Mule Runtime: Performance Tuning
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Explore case studies from our most demanding deployments and provide a best practice approach to designing and tuning applications for optimal performance.
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Mule Runtime: Performance Tuning
- 1. All contents © MuleSoft Inc. Rupesh Ramachandran Roy Prins Performance Tuning
- 2. All contents © MuleSoft Inc. What we’ll cover today… • What does Mule runtime performance look like • Why does it perform so well • How do you tune for high performance • Testing Process • Quick Wins • Fine Tuning
- 3. All contents © MuleSoft Inc. Mule runtime performance • Real-world customer examples • Financial institution • ~100 million transactions per day • On-premise • Elastic scale • Large fast food chain • ~40 million transactions per day • CloudHub
- 4. All contents © MuleSoft Inc. Stability under Concurrency very important for API’s Performance characteristics 4
- 5. All contents © MuleSoft Inc. Batch jobs and bulk load still relevant Performance characteristics 5
- 6. All contents © MuleSoft Inc. Scalability 6
- 7. All contents © MuleSoft Inc. 7
- 8. All contents © MuleSoft Inc. Why does it perform this well (technology) 8 • Non-blocking architecture • Plus many others • Streaming • Log4j2 • Optimized xpath, jms, jetty • kryo serialize • etc
- 9. All contents © MuleSoft Inc. Why does it perform this well (people, process) World class performance engineering team • Regression tests for every release • Realistic customer projects and production traffic • Profile and optimize new capabilities • Pass on lessons learnt • Performance Tuning Guide • Optimized defaults for tunables
- 10. All contents © MuleSoft Inc. Tuning Mule Runtime
- 11. All contents © MuleSoft Inc. Define Performance Goals •Memory Footprint • Large payload steady state • Less important on 64-bit • Prevent swapping •Startup Time • Somewhat important (e.g. production) • Microservices, containerization requiring elastic scale up/down •Throughput • Transactions processed over time (e.g. TPS) • Almost always Important •Responsiveness • Latency, round trip time, user wait time, etc • Very Important for API’s • Common mistake: using single message latency as indication of machine performance, does not account for concurrency overheads You can only pick two!! Minimize Memory Footprint Minimize Latency Minimize Overhead
- 12. All contents © MuleSoft Inc. Performance Tuning Process •Repeatability - use a Controlled Environment •High performance test driver •External dependencies •Mock if susceptible to outside constraints •Keep CPU utilization under 75% •Find the inflection point •Tune one parameter at a time
- 13. All contents © MuleSoft Inc.Performance Tunables
- 14. All contents © MuleSoft Inc. Mule Performance Tunables - Overview •Mule Design time tuning •Mule flows •Mule configuration settings •Mule Runtime tuning •JVM •GC •Infrastructure tuning •OS •Network
- 15. All contents © MuleSoft Inc. Longevity Tests Scalability Tests Tune test infra Tune backend Mule flow/config JVM/GC Iterative Test, Monitor, Profile Final Report Performance Test Lifecycle Take baseline Tune test infra and tools Functional Tests Design Mule Flows
- 16. All contents © MuleSoft Inc. Quick Wins
- 17. All contents © MuleSoft Inc. 80/20 Rule •Runtime tuning –JVM • Xmx, Xms –GC • CMS for API’s –If large machines (cores/RAM), consider more than 1 Mule per machine •Infrastructure tuning –ulimit –n –File descriptors for high socket connections wrapper.java.additional.16=-XX:NewSize=1536m wrapper.java.additional.17=-XX:MaxNewSize=1536m wrapper.java.additional.20=-Xms=2048m wrapper.java.additional.21=-Xmx=2048m # GC Tuning wrapper.java.additional.22=-XX:+UseConcMarkSweepGC wrapper.java.additional.23=-XX:CMSInitiatingOccupancyFraction=65 wrapper.java.additional.xz=-XX:UseCMSInitiatingOccupancyOnly
- 18. All contents © MuleSoft Inc. Fine tuning Mule Runtime
- 19. All contents © MuleSoft Inc. Design time tuning – Mule Flows •Session Variables • Fewer • Smaller •Payload Formats • Java objects fastest •Data Extraction • MEL preferred to scripting languages •Message Transformers • DataWeave preferred option • XSLT • Java
- 20. All contents © MuleSoft Inc. Design time tuning – Mule Flows •Integration Patterns and performance implications • Scatter-Gather –Two or more independent data operations with a single source –Results of the operations are combined • Async scope • Cache scope • Transactional scope (cost) • Stateful components (resequencer, idempotent, etc) • Batch Module for bulk loads
- 21. All contents © MuleSoft Inc. Design time tuning – Mule configurations •HTTP Connections • Don’t use Jetty connector • Use latest runtime for best NIO performance • NIO acceptor threads handoff to worker threads • Acceptor threads default is #cores. Works well • For worker threads tuning, adjust MaxThreadsActive on HTTP listener (default 128) •HTTP Keep-alive • For HTTP outbound, on by default • Test driver or HTTP clients should use the same •HTTPS • Use latest TLS 1.1
- 22. All contents © MuleSoft Inc. Design time tuning – Mule configurations •Messaging • Producer Mule flows • JMS Connection Pooling (sessionCacheSize param) • Consumer Mule flows • JMS – ‘numberOfConsumers’ • AMQP – ‘numberOfChannels’ • JMS Server • Disable message persistence if not needed • Durable subscriber (cost) • Message filtering (cost) • VM Endpoints • Flow references instead of VM endpoints within same app • Unless HA save point required functionally
- 23. All contents © MuleSoft Inc. Design time tuning – Mule configurations •Threading • Threading Profiles • Synchronous vs Asynchronuos processing • Thread Pools •Logging • Async logger vs sync
- 24. All contents © MuleSoft Inc. Runtime tuning – Mind-map of 80/20 rule for GC tuning 24 Oracle Hotspot (Deprecated in JDK 8. Use MetaSpaceSize instead to bound native space use)
- 25. All contents © MuleSoft Inc. 25
- 26. All contents © MuleSoft Inc. Thank you!
- 27. All contents © MuleSoft Inc.