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Choosing Right Garbage Collector to Increase Efficiency of Java Memory Usage

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With microservices, cloud hosting, and vertical scaling in mind, we'll compare the top Java garbage collectors to see how efficiently they handle memory resources. Being configured smartly, Java can be cost-effective for all ranges of projects — from cloud-native startups to legacy enterprise applications. And the selected garbage collection algorithm is one of the main foundational bricks here, as its settings can influence the whole project. In this presentation, we share our experiences in tuning RAM usage in a Java process to make it more elastic and gain the benefits of faster scaling and lower total cost of ownership (TCO). The provided results of testing G1, Parallel, ConcMarkSweep, Serial, Shenandoah, ZGC, C4 and OpenJ9 garabage collectors while scaling Java applications vertically will help you to make the right choice for own projects.

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Choosing Right Garbage Collector to Increase Efficiency of Java Memory Usage

  1. 1. Choosing Right Garbage Collector for Efficient Java Memory Usage Ruslan Synytsky
  2. 2. Agenda ● Java Memory Usage Problems ● JDK Improvements for Elastic Java Memory Scaling ● Garbage Collection Testing Results
  3. 3. Heap Vertical Scaling Unreleased Heap Memory
  4. 4. OOM Error and OOM Killer ● OutOfMemoryError exception is usually thrown when there is insufficient space to allocate an object in the Java heap or insufficient native memory to support the loading of a Java class ● oom_kill is a job that helps to sacrifice one or more processes in order to free up memory for the system
  5. 5. Over-Allocation and Underutilization
  6. 6. Java Memory Consumption Problems The most widely acknowledged issue with Java EE is large memory requirements (40%), then slow startup times (40%), followed by missing technologies and specifications (20%) Jakarta EE Developer Survey 2018
  7. 7. Pay-Per-Use Billing Model Using automatic vertical scaling, cloud providers can offer economically advantageous pricing based on the real resource consumption Forbes - Deceptive Cloud Efficiency: Do You Really Pay As You Use?
  8. 8. Too Many Points to Consider
  9. 9. Understanding of the OutOfMemoryError Exception ● java.lang.OutOfMemoryError: Java heap space ● java.lang.OutOfMemoryError: GC Overhead limit exceeded ● java.lang.OutOfMemoryError: Requested array size exceeds VM limit ● java.lang.OutOfMemoryError: Metaspace ● java.lang.OutOfMemoryError: request size bytes for reason. Out of swap space? ● java.lang.OutOfMemoryError: Compressed class space ● java.lang.OutOfMemoryError: reason stack_trace_with_native_method https://docs.oracle.com/javase/8/docs/technotes/guides/troubleshoot/memleaks002.html
  10. 10. Understanding of the OutOfMemoryError Exception
  11. 11. OutOfMemoryError Exception
  12. 12. OOM Killer
  13. 13. OOM Killer
  14. 14. Runtime Environments ● Application Containers ● System Containers ● Virtual Machines
  15. 15. Heap Vertical Scaling Unreleased Heap Memory
  16. 16. Calling Full GC Periodically (Before OpenJDK12) https://github.com/jelastic-jps/java-memory-agent Compacting GC cycles are not triggered automatically and must be executed explicitly Workaround: inject an agent which monitors the memory usage and calls System.gc() periodically: -javaagent:jelastic-gc-agent.jar=period=300,debug=true
  17. 17. G1 and Full GC java -XX:+UseG1GC -Xmx2g -Xms32m -jar app.jar 0 Memory grew from 32 MB to 1 GB in 25 seconds https://github.com/jelastic/java-vertical-scaling-test
  18. 18. Timely Reduce Unused Committed Memory (JEP 346) Make the G1 garbage collector automatically give back Java heap memory to the operating system when idle ● G1PeriodicGCInterval ● G1PeriodicGCSystemLoadThreshold ● G1PeriodicGCInvokesConcurrent JEP 346: Promptly Return Unused Committed Memory from G1 java -Xms32M -Xmx2g -XX:+UseG1GC -XX:G1PeriodicGCSystemLoadThreshold=0.6 -XX:G1PeriodicGCInterval=900k -jar app.jar
  19. 19. Improved Elasticity Automatically Released Heap
  20. 20. Community Recognition Special Appreciation to Rodrigo Bruno Senior/Postdoc Researcher at the Systems Group in ETH Zurich. PhD in Técnico (University of Lisbon)
  21. 21. Running GC Tests in Jelastic
  22. 22. Load Testing Logic https://github.com/jelastic/java-vertical-scaling-test/blob/ma ster/src/com/jelastic/verticalscaling/Load.java#L50 java [OPTIONS] -jar app.jar <sleep> <mode> where sleep - 10 mode - 1
  23. 23. Auto Testing Package https://github.com/jelastic/java-vertical-scaling-test/blob/master/manifest.yml
  24. 24. G1 Collector (-XX:+UseG1GC) The Garbage-First (G1) is a server-style Garbage Collector for multiprocessor machines with a large amount of memory. The heap is partitioned into fixed-sized regions and G1 tracks the live data in those regions. When Garbage Collection is required, it collects from the regions with less live data first. ● 2004, Sun Microsystems JEP 346: Promptly Return Unused Committed Memory from G1
  25. 25. G1 -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UseG1GC -XX:G1PeriodicGCInterval=1k
  26. 26. G1 and G1PeriodicGCSystemLoadThreshold https://github.com/lxc/lxcfs/ Using LXCFS to Improve Container Resource Visibility Threshold for the current system load as returned by the hosts getloadavg() call to determine whether a periodic garbage collection should be triggered: ● a current system load higher than the tigger value prevents periodic garbage collections ● zero value indicates that this threshold check is disabled If running in Docker container then use
  27. 27. Shenandoah GC (-XX:+UseShenandoahGC) Shenandoah GC is a concurrent garbage collector for the JVM. GC tries to perform most of the activities in parallel without interrupting application performance. Such parallelism makes “stop-the-world” (STW) pauses extremely short. Another inherent advantage is an efficient work with small and large heaps with no impact on STW pauses’ length. ● 2014, Christine H. Flood, Red Hat https://wiki.openjdk.java.net/display/shenandoah/Main#Main-Heuristics
  28. 28. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UnlockExperimentalVMOptions -XX:+UseShenandoahGC -XX:ShenandoahGCHeuristics=compact Shenandoah
  29. 29. G1 vs Shenandoah - CPU Usage G1 Shenandoah
  30. 30. ZGC (-XX:+UseZGC) ZGC is low latency scalable garbage collector. Designed for use with applications that require a large heap and low latency. It uses a bunch of one generation and performs most (but not all) garbage collection in parallel with uninterrupted application work. This greatly limits the impact of garbage collection on your application response time. ● 2018, Per Liden, Oracle JEP 351: ZGC: Uncommit Unused Memory - available from JDK 13 Release
  31. 31. -Xmx3g -Xms32m -XX:+UnlockExperimentalVMOptions -XX:+UseZGC -XX:ZUncommitDelay=1 -XX:ZCollectionInterval=30 ZGC @ Oracle OpenJDK
  32. 32. OpenJ9 OpenJ9 uses the Generational Concurrent (-Xgcpolicy:gencon) policy by default, which is best suited to transactional applications that have many short lived objects. Alternative policies are available, including those that cater for applications with large Java heaps (-Xgcpolicy:balanced), applications that are sensitive to response-time (-Xgcpolicy:metronome), or applications that require high application throughput (-Xgcpolicy:optthruput). ● 2017, Eclipse Foundation
  33. 33. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+IdleTuningCompactOnIdle -XX:+IdleTuningGcOnIdle -XX:IdleTuningMinIdleWaitTime=1 -Xjit:waitTimeToEnterDeepIdleMode=1000 Bash command to check the real usage while true do pid=$(pgrep -f java | tail -n1) used=$(ps -orss --no-headers --pid $pid) echo "scale=2 ; $used / 1024/1024" | bc sleep 1 done Inconsistent behaviour with -XX:+IdleTuningGcOnIdle, mem not released back to OS on Idle OpenJ9
  34. 34. C4 GC ● 2010, Gil Tene, Azul Systems The C4 (Continuously Concurrent Compacting Collector) is an updated generational form of the Azul Pauseless GC Algorithm and is the default collector of Zing®. C4 differentiates itself from other generational garbage collectors by supporting simultaneous – generational concurrency: the different generations are collected using concurrent (non-stop-the-world) mechanisms that can be simultaneously and independently active. Unlike other algorithms, it is not ‘mostly’ concurrent, but fully concurrent, so it never falls back to a stop-the-world compaction.
  35. 35. -Xmx500m -Xms32m -XX:+UseZST C4 @ Zing
  36. 36. -Xmx500m -Xms32m -XX:+UseZST C4 @ Zing
  37. 37. ConcMarkSweep GC (-XX:+UseConcMarkSweepGC) ConcMarkSweep GC collector is designed for applications that prefer shorter garbage collection pauses and which can afford to share processor resources with the garbage collector while the application is running. It makes sense to use such a collector when applications requirements for time garbage collection pauses are low. ● 2004, Sun Microsystems
  38. 38. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UseConcMarkSweepGC + periodical jcmd <pid> GC.run ConcMarkSweep
  39. 39. Serial GC (-XX:+UseSerialGC) Serial GC performs garbage collection in a single thread and has the lowest consumption of memory among all GC types but, at the same time, it makes long pauses that can lead to application performance degradation. ● 2004, Sun Microsystems
  40. 40. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UseSerialGC + periodical jcmd <pid> GC.run Serial
  41. 41. Epsilon GC (-XX:+UseEpsilonGC) Epsilon GC is a passive GC that handles memory allocation and doesn’t clear it when objects are no longer used. When your application exhausts the Java heap, the JVM goes down. So, EpsilonGC prolongs an application life until the memory will run out and dumps the memory, that can be useful for application memory usage debugging, as well as measuring and managing application performance. ● 2014, Aleksey Shipilev, Red Hat
  42. 42. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UnlockExperimentalVMOptions -XX:+UseEpsilonGC Epsilon
  43. 43. Parallel GC (-XX:+UseParallelGC) Parallel GC is a “stop-the-world” multithreaded Garbage Collector similar to the serial collector. The primary difference is that multiple threads are used to speed up garbage collection. By default, both minor and major collections are executed in parallel to further reduce garbage collection costs. ● 2000, Sun Microsystems
  44. 44. -Xmx3g -Xms32m -XX:+UseCompressedOops -XX:+UseParallelGC + periodical jcmd <pid> GC.run Parallel
  45. 45. Running GC Tests in Kubernetes
  46. 46. Auto Testing Package for Kubernetes https://github.com/jelastic/java-vertical-scaling-test/blob/master/manifest-k8s.yaml
  47. 47. Load Testing Logic java [OPTIONS] -jar app.jar <sleep> <mode> where sleep - 100 mode - 2 https://github.com/jelastic/java-vertical-scaling-test/blob/master/ src/com/jelastic/verticalscaling/Load.java#L64
  48. 48. G1 in Kubernetes
  49. 49. Shenandoah in Kubernetes
  50. 50. ZGC @ Oracle OpenJDK in Kubernetes
  51. 51. OpenJ9 in Kubernetes
  52. 52. C4 @ Zing in Kubernetes
  53. 53. Joint Comparison - Several Load Cycles RAM CPU
  54. 54. Resizing Xmx On the Fly
  55. 55. Heap Resizing Restart for Xmx Resize
  56. 56. -XX:SoftMaxHeapSize @ ZGC SoftMaxHeapSize is set for the GC to strive not to grow heap size beyond the specified size unless it is highly needed: ● to keep the heap footprint down, while maintaining the capability to deal with a temporary increase in heap space requirement ● with lots of margin, to increase confidence that you will not run into an allocation stall because of an unforeseen increase in allocation rate
  57. 57. JEP draft: Dynamic Max Memory Limit Xmx can be set higher than the container max memory limit (Cmx). And both Smx and Cmx can be adjusted on the fly without the need to restart JVM or container. At the moment the heap size can go beyond SoftMaxHeapSize (Smx) and there is no guarantee on how much the heap will grow other than up to Xmx. The problem arises when Smx < Cmx < Used Heap < Xmx: the JVM will be killed by the OS OOM Killer as it exceeds the amount of memory given to the container. We suggest to provide an option for making SoftMaxHeapSize as the hard limit, so when overshoot happens JVM will throw OOM Error which is not as bad OOM Kill. Dynamic Max Memory Limit @ G1
  58. 58. -Xsoftmx @ OpenJ9 https://www.ibm.com/support/knowledgecenter/en/SSYKE2_8.0.0/openj9/xsoftmx/index.html Runtime adjustable heap size (-Xsoftmx) allows to adjust heap size dynamically and take advantage of hot-add of memory. You can set this option on the command line, then modify it at run time by using the com.ibm.lang.management.MemoryMXBean.setMaxHeapSize(). This option can be useful in virtualized or cloud environments, for example, where the available memory might change dynamically to meet business needs. By default, -Xsoftmx is set to the same value as -Xmx.
  59. 59. C4 is fully elastic and can return all empty pages to the OS after each GC cycle. However, C4 sticks to the Xmx it was given, and avoid doing heavy elastic memory dance, since relinquishing memory mappings and reestablishing them on Linux kernels is bandwidth-limited in practice by the rate of page mapping invalidation the kernel can handle. C4 goes above Xmx rather than go between Xms and Xmx. JavaMemMax option controls the true maximum. In the future it will allow both scenarios where above-Xmx is allowed and where above-Xmx is prohibited. Two modes: ● Contingency (default mode) - goes above Xmx if it absolutely has to and will work hard to collect and stay below Xmx. ● Insurance (best effort elasticity) - borrows available memory and goes above Xmx in order to delay GC whenever possible. JavaMemMax @ С4 + ZST (Zing System Tools)
  60. 60. Keep Only Best Java Memories Learn More Get In Touch @siruslan rs@jelastic.com

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