stackconf 2020 | Real Continuous Deployment of JVM applications by Nicolas Frankel
A couple of years ago, continuous integration in the JVM ecosystem meant Jenkins. Since that time, a lot of other tools have been made available. But new tools don’t mean new features, just new ways. Beside that, what about continuous deployment? There’s no tool that allows to deploy new versions of a JVM-based application without downtime. The only way to achieve zero downtime is to have multiple nodes deployed on a platform, and let that platform achieve that _e.g._ Kubernetes. And yet, achieving true continuous deployment of bytecode on one single JVM instance is possible if one changes one’s way of looking at things. What if compilation could be seen as changes? What if those changes could be stored in a data store, and a listener on this data store could stream those changes to the running production JVM via the Attach API? In that talk, I’ll demo exactly that using Hazelcast and Hazelcast Jet – but it’s possible to re-use the principles that will be shown using other streaming technologies.
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stackconf 2020 | Real Continuous Deployment of JVM applications by Nicolas Frankel
- 1. @nicolas_frankel An Experiment in Continuous Deployment of JVM applications Nicolas Fränkel
- 2. @nicolas_frankel Me, myself and I § Former developer, team lead, architect, blah-blah § Developer Advocate § Lots of crazy creative ideas
- 3. @nicolas_frankel Hazelcast HAZELCAST IMDG is an operational, in- memory, distributed computing platform that manages data using in-memory storage and performs parallel execution for breakthrough application speed and scale. HAZELCAST JET is the ultra fast, application embeddable, 3rd generation stream processing engine for low latency batch and stream processing.
- 4. @nicolas_frankel Continuous Integration § Compilation § Unit testing § Packaging § Integration testing § Containerization § Storing in a registry § Etc.
- 5. @nicolas_frankel Continous Stuff § Continuous Integration • Should be the norm § Continuous Delivery • Much harder § Continous Deployment • No downtime
- 6. @nicolas_frankel Traditional JVM deployment model § Stop the JVM § Overwrite JAR / WAR § Start the JVM again
- 8. @nicolas_frankel Continuous Deployment approaches Kubernetes Rolling Updated 1.0 1.01.02.0 2.0 2.0
- 9. @nicolas_frankel Change bytecode inside a JVM with no downtime?
- 10. @nicolas_frankel The Attach API § Allows to dynamically load an agent inside another running JVM • No need to run in debug § Requirements: 1. Need to run on the same system 2. Must know the PID of the JVM
- 12. @nicolas_frankel Java agents § JAR with specific meta-data § Part of Instrumentation API • Change bytecode of running applications
- 13. @nicolas_frankel Types of agent § Static • Set on at startup • java -jar -javaagent:/path/to/agent.jar my.jar § Dynamic • Loaded by the Attach API
- 14. @nicolas_frankel Agent configuration Static Dynamic Entry- point public static void premain( String agentArgs, Instrumentation inst) public static void agentmain( String agentArgs, Instrumentation inst) Manifest entry Premain-Class Agent-Class
- 15. @nicolas_frankel Instrumentation use-cases § Change existing class implementation • With some limitations § Apply automatic transformation of bytecode
- 17. @nicolas_frankel Options to move bytecode around § Batching § Streaming
- 18. @nicolas_frankel In-memory stream processing engines § Cloud • Amazon Kinesis • IBM Streams § On-premise • Apache Flink • Hazelcast Jet • Apache Beam ⁃ Abstraction over some of the above
- 19. @nicolas_frankel Hazelcast Jet § Apache 2 Open Source § Single JAR § Unified batch/streaming API
- 20. Hazelcast In-Memory Computing Platform Ingest Combine Stream Compute Transform Publish In-Memory Operational Storage Join, Enrich, Group, Aggregate Windowing, Event Time Processing Distributed and Parallel Computations Filter, Clean, Convert In-Memory Subscriber Notifications Databases Kafka IoT Custom Connector Enterprise Applications Hazelcast IMDG File Watcher Socket Database Events Kafka Alerts Enterprise Applications Interactive Analytics Hazelcast IMDG, Maps, Caches, Lists Databases HDFS File
- 23. @nicolas_frankel Time for a DEMO
- 24. @nicolas_frankel Limitations of the demo § No tagging § No loading of new classes § redefineClasses() “must not add, remove or rename fields or methods, change the signatures of methods, or change inheritance”
- 25. @nicolas_frankel Improvements 1. Stream from the CI pipeline 2. Add additional meta-data in each class • e.g. from Git tag
- 26. @nicolas_frankel Thanks for your attention! § https://blog.frankel.ch/ § @nicolas_frankel § https://bit.ly/bytecode-streaming