README.md

Containerize a Kafka Streams application with Jib

Run ./mvnw clean package to build your container.

Tutorial

The following tutorial for using Jib alongside Kafka will require installation of docker-compose, and uses the Bitnami Kafka+Zookeeper images, however any other Kafka or ZooKeeper Docker images should work.

Without Docker

If not using Docker, Kafka and ZooKeeper can be started locally using their respective start scripts. If this is done, though, the the variables for the bootstrap servers will need to be adjusted accordingly.

The following steps can be used to run this application locally outside of Docker after creating the input and output topic, and producing lipsum.txt into the input topic (copy the executed commands from below, and switch the real addresses of Kafka for localhost).

export BOOTSTRAP_SERVERS=localhost:9092  # Assumes Kafka default port
export STREAMS_INPUT=streams-plaintext-input
export STREAMS_OUTPUT=streams-plaintext-output
export AUTO_OFFSET_RESET=earliest
./mvnw clean exec:java

Start Kafka Cluster in Docker

Note: Sometimes the Kafka container kills itself in below steps, and the consumer commands therefore may need to be re-executed. The Streams Application should reconnect on its own.

For this exercise, we will be using three separate termainal windows, so go ahead and open those.

First, we start with getting our cluster running in the foreground. This starts Kafka listening on 29092 on the host, and 9092 within the Docker network. Zookeeper is available on 2181.

Terminal 1

docker-compose up zookeeper kafka

Create Kafka Topics

We need to create the topics where data will be produced into.

Terminal 2

docker-compose exec kafka \
    bash -c "kafka-topics.sh --create --if-not-exists --zookeeper zookeeper:2181 --topic streams-plaintext-input --partitions=1 --replication-factor=1"

docker-compose exec kafka \
    bash -c "kafka-topics.sh --create --if-not-exists --zookeeper zookeeper:2181 --topic streams-plaintext-output --partitions=1 --replication-factor=1"

Verify topics exist

docker-compose exec kafka \
    bash -c "kafka-topics.sh --list --zookeeper zookeeper:2181"

Produce Lorem Ipsum into input topic

Terminal 2

docker-compose exec kafka \
    bash -c "cat /data/lipsum.txt | kafka-console-producer.sh --topic streams-plaintext-input --broker-list kafka:9092"

Verify that data is there (note: hard-coding max-messages to the number of lines of expected text)

docker-compose exec kafka \
    bash -c "kafka-console-consumer.sh --topic streams-plaintext-input --bootstrap-server kafka:9092 --from-beginning --max-messages=9"

Start Console Consumer for output topic

This command will seem to hang, but since there is no data yet, this is expected.

Terminal 2

docker-compose exec kafka \
    bash -c "kafka-console-consumer.sh --topic streams-plaintext-output --bootstrap-server kafka:9092 --from-beginning"

Start Kafka Streams Application

Now, we can start our application to read from the beginning of the input topic that had data sent into it, and begin processing it.

Terminal 3

docker-compose up kafka-streams

You should begin to see output in Terminal 2

Ctrl+C on terminal 2 after successful output and should see Processed a total of 509 messages if all words produced and consumed exactly once.

Extra

Redo the tutorial with more input data and partitions, then play with docker-compose scale to add more Kafka Streams process in parallel.

Profiles

The exec:java goal can be rerun or container rebuilt to perform completely different Kafka Streams pipelines using Maven Profiles.

To rebuild the container, for example, run mvn -Pprofile clean package

Profile	Container Name	Main Class	Description
upper (default)	kafka-streams-jib-example	ToUpperCaseProcessor	Takes text from input topic and and uppercases all the words to an output topic. (The example provided above.)
consumer-offsets	kafka-streams-jib-example-2	ConsumerOffsetsToJSONProcessor	Translates the __consumer_offsets topic in binary format out to JSON. It is a Java port of this repo.

Disclaimer

The examples given are mostly intended to show stateless processing. If doing stateful operations within Kafka Streams that maintain data on disk (e.g. in RocksDB), then you'll want a persistent storage layer for the application. This is not provided with Docker out of the box.

If running the apps multiple times and expecting similar results, then you'll need to first use the Kafka Streams Application Reset Tool after first stopping the Docker/Streams instance(s).

Cleanup environment

docker-compose rm -sf
# Clean up mounted docker volumes
docker volume ls | grep $(basename `pwd`) | awk '{print $2}' | xargs docker volume rm 
# Clean up networks
docker network ls | grep $(basename `pwd`) | awk '{print $2}' | xargs docker network rm

More information

Learn more about Jib.

Learn more about Apache Kafka & Kafka Streams.