ForkJoinPools and parallel streams

ForkJoinPools and parallel streams

• 1.
  ForkJoinPools & parallel streams … areuseless?
• 2.
  Agenda ● Motivation ● History ●Big Picture ● ForkJoinPool ● Parallel streams ● Advises ● Alternatives
• 3.
  :(){ :|:& };:
• 4.
  Motivation why?
• 5.
  Motivation Think Twice BeforeUsing Java 8 Parallel Streams - DZone Java https://dzone.com/articles/think-twice-using-java-8 Java Parallel Streams Can Unexpectedly Degrade Performance ... https://zeroturnaround.com/.../java-parallel-streams-are-bad-for-your-... A Java Parallel Calamity - Cooperative Software Systems, Inc coopsoft.com/ar/Calamity2Article.html A Java Fork-Join Calamity - Cooperative Software Systems, Inc coopsoft.com/ar/CalamityArticle.html What's Wrong in Java 8, Part III: Streams and Parallel Streams - DZone ... https://dzone.com/articles/whats-wrong-java-8-part-iii ...
• 6.
  History
• 7.
  History 2000 2006 JDK 6 20112014 JDK 7 JDK 8 ... JDK 9 extension fork-join parallel Files.lines()
• 8.
  Big Picture
• 9.
  Big Picture
• 13.
  ForkJoinPool
• 14.
  Recursive Task Decomposition forkfork fork fork fork fork join join join join join join
• 16.
  main w steal w Anatomy
• 17.
  Pros & Cons Pros ●No blocked wait on join() ● Lower latency in case of multiple jobs ● API for task decomposition and parallel computation ● Load-balancing mechanism ● Local submission queues may improve CPU caching
• 18.
  Pros & Cons Cons ●Workers are usual java.lang.Thread extensions ● Computation overhead from stealing mechanism (spinlock) ● Warm-up has some overhead ● Hard to tune the computation. (Task decomposition granularity) ● Can be slower than sequential computing ● Can be memory intensive ● API has some flaws ● No JMX ● Cryptic source code
• 19.
  The Common Pool
• 20.
  The Common Pool bootCL Extension CL System CL I’m a singleton! URL CL#1 URL CL#1
• 22.
  Pros & Cons Cons ●No multitenancy ● main thread joins the workers ● Error prone ● Computation may stall ● We can’t use operations that are waiting for resources ○ I/O ○ Shared memory access ○ Network wait
• 23.
  Parallel Streams
• 24.
  CPU4 CPU3 CPU2 CPU1 stream()Collection parallel() map() map() map() map() filter() filter() filter() filter() reduce()
• 25.
  Parallel Minimum Search
• 26.
  Pros & Cons Pros ●Ease of use ● Functional interface ● Easy to parallelize an existing method ● Lots of ready-made operations in Java 8 CPU4 CPU3 CPU2 CPU1
• 27.
  Pros & Cons Cons ●Uses common fork-join pool! ○ One slow operation affects others ● Computation can be invalid as ordering is not guaranteed ● Can be slower, than sequential stream CPU4 CPU3 CPU2 CPU1
• 28.
  Performance Considerations ● Collectionsize ● Splitting efficiency ○ Arrays / ArrayLists ○ HashMaps/HashSets ○ LinkedLists ○ BlockingQueues ● Operation efficiency ○ Dependencies ○ Statelessnes CPU4 CPU3 CPU2 CPU1
• 29.
  Executive Summary
• 30.
  Executive Summary ● Allparallel steam uses common pool ● One pool per JVM ● One slow operation can block all others ● Waiting for resources (IO/Network/Lock) is slow ● Even there’s no JMX ● What can I do?
• 31.
  Holy Hack How tostart parallel stream using custom ForkJoinPool?
• 34.
  Holy Hack II UsingCompletableFutute
• 36.
  Advises
• 37.
  Advises ● Avoid usingthe common pool ○ Check every library for parallel() calls ○ Check common-pool thread usage in test environment ● Try to automate testing of parallel operations ● Performance test with sequential and parallel streams ○ Use different collection sizes ○ Take number of CPUs into account ● Use CompletableFuture, which has an Executor argument ○ Try out various executors, but be aware of what you’re doing!
• 38.
  Bulkheads
• 39.
  Service Data Access Gateway Using Bulkheads incoming requestspool #2 pool #3 pool #1
• 40.
  Alternatives
• 41.
  Alternatives ● Reactor Core- http://projectreactor.io/ ● Akka - http://akka.io/ ● Reactive Streams - http://www.reactive-streams.org/ ● Speedment Java Stream ORM - https://github.com/speedment/speedment ● Quasar - https://github.com/puniverse/quasar
• 42.
  Thanks!
• 43.
  References ● GitHub examples& presentation ○ https://github.com/gitaroktato/forkjoinpools-and-parallelstreams ● HTTP Async calls in REST ○ https://jersey.java.net/documentation/latest/async.html ○ http://allegro.tech/2014/10/async-rest.html ● Presentations discussing Java 8 ‘s parallel & asynchronous capabilities ○ https://www.youtube.com/watch?v=fabN6HNZ2qY ○ https://www.youtube.com/watch?v=h1N-qbo5VyY ○ https://www.youtube.com/watch?v=HdnHmbFg_hw ● Talks with the authors of parallel computation in Java 8 ○ https://www.infoq.com/interviews/doug-lea-fork-join ○ https://www.infoq.com/presentations/parallel-java-se-8
• 44.
  References II ● Shortestpath algorithm parallelization in graphs ○ http://www.mcs.anl.gov/~itf/dbpp/text/node35.html ○ http://www.cslab.ntua.gr/courses/pps/files/fall2011/paper_sfhmmy.pdf ● Original Fork-Join whitepaper ○ http://gee.cs.oswego.edu/dl/papers/fj.pdf ● ForkJoinPool explanations ○ http://www.h-online.com/developer/features/The-fork-join-framework-in-Java-7-1762357.html ○ http://howtodoinjava.com/java-7/forkjoin-framework-tutorial-forkjoinpool-example/ ○ http://www.oracle.com/technetwork/articles/java/fork-join-422606.html ○ http://www.javaworld.com/article/2078440/enterprise-java/java-tip-when-to-use-forkjoinpool -vs-executorservice.html
• 45.
  References III ● Parallelstream explanations ○ https://blog.oio.de/2016/01/22/parallel-stream-processing-in-java-8-performance-of-sequentia l-vs-parallel-stream-processing/ ○ http://www.drdobbs.com/jvm/parallel-array-operations-in-java-8/240166287?pgno=2 ● Files.lines().parallel() issue in Java 8 ○ http://bytefish.de/blog/jdk8_files_lines_parallel_stream/