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. Besides that, what about continuous deployment? There’s no tool that allows deploying 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 the 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 this talk, we'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.

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
execution for breakthrough
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

7. @nicolas_frankel
Continuous Deployment approaches
Blue-Green Deployment

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
Plan
1. The Instrumentation API
2. The Attach API
3. Binding everything together with
streaming
4. Demo!

11. @nicolas_frankel
Java agents
 JAR with specific meta-data
 Part of Instrumentation API
• Change bytecode of running
applications

12. @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

13. @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

14. @nicolas_frankel
Instrumentation use-cases
 Change existing class implementation
• With some limitations
 Apply automatic transformation of
bytecode

15. @nicolas_frankel
Instrumentation API

16. @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

17. @nicolas_frankel
The Attach API

18. @nicolas_frankel
Options to move bytecode around
 Batching
 Streaming

19. @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

20. @nicolas_frankel
Hazelcast Jet
 Apache 2 Open Source
 Single JAR
 Unified batch/streaming API

21. 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

22. @nicolas_frankel
Overall design

23. @nicolas_frankel
Overall flow

24. @nicolas_frankel

25. @nicolas_frankel
Limitations of the demo
 No version tagging
 No loading of new classes
 redefineClasses() “must not add,
remove or rename fields or methods,
change the signatures of methods, or
change inheritance”

26. @nicolas_frankel
Improvements
1. Stream from the CI pipeline
2. Add additional meta-data in each class
• e.g. from Git tag
3. Implement one’s custom classloader à
la Spring DevTools

27. @nicolas_frankel
Thanks for your attention!
 https://blog.frankel.ch/
 @nicolas_frankel
 https://bit.ly/bytecode-streaming