Apache Airflow Provider - Apache Kafka

Apache Kafka

OfficialData Processing

An Apache Airflow provider for Apache Kafka

Last Published
Feb. 3, 2023
Quick Install

Kafka Airflow Provider


This package has been deprecated after being accepted to OSS Airflow. Please use apache-airflow[apache.kafka] instead if you're looking for a supported kafka provider.

GitHub release (latest by date)PyPIPyPI - Downloads

An airflow provider to:

  • interact with kafka clusters
  • read from topics
  • write to topics
  • wait for specific messages to arrive to a topic

This package currently contains

3 hooks (airflow_provider_kafka.hooks) :

  • admin_client.KafkaAdminClientHook - a hook to work against the actual kafka admin client
  • consumer.KafkaConsumerHook - a hook that creates a consumer and provides it for interaction
  • producer.KafkaProducerHook - a hook that creates a producer and provides it for interaction

4 operators (airflow_provider_kafka.operators) :

  • await_message.AwaitKafkaMessageOperator - a deferable operator (sensor) that awaits to encounter a message in the log before triggering down stream tasks.
  • consume_from_topic.ConsumeFromTopicOperator - an operator that reads from a topic and applies a function to each message fetched.
  • produce_to_topic.ProduceToTopicOperator - an operator that uses a iterable to produce messages as key/value pairs to a kafka topic.
  • event_triggers_function.EventTriggersFunctionOperator - an operator that listens for messages on the topic and then triggers a downstream function before going back to listening.

1 trigger airflow_provider_kafka.triggers :

  • await_message.AwaitMessageTrigger

Quick start

pip install airflow-provider-kafka

Example usages :


Why confluent kafka and not (other library) ? A few reasons: the confluent-kafka library is guaranteed to be 1:1 functional with librdkafka, is faster, and is maintained by a company with a commercial stake in ensuring the continued quality and upkeep of it as a product.

Why not release this into airflow directly ? I could probably make the PR and get it through, but the airflow code base is getting huge and I don't want to burden the maintainers with code that they don't own for maintenance. Also there's been multiple attempts to get a Kafka provider in before and this is just faster.

Why is most of the configuration handled in a dict ? Because that's how confluent-kafka does it. I'd rather maintain interfaces that people already using kafka are comfortable with as a starting point - I'm happy to add more options/ interfaces in later but would prefer to be thoughtful about it to ensure that there difference between these operators and the actual client interface are minimal.

How performant is this ? Look we're not replacing native consumer/producer applications with this - but if you have some light/medium weight batch processes you need to run against a Kafka cluster, this should get you started while you figure out if you need to scale up into something

Local Development

Getting started:

  1. pip install angreal && angreal dev-setup
angreal 2.0.3
-h, --help Print help information
-v, --verbose verbose level, (may be used multiple times for more verbosity)
-V, --version Print version information
demo-clean shut down services and remove files
demo-start start services for example dags
demo-stop stop services for example dags
dev-setup setup a development environment
help Print this message or the help of the given subcommand(s)
init Initialize an Angreal template from source.
lint lint our project
run-tests run our test suite. default is unit tests only
static-tests run static analyses on our project

Setup on M1 Mac

Installing on M1 chip means a brew install of the librdkafka library before you can pip install confluent-kafka

brew install librdkafka
export C_INCLUDE_PATH=/opt/homebrew/Cellar/librdkafka/1.8.2/include
export LIBRARY_PATH=/opt/homebrew/Cellar/librdkafka/1.8.2/lib
pip install confluent-kafka