A mini-framework for Kafka apps

pip install kaf==0.2.3



Så er det tid til en kop kaf'

Kaf is a small Python framework for creating Kafka apps. It is inspired by Faust, but differs in the following ways:

  • Kaf is synchronous (async is future work)
  • Kaf is compatible with Azure Eventhubs (over Kafka interface)
  • Kaf is designed to work with different brokers for the consumer and producer

The framework depends on Confluent Kafka.

How to use

Minimal example:

import logging

from kaf import KafkaApp

consumer_config = {'bootstrap.servers': 'kafka:9092', 'group.id': 'myapp'}
producer_config = {'bootstrap.servers': 'kafka:9092'}

app = KafkaApp(

@app.process(topic='foo', publish_to='bar', accepts='json', returns='json')
def add_one(input):
    number = input['number']
    yield {'result':  number+1}, bytes(number)

def done(msg, seconds_elapsed):
    app.logger.info(f'Processed message in {seconds_elapsed} seconds')

if __name__ == '__main__':

How errors are handled

Kafka functions keep trying until they succeed. Each user function will get one chance to process each incoming message. Any exception raised by a user functions will only be logged, but otherwise ignored. If a user wants to implement retrying they can do that, but it will stall the pipeline until the function returns.

Future work:

Features to be added:

  • Add decorators for app events on_consume, on_processed and on_publised. This will allow to hook up e.g. Datadog metrics to these events.

How to deploy a new version

Steps (can maybe be improved):

  1. change version and download_url in setup.py
  2. git add + commit + push
  3. create new release in GitHub (check source-code link, should match download_url)
  4. Run python setup.py sdist
  5. Run twine upload dist/* --verbose (if not installed, pip install twine first)

Useful links used: