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Docker kafka-producer Image Size Docker kafka-zookeeper Image Size

kafka-producer-demo

Containerized Kafka producer endlessly producing data for testing and demoing of consumer apps. The project consists on:

  • A python script using kafka-python to endlessly produce data
  • A modified Docker image already prepared with a topic, a producer and ready to produce data on launch

Docker images available:

Running the demo

  1. Tweak producer.py to suit your needs (it is programmed to produce data that serves my use case) or pull ulitol97/kafka-producer to use as is
  2. Build a Docker image using the project's Dockerfile
  3. Run your image in a new container: docker run --name kafka-producer -d -p 2181:2181 -p 9092:9092 --env TOPIC_NAME=my-topic-for-tests ulitol97/kafka-producer:dev
  4. Enjoy an endless Kafka stream in the configured host and port

Tweaking the image

The following environment variables can be used to modify app containers:

  • TOPIC_NAME: the name of the topic that will be constantly streaming messages from the container (default is test-topic)
  • TIME_BETWEEN_MESSAGES: the number of milliseconds for the producer to wait between messages (default is 5000)

Additionally, the following can be used to change Kafka's base behaviour (see them here):

  • ADVERTISED_HOST: the external ip for the container (default is localhost)
  • ZK_CHROOT: the zookeeper chroot that's used by Kafka (without / prefix), e.g. "kafka"
  • LOG_RETENTION_HOURS: the minimum age of a log file in hours to be eligible for deletion
  • LOG_RETENTION_MINUTES: the minimum age of a log file in minutes to be eligible for deletion (supersedes LOG_RETENTION_HOURS if defined) (default is 15)
  • LOG_RETENTION_BYTES: configure the size at which segments are pruned from the log (default is 20971520, for 20MB)
  • NUM_PARTITIONS: configure the default number of log partitions per topic ( default is 1)
  • AUTO_CREATE_TOPICS: whether a new topic should be created when a non-existent topic is written to (default is true)

LOG_RETENTION_MINUTES and LOG_RETENTION_BYTES are low by default to avoid wasting space since data is not relevant and only used for testing

The base image

As explained in Behind the scenes, the demo itself relies on a custom image running Kafka and Zookeeper altogether. You are free to build your own derived images from its Dockerfile , although for most changes overriding one of this build arguments should do the trick:

  • KAFKA_VERSION: Kafka version to be downloaded and installed
  • ZOOKEEPER_VERSION: Zookeeper version to be downloaded and installed. Download links are slightly different since 3.5, so downgrading below that won't work without adapting the Dockerfile
  • SCALA_VERSION: Scala version, should remain 2.13 for a while for new Kafka versions

Why this project

I was involved in a project requiring validation of persistent data streams, but I needed some data producers to begin testing and deployment!

Instead of hard-coding my way through, I thought of preparing a customizable Docker image that can serve anyone's needs.

Behind the scenes

The resulting project is a compendium of different techniques from different sources, especial thanks go to:

  • Dario Radečić and his awesome blog posts for installing Kafka and creating a simple producer
  • @hey-johnnypark for implementing a Docker image containing both Kafka and Zookeeper altogether, removing the hassle of setting up a docker-compose setup.