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@nicolas_frankel
An Experiment in Continuous
Deployment of JVM applications
Nicolas Fränkel
@nicolas_frankel
Me, myself and I
§ Former developer, team lead, architect,
blah-blah
§ Developer Advocate
§ Lots of crazy creative ideas
@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.
@nicolas_frankel
Continuous Integration
§ Compilation
§ Unit testing
§ Packaging
§ Integration testing
§ Containerization
§ Storing in a registry
§ Etc.
@nicolas_frankel
Continous Stuff
§ Continuous Integration
• Should be the norm
§ Continuous Delivery
• Much harder
§ Continous Deployment
• No downtime
@nicolas_frankel
Traditional JVM deployment model
§ Stop the JVM
§ Overwrite JAR / WAR
§ Start the JVM again
@nicolas_frankel
Continuous Deployment approaches
Blue-Green Deployment
@nicolas_frankel
Continuous Deployment approaches
Kubernetes Rolling Updated
1.0 1.01.02.0 2.0 2.0
@nicolas_frankel
Change bytecode inside a JVM with no downtime?
@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
@nicolas_frankel
The Attach API
@nicolas_frankel
Java agents
§ JAR with specific meta-data
§ Part of Instrumentation API
• Change bytecode of running
applications
@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
@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
@nicolas_frankel
Instrumentation use-cases
§ Change existing class implementation
• With some limitations
§ Apply automatic transformation of
bytecode
@nicolas_frankel
Instrumentation API
@nicolas_frankel
Options to move bytecode around
§ Batching
§ Streaming
@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
@nicolas_frankel
Hazelcast Jet
§ Apache 2 Open Source
§ Single JAR
§ Unified batch/streaming API
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
@nicolas_frankel
Overall design
@nicolas_frankel
Overall flow
@nicolas_frankel
Time for a DEMO
@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”
@nicolas_frankel
Improvements
1. Stream from the CI pipeline
2. Add additional meta-data in each class
• e.g. from Git tag
@nicolas_frankel
Thanks for your attention!
§ https://blog.frankel.ch/
§ @nicolas_frankel
§ https://bit.ly/bytecode-streaming

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stackconf 2020 | Real Continuous Deployment of JVM applications by Nicolas Frankel