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Enabling Java in Latency Sensitive Environments - Dallas JUG April 2015

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Enabling Java in Latency Sensitive Environments examines the core issues that have historically kept Java from performing well in low latency environments and then offers solutions without trade-offs and compromises. The presentation describes sources of Java jitter including garbage collection and discusses common workarounds for latency and performance problems. The presentation also describes a JVM that allows Java applications to run with consistent low latency.

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Enabling Java in Latency Sensitive Environments - Dallas JUG April 2015

  1. 1. © Copyright Azul Systems 2015 © Copyright Azul Systems 2015 @azulsystems Enabling Java in Latency Sensitive Environments Matt Schuetze Azul Director of Product Management Matt Schuetze, Product Manager, Azul Systems Utah JUG, Murray UT, November 20, 2014 Dallas Java Developers Meetup Dallas, Texas 5/3/20151
  2. 2. © Copyright Azul Systems 2015 High Level Agenda  Intro, jitter vs. JITTER  Java in a low latency application world  The (historical) fundamental problems  What people have done to try to get around them  What if the fundamental problems were eliminated?  What 2015 looks like for Low Latency Java developers  Real World Case Studies Welcome to all Dallas Java Developers 5/3/20152
  3. 3. © Copyright Azul Systems 20155/3/20153
  4. 4. © Copyright Azul Systems 2015 Is “jitter” a proper word for this? 99%‘ile is ~60 usec Max is ~30,000% higher than “typical” Answer: no its not jitter at all. Its phase changes. 5/3/20154
  5. 5. © Copyright Azul Systems 2015 About Azul Systems Vega C4  We make scalable Virtual Machines  Have built “whatever it takes to get job done” since 2002  3 generations of custom SMP Multi-core HW (Vega)  Now Pure software for commodity x86 (Zing)  Certified OpenJDK (Zulu)  Known for Low Latency, Consistent execution, and Large data set excellence Zing, Zulu, and everything about Java Virtual Machines 5/3/20155
  6. 6. © Copyright Azul Systems 2015 Java in the low latency world 5/3/20156
  7. 7. © Copyright Azul Systems 2015 Java in a low latency world  Why do people use Java for low latency apps?  Are they crazy?  No. There are good, easy to articulate reasons  Projected lifetime cost  Developer productivity  Time-to-product, Time-to-market, ...  Leverage, ecosystem, ability to hire Yep, low latency Java is goin’ down for real… 5/3/20157
  8. 8. © Copyright Azul Systems 2015 e.g. customer answer to: “Why do you use Java in Algo Trading?”  Strategies have a shelf life  We have to keep developing and deploying new ones  Only one out of N is actually productive  Profitability therefore depends on ability to successfully deploy new strategies, and on the cost of doing so  Our developers seem to be able to produce 2x-3x as much when using a Java environment as they would with C++ ... 5/3/20158
  9. 9. © Copyright Azul Systems 2015 So what is the problem? Is Java Slow?  No  A good programmer will get roughly the same speed from both Java and C++  A bad programmer won’t get you fast code on either  The 50%‘ile and 90%‘ile are typically excellent...  It’s those pesky occasional stutters and stammers and stalls that are the problem...  Ever hear of Garbage Collection? 5/3/20159
  10. 10. © Copyright Azul Systems 2015 Java’s Achilles heel 5/3/201510
  11. 11. © Copyright Azul Systems 2015 Stop-The-World Garbage Collection: How bad is it?  Let’s ignore the bad multi-second pauses for now...  Low latency applications regularly experience “small”, “minor” GC events that range in the 10s of msec  Frequency directly related to allocation rate  In turn, directly related to throughput  So we have great 50%, 90%. Maybe even 99%  But 99.9%, 99.99%, Max, all “suck”  So bad that it affects risk, profitability, service expectations, etc. 5/3/201511
  12. 12. © Copyright Azul Systems 2015 STW-GC effects in a low latency application 99%‘ile is ~60 usec Max is ~30,000% higher than “typical” 5/3/201512
  13. 13. © Copyright Azul Systems 2015 One way to deal with Stop-The-World GC I cannot see it, so it cannot see me. 5/3/201513
  14. 14. © Copyright Azul Systems 2015 More Stop-The-World GC avoidance Time for a bigger rug. 5/3/201514
  15. 15. © Copyright Azul Systems 2015 What do actual low latency developers do about it?  They use “Java” instead of Java  They write “in the Java syntax”  They avoid allocation as much as possible  E.g. They build their own object pools for everything  They write all the code they use (no 3rd party libs)  They train developers for their local discipline  In short: They revert to many of the practices that hurt productivity. They lose out on much of Java. 5/3/201515
  16. 16. © Copyright Azul Systems 2015 Another way to cope: “Creative Language” “Guarantee a worst case of 5 msec, 99% of the time” Translation: “1% will be far worse than worst case” “Mostly” Concurrent, “Mostly” Incremental Translation: “Will at times exhibit long monolithic stop-the- world pauses” “Fairly Consistent” Translation: “Will sometimes show results well outside this range” “Typical pauses in the tens of milliseconds” Translation: “Some pauses are much longer than tens of milliseconds” Drawn from evil vendor marketing literature 5/3/201516
  17. 17. © Copyright Azul Systems 2015 What do low latency (Java) developers get for all their effort?  They still see pauses (usually ranging to tens of msec)  But they get fewer (as in less frequent) pauses  And they see fewer people able to do the job  And they have to write EVERYTHING themselves  And they get to debug malloc/free patterns again  And they can only use memory in certain ways  ...  Some call it “fun”... Others “duct tape engineering”... 5/3/201517
  18. 18. © Copyright Azul Systems 2015 There is a fundamental problem: Stop-The-World GC mechanisms are contradictory to the fundamental requirements of low latency & low jitter apps 5/3/201518
  19. 19. © Copyright Azul Systems 2015 Unsustainable Throughout Sustainable Throughput The throughput achieved while safely maintaining service levels 5/3/201519
  20. 20. © Copyright Azul Systems 2015 It’s an industry-wide problem 5/3/201520
  21. 21. © Copyright Azul Systems 2015 It was an industry-wide problem It’s 2015... Now we have Zing®. 5/3/201521
  22. 22. © Copyright Azul Systems 2015 The common GC behavior across ALL currently shipping (non-Zing) JVMs  ALL use a Monolithic Stop-the-world NewGen – “small” periodic pauses (small as in 10s of msec) – pauses more frequent with higher throughput or allocation rates  Development focus for ALL is on OldGen collectors – Focus is on trying to address the many-second pause problem – Usually by sweeping it farther and farther the rug – “Mostly X” (e.g. “mostly concurrent”) hides the fact that they refer only to the OldGen part of the collector – E.g. CMS, G1, Balanced.... all are OldGen-only efforts  ALL use a Fallback to Full Stop-the-world Collection – Used to recover when other mechanisms (inevitably) fail – Also hidden under the term “Mostly”... 5/3/201522
  23. 23. © Copyright Azul Systems 2015 At Azul, STW-GC was addressed head-on  We decided to focus on the right core problems – Scale & productivity being limited by responsiveness – Even “short” GC pauses are considered a problem  Responsiveness must be unlinked from key metrics: – Transaction Rate, Concurrent users, Data set size, etc. – Heap size, Live Set size, Allocation rate, Mutation rate – Responsiveness must be continually sustainable – Can’t ignore “rare but periodic” events  Eliminate ALL Stop-The-World Fallbacks – Any STW fallback is a real-world failure Trivia: Azul as a company founded predominantly around this one premise plaguing then Java servers 5/3/201523
  24. 24. © Copyright Azul Systems 2015 The Zing “C4” Collector Continuously Concurrent Compacting Collector  Concurrent, compacting old generation  Concurrent, compacting new generation  No stop-the-world fallback – Always compacts, and always does so concurrently 5/3/201524
  25. 25. © Copyright Azul Systems 2015 Benefits 5/3/201525
  26. 26. © Copyright Azul Systems 20155/3/201526 Stay Responsive Even when traffic patterns change without warning 7x Load Increase 30 minute span shows elevated load long after event, yet no pauses.
  27. 27. © Copyright Azul Systems 20155/3/201527 Handle Real World traffic patterns One second view of transactions. Not constant. Not random either. Bursty is normal. Red line shows where order pricing arrival rate would be if constant
  28. 28. © Copyright Azul Systems 20155/3/201528 Achieve Measureable Benefits  Zing helped LMAX tame GC-related latency outlier pauses – Highly-engineered system: 4ms every 30 seconds down to 1ms every 2 hours – Less well-tuned system: 50ms every 30 seconds down to 3ms every 15 minutes  No more unexpected/unwanted old-gen pauses caused by external behavior – CMS STW intra-day, generally ~500ms, gone – Removed source of backpressure on latency critical path. – Pre-Azul these would occur less predictably, but multiple times a week. From joint LMAX/Azul talk at QCon London, March 2015
  29. 29. © Copyright Azul Systems 2015 This is not “just Theory” jHiccup A tool that measures and reports (as your application is running) if your JVM is running all the time 5/3/201529
  30. 30. © Copyright Azul Systems 2015 Discontinuities in Java execution - Easy To Measure A telco App with a bit of a “problem” 5/3/201530 We call these “hiccups”
  31. 31. © Copyright Azul Systems 2015 Oracle HotSpot™ (pure newgen) Zing Low latency trading application 5/3/201531
  32. 32. © Copyright Azul Systems 2015 Oracle HotSpot (pure newgen) Zing Low latency trading application 5/3/201532
  33. 33. © Copyright Azul Systems 2015 Low latency - Drawn to scale Oracle HotSpot (pure newgen) Zing 5/3/201533
  34. 34. © Copyright Azul Systems 2015 It’s not just for Low Latency Just as easy to demonstrate for human- response-time apps 5/3/201534
  35. 35. © Copyright Azul Systems 2015 Portal Application, slow Ehcache “churn” Oracle HotSpot CMS, 1GB in an 8GB heap Zing, 1GB in an 8GB heap 5/3/201535
  36. 36. © Copyright Azul Systems 2015 Portal Application, slow Ehcache “churn” Oracle HotSpot CMS, 1GB in an 8GB heap Zing, 1GB in an 8GB heap 5/3/201536
  37. 37. © Copyright Azul Systems 2015 Portal Application - Drawn to scale Oracle HotSpot CMS, 1GB in an 8GB heap Zing, 1GB in an 8GB heap 5/3/201537
  38. 38. © Copyright Azul Systems 2015 A Recent E-Commerce Case Study 5/3/201538
  39. 39. © Copyright Azul Systems 2015 Cyber Monday comes earlier every year… General trends of real world e-commerce traffic 5/3/201539
  40. 40. © Copyright Azul Systems 2015 Human-Time Real World Latency Case  Web retail site faces spike loads every year over Thanksgiving through Cyber Monday.  Site latency suffers at peak viewing and buying times, discouraging shoppers and leaving abandoned carts.  Hard to predict height of surge, just know its big, far higher than regular traffic 362 other days of the year.  New features like gallery search (Solr/Lucene) added higher memory footprint, longer GC times.  Staff spent lots of effort tuning HotSpot. Specific e-tail customer based in Salt Lake City, Utah. 5/3/201540
  41. 41. © Copyright Azul Systems 2015 Real World Latency Results  Customer studied Azul, met at Strata, NYC  Discussion led to Zing as viable alternative  Customer ran pilot tests with positive results. Needed one Linux adjustment, otherwise same server gear.  POC on customer live system showed better than expected latency profiles.  No more GC tuning!  Experienced a stable and profitable Thanksgiving 2014 weekend. Timeframe was fall 2014. 5/3/201541
  42. 42. © Copyright Azul Systems 2015 Remind me how GC tuning sucks 5/3/201542
  43. 43. © Copyright Azul Systems 2015 Java GC tuning is “hard”…  Examples of actual command line GC tuning terms: Java -Xmx12g -XX:MaxPermSize=64M -XX:PermSize=32M -XX:MaxNewSize=2g -XX:NewSize=1g -XX:SurvivorRatio=128 -XX:+UseParNewGC -XX:+UseConcMarkSweepGC -XX:MaxTenuringThreshold=0 -XX:CMSInitiatingOccupancyFraction=60 -XX:+CMSParallelRemarkEnabled -XX:+UseCMSInitiatingOccupancyOnly -XX:ParallelGCThreads=12 -XX:LargePageSizeInBytes=256m … Java –Xms8g –Xmx8g –Xmn2g -XX:PermSize=64M -XX:MaxPermSize=256M -XX:-OmitStackTraceInFastThrow -XX:SurvivorRatio=2 -XX:-UseAdaptiveSizePolicy -XX:+UseConcMarkSweepGC -XX:+CMSConcurrentMTEnabled -XX:+CMSParallelRemarkEnabled -XX:+CMSParallelSurvivorRemarkEnabled -XX:CMSMaxAbortablePrecleanTime=10000 -XX:+UseCMSInitiatingOccupancyOnly -XX:CMSInitiatingOccupancyFraction=63 -XX:+UseParNewGC –Xnoclassgc … 5/3/201543
  44. 44. © Copyright Azul Systems 2015 A few GC tuning flags Source: Word Cloud created by Frank Pavageau in his Devoxx FR 2012 presentation titled “Death by Pauses” 5/3/201544
  45. 45. © Copyright Azul Systems 2015 Complete guide to Zing GC tuning java -Xmx40g 5/3/201545
  46. 46. © Copyright Azul Systems 2015 Any other problems beyond GC? 5/3/201546
  47. 47. © Copyright Azul Systems 2015 JVMs make many tradeoffs often trading speed vs. outliers  Some speed techniques come at extreme outlier costs – E.g. (“regular”) biased locking – E.g. counted loops optimizations  Deoptimization  Lock deflation  Weak References, Soft References, Finalizers  Time To Safe Point (TTSP) 5/3/201547
  48. 48. © Copyright Azul Systems 2015 Time To Safepoint: Your new #1 enemy  Many things in a JVM (still) use a global safepoint  All threads brought to a halt, at a “safe to analyze” point in code, and then released after work is done.  E.g. GC phase shifts, Deoptimization, Class unloading, Thread Dumps, Lock Deflation, etc. etc.  A single thread with a long time-to-safepoint path can cause an effective pause for all other threads. Consider this a variation on Amdahl’s law.  Many code paths in the JVM are long... Once GC itself was taken care of 5/3/201548
  49. 49. © Copyright Azul Systems 2015 Time To Safepoint (TTSP), the most common examples  Array copies and object clone()  Counted loops  Many other variants in the runtime...  Measure, Measure, Measure...  Zing has a built-in TTSP profiler  At Azul, the CTO walks around with a 0.5msec beat down stick... 5/3/201549
  50. 50. © Copyright Azul Systems 2015 OS related stuff  OS related hiccups tend to dominate once GC and TTSP are removed as issues.  Take scheduling pressure seriously (Duh?)  Hyper-threading (good? bad?)  Swapping (Duh!)  Power management  Transparent Huge Pages (THP).  ... Once GC and TTSP are taken care of 5/3/201550
  51. 51. © Copyright Azul Systems 2015 Takeaway: In 2015, “Real” Java is finally viable for low latency applications  GC is no longer a dominant issue, even for outliers  2-3 msec worst case with “easy” tuning  < 1 msec worst case is very doable  No need to code in special ways any more – You can finally use “real” Java for everything – You can finally 3rd party libraries without worries – You can finally use as much memory as you want – You can finally use regular (good) programmers 5/3/201551
  52. 52. © Copyright Azul Systems 2015 One-liner Takeaway: Zing: the cure for your Java hiccups 5/3/201552
  53. 53. © Copyright Azul Systems 2015 Compulsory Marketing Pitch 5/3/201553
  54. 54. © Copyright Azul Systems 2015 Azul Hot Topics 5/3/201554 Zing 15.05 imminent  1TB heap  ReadyNow!  JMX  Oracle Linux Zing for Cloud  Amazon AMIs  Rackspace OnMetal compat  Docker in R&D Zing for Big Data  Cloudera CDH5 cert  Cassandra paper  Spark is in Zing open source program Zulu®  Azure Gallery  JSE Embedded  8u45 in the chute
  55. 55. © Copyright Azul Systems 2015 Q&A and In Closing… Go get some Zing today! azul.com/trial At very least download JHiccup. azul.com/jhiccup Grab a Zing Free Trial card. Let’s talk about best BBQ in Texas azul.com 5/3/201555 @schuetzematt

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