Kafka Airflow Provider

Warning

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.

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 :

FAQs

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:

pip install angreal && angreal dev-setup

angreal 2.0.3

USAGE:
    angreal [OPTIONS] <SUBCOMMAND>

OPTIONS:
    -h, --help       Print help information
    -v, --verbose    verbose level, (may be used multiple times for more verbosity)
    -V, --version    Print version information

SUBCOMMANDS:
    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