shortCities = new HashSet<>(); for (Person p : people) { City c = p.getCity(); if (c.getName().length() < 4 ) { shortCities.add(c); } } instead of writing this: 7
- 12. Streams – Why? • Intention: replace loops for aggregate operations • more concise, more readable, composable operations, parallelizable Set<City> shortCities = new HashSet<>(); for (Person p : people) { City c = p.getCity(); if (c.getName().length() < 4 ) { shortCities.add(c); } } instead of writing this: List<Person> people = … Set<City> shortCities = people.stream() .map(Person::getCity) .filter(c -> c.getName().length() < 4) .collect(toSet()); 8 we’re going to write this:
- 13. Streams – Why? • Intention: replace loops for aggregate operations • more concise, more readable, composable operations, parallelizable Set<City> shortCities = new HashSet<>(); for (Person p : people) { City c = p.getCity(); if (c.getName().length() < 4 ) { shortCities.add(c); } } instead of writing this: List<Person> people = … Set<City> shortCities = people.stream() .map(Person::getCity) .filter(c -> c.getName().length() < 4) .collect(toSet()); 8 we’re going to write this:
- 14. Streams – Why? • Intention: replace loops for aggregate operations • more concise, more readable, composable operations, parallelizable Set<City> shortCities = new HashSet<>(); for (Person p : people) { City c = p.getCity(); if (c.getName().length() < 4 ) { shortCities.add(c); } } instead of writing this: List<Person> people = … Set<City> shortCities = people.parallelStream() .map(Person::getCity) .filter(c -> c.getName().length() < 4) .collect(toSet()); 9 we’re going to write this:
- 15. Visualising Sequential Streams x2x0 x1 x3x0 x1 x2 x3 Source Map Filter Reduction Intermediate Operations Terminal Operation “Values in Motion”
- 16. Visualising Sequential Streams x2x0 x1 x3x1 x2 x3 ✔ Source Map Filter Reduction Intermediate Operations Terminal Operation “Values in Motion”
- 17. Visualising Sequential Streams x2x0 x1 x3 x1x2 x3 ❌✔ Source Map Filter Reduction Intermediate Operations Terminal Operation “Values in Motion”
- 18. Visualising Sequential Streams x2x0 x1 x3 x1x2x3 ❌✔ Source Map Filter Reduction Intermediate Operations Terminal Operation “Values in Motion”
- 19. Practical Benefits of Streams?
- 20. Practical Benefits of Streams?
- 21. Practical Benefits of Streams? Functional style will affect (nearly) all collection processing
- 22. Practical Benefits of Streams? Functional style will affect (nearly) all collection processing Automatic parallelism is useful, in certain situations
- 23. Practical Benefits of Streams? Functional style will affect (nearly) all collection processing Automatic parallelism is useful, in certain situations - but everyone cares about performance!
- 24. Parallelism – Why? The Free Lunch Is Over http://www.gotw.ca/publications/concurrency-ddj.htm
- 25. Intel Xeon E5 2600 10-core
- 30. What to Measure?
- 31. What to Measure? How do code changes affect system performance?
- 32. What to Measure? How do code changes affect system performance?
- 33. What to Measure? How do code changes affect system performance? Controlled experiment, production conditions
- 34. What to Measure? How do code changes affect system performance? Controlled experiment, production conditions - difficult!
- 35. What to Measure? How do code changes affect system performance? Controlled experiment, production conditions - difficult!
- 36. What to Measure? How do code changes affect system performance? Controlled experiment, production conditions - difficult! So: controlled experiment, lab conditions
- 37. What to Measure? How do code changes affect system performance? Controlled experiment, production conditions - difficult! So: controlled experiment, lab conditions - beware the substitution effect!
- 38. Microbenchmarking Really hard to get meaningful results from a dynamic runtime: – timing methods are flawed – System.currentTimeMillis() and System.nanoTime() – compilation can occur at any time – garbage collection interferes – runtime optimizes code after profiling it for some time – then may deoptimize it – optimizations include dead code elimination
- 39. Microbenchmarking Don’t try to eliminate these effects yourself! Use a benchmarking library – Caliper – JMH (Java Benchmarking Harness) Ensure your results are statistically meaningful Get your benchmarks peer-reviewed
- 40. Case Study: grep -b grep -b: “The offset in bytes of a matched pattern is displayed in front of the matched line.”
- 41. Case Study: grep -b The Moving Finger writes; and, having writ, Moves on: nor all thy Piety nor Wit Shall bring it back to cancel half a Line Nor all thy Tears wash out a Word of it. rubai51.txt grep -b: “The offset in bytes of a matched pattern is displayed in front of the matched line.”
- 42. Case Study: grep -b The Moving Finger writes; and, having writ, Moves on: nor all thy Piety nor Wit Shall bring it back to cancel half a Line Nor all thy Tears wash out a Word of it. rubai51.txt grep -b: “The offset in bytes of a matched pattern is displayed in front of the matched line.” $ grep -b 'W.*t' rubai51.txt 44:Moves on: nor all thy Piety nor Wit 122:Nor all thy Tears wash out a Word of it.
- 43. Because we don’t have a problem Why Shouldn’t We Optimize Code?
- 44. Why Shouldn’t We Optimize Code? Because we don’t have a problem - No performance target!
- 45. Because we don’t have a problem - No performance target! Else there is a problem, but not in our process Why Shouldn’t We Optimize Code?
- 46. Because we don’t have a problem - No performance target! Else there is a problem, but not in our process - The OS is struggling! Why Shouldn’t We Optimize Code?
- 47. Because we don’t have a problem - No performance target! Else there is a problem, but not in our process - The OS is struggling! Else there’s a problem in our process, but not in the code Why Shouldn’t We Optimize Code?
- 48. Because we don’t have a problem - No performance target! Else there is a problem, but not in our process - The OS is struggling! Else there’s a problem in our process, but not in the code - GC is using all the cycles! Why Shouldn’t We Optimize Code?
- 49. Because we don’t have a problem - No performance target! Else there is a problem, but not in our process - The OS is struggling! Else there’s a problem in our process, but not in the code - GC is using all the cycles! Now we can consider the code Else there’s a problem in the code… somewhere - now we can consider optimising!
- 50. 122Nor …Moves … 44 grep -b: Collector combiner 0The …[ , 80Shall … ], ,
- 51. 41 122Nor …Moves … 36 44 grep -b: Collector combiner 0The … 44[ , 42 80Shall … ], ,
- 52. 41 122Nor …Moves … 36 44 grep -b: Collector combiner 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [
- 53. 41 122Nor …Moves … 36 44 grep -b: Collector combiner 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [ Moves … 36 0 41 0Nor …42 0Shall …0The … 44[ ]] [[ [] ]
- 54. grep -b: Collector accumulator 44 0The moving … writ, “Moves on: … Wit” 44 0The moving … writ, 36 44Moves on: … Wit ][ ][ , [ ] Supplier “The moving … writ,” accumulator accumulator
- 55. 41 122Nor …Moves … 36 44 grep -b: Collector solution 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [
- 56. 41 122Nor …Moves … 36 44 grep -b: Collector solution 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [ 80
- 57. 41 122Nor …Moves … 36 44 grep -b: Collector solution 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [ 80
- 58. 41 122Nor …Moves … 36 44 grep -b: Collector solution 0The … 44[ , 42 80Shall … ] 41 42Nor …Moves … 36 440The … 44[ , 42 0Shall … ] , , ,] [ 80
- 59. What’s wrong?
- 60. What’s wrong?
- 61. What’s wrong? • Possibly very little
- 62. What’s wrong? • Possibly very little - overall performance comparable to Unix grep -b
- 63. What’s wrong? • Possibly very little - overall performance comparable to Unix grep -b • Can we improve it by going parallel?
- 65. Serial vs. Parallel • The problem is a prefix sum – every element contains the sum of the preceding ones.
- 66. Serial vs. Parallel • The problem is a prefix sum – every element contains the sum of the preceding ones. - Combiner is O(n)
- 67. Serial vs. Parallel • The problem is a prefix sum – every element contains the sum of the preceding ones. - Combiner is O(n) • The source is streaming IO (BufferedReader.lines())
- 68. Serial vs. Parallel • The problem is a prefix sum – every element contains the sum of the preceding ones. - Combiner is O(n) • The source is streaming IO (BufferedReader.lines()) • Amdahl’s Law strikes:
- 69. Serial vs. Parallel • The problem is a prefix sum – every element contains the sum of the preceding ones. - Combiner is O(n) • The source is streaming IO (BufferedReader.lines()) • Amdahl’s Law strikes:
- 70. A Parallel Solution for grep -b Need to get rid of streaming IO – inherently serial Parallel streams need splittable sources
- 71. Stream Sources Implemented by a Spliterator
- 72. Stream Sources Implemented by a Spliterator
- 73. Stream Sources Implemented by a Spliterator
- 74. Stream Sources Implemented by a Spliterator
- 75. Stream Sources Implemented by a Spliterator
- 76. Stream Sources Implemented by a Spliterator
- 77. Stream Sources Implemented by a Spliterator
- 78. Stream Sources Implemented by a Spliterator
- 79. Stream Sources Implemented by a Spliterator
- 80. Moves …Wit LineSpliterator The moving Finger … writ n Shall … Line Nor all thy … itn n n spliterator coverage
- 81. Moves …Wit LineSpliterator The moving Finger … writ n Shall … Line Nor all thy … itn n n spliterator coverage MappedByteBuffer
- 82. Moves …Wit LineSpliterator The moving Finger … writ n Shall … Line Nor all thy … itn n n spliterator coverage MappedByteBuffer mid
- 83. Moves …Wit LineSpliterator The moving Finger … writ n Shall … Line Nor all thy … itn n n spliterator coverage MappedByteBuffer mid
- 84. Moves …Wit LineSpliterator The moving Finger … writ n Shall … Line Nor all thy … itn n n spliterator coveragenew spliterator coverage MappedByteBuffer mid
- 85. Parallelizing grep -b • Splitting action of LineSpliterator is O(log n) • Collector no longer needs to compute index • Result (relatively independent of data size): - sequential stream ~2x as fast as iterative solution - parallel stream >2.5x as fast as sequential stream - on 4 hardware threads
- 86. When to go Parallel The workload of the intermediate operations must be great enough to outweigh the overheads (~100µs): – initializing the fork/join framework – splitting – concurrent collection Often quoted as N x Q size of data set processing cost per element
- 87. Intermediate Operations Parallel-unfriendly intermediate operations: stateful ones – need to store some or all of the stream data in memory – sorted() those requiring ordering – limit()
- 88. Collectors Cost Extra! Depends on the performance of accumulator and combiner functions • toList(), toSet(), toCollection() – performance normally dominated by accumulator • but allow for the overhead of managing multithread access to non- threadsafe containers for the combine operation • toMap(), toConcurrentMap() – map merging is slow. Resizing maps, especially concurrent maps, is very expensive. Whenever possible, presize all data structures, maps in particular.
- 89. Threads for executing parallel streams are (all but one) drawn from the common Fork/Join pool • Intermediate operations that block (for example on I/O) will prevent pool threads from servicing other requests • Fork/Join pool assumes by default that it can use all cores – Maybe other thread pools (or other processes) are running? Parallel Streams in the Real World
- 90. Performance mostly doesn’t matter But if you must… • sequential streams normally beat iterative solutions • parallel streams can utilize all cores, providing - the data is efficiently splittable - the intermediate operations are sufficiently expensive and are CPU-bound - there isn’t contention for the processors Conclusions