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kafka-streams-quickstart

README.md

Quarkus Kafka Streams Quickstart

This project illustrates how you can build Apache Kafka Streams applications using Quarkus.

Anatomy

This quickstart is made up of the following parts:

  • Apache Kafka and ZooKeeper
  • producer, a Quarkus application that publishes some test data on two Kafka topics: "weather-stations" and "temperature-values"
  • aggregator, a Quarkus application processing the two topics, using the Kafka Streams API

The aggregator application is the interesting piece; it

  • runs a KStreams pipeline, that joins the two topics (on the weather station id), groups the values by weather station and emits the minimum/maximum temperature value per station to the "temperatures-aggregated" topic
  • exposes an HTTP endpoint for getting the current minimum/maximum values for a given station using Kafka Streams interactive queries.

Building

To build the producer and aggregator applications, run

mvn clean install

Running

A Docker Compose file is provided for running all the components. Start all containers by running

docker-compose up -d --build

Now run an instance of the debezium/tooling image which comes with several useful tools such as kafkacat and httpie:

docker run --tty --rm -i --network ks debezium/tooling:1.1

In the tooling container, run kafkacat to examine the results of the streaming pipeline:

kafkacat -b kafka:9092 -C -o beginning -q -t temperatures-aggregated

You also can obtain the current aggregated state for a given weather station using httpie, which will invoke an Kafka Streams interactive query for that value:

http aggregator:8080/weather-stations/data/1

Scaling

Kafka Streams pipelines can be scaled out, i.e. the load can be distributed amongst multiple application instances running the same pipeline. To try this out, scale the aggregator service to three nodes:

docker-compose up -d --scale aggregator=3

This will spin up two more instances of this service. The state store that materializes the current state of the streaming pipeline (which we queried before using the interactive query), is now distributed amongst the three nodes. I.e. when invoking the REST API on any of the three instances, it might either be that the aggregation for the requested weather station id is stored locally on the node receiving the query, or it could be stored on one of the other two nodes.

As the load balancer of Docker Compose will distribute requests to the aggregator service in a round-robin fashion, we'll invoke the actual nodes directly. The application exposes information about all the host names via REST:

http aggregator:8080/weather-stations/meta-data

Retrieve the data from one of the three hosts shown in the response (your actual host names will differ):

http cf143d359acc:8080/weather-stations/data/1

If that node holds the data for key "1", you'll get a response like this:

HTTP/1.1 200 OK
Connection: keep-alive
Content-Length: 74
Content-Type: application/json
Date: Tue, 11 Jun 2019 19:16:31 GMT

{
    "avg": 15.7,
    "count": 11,
    "max": 31.0,
    "min": 3.3,
    "stationId": 1,
    "stationName": "Hamburg"
}

Otherwise, the service will send a redirect:

HTTP/1.1 303 See Other
Connection: keep-alive
Content-Length: 0
Date: Tue, 11 Jun 2019 19:17:51 GMT
Location: http://72064bb97be9:8080/weather-stations/data/2

You can have httpie automatically follow the redirect by passing the --follow option:

http --follow cf143d359acc:8080/weather-stations/data/2

Running in native

To run the producer and aggregator applications as native binaries via GraalVM, first run the Maven builds using the native profile:

mvn clean install -Pnative -Dnative-image.container-runtime=docker

Then create an environment variable named QUARKUS_MODE and with value set to "native":

export QUARKUS_MODE=native

Now start Docker Compose as described above.

Running locally

For development purposes it can be handy to run the producer and aggregator applications directly on your local machine instead of via Docker. For that purpose, a separate Docker Compose file is provided which just starts Apache Kafka and ZooKeeper, docker-compose-local.yaml configured to be accessible from your host system. Open this file an editor and change the value of the KAFKA_ADVERTISED_LISTENERS variable so it contains your host machine's name or ip address. Then run:

docker-compose -f docker-compose-local.yaml up

mvn quarkus:dev -f producer/pom.xml

mvn quarkus:dev -Dquarkus.http.port=8081 -f aggregator/pom.xml

Any changes done to the aggregator application will be picked up instantly, and a reload of the stream processing application will be triggered upon the next Kafka message to be processed.