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Easy publish and subscribe to events with python and Kafka.

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About The Project

This project is meant to help facilitate effortless publishing and subscribing to events with Python and Kafka.

Guiding principal

  • HTTP
  • Language agnostic
  • Contracts built on top of Kafka


  • aiokafka: can be complex to scale correctly
  • guillotina_kafka: complex, tied to Guillotina
  • faust: requires additional data layers, not language agnostic
  • confluent kafka + avro: close but ends up being like grpc. compilation for languages. No asyncio.

Consider this Python project as syntactic sugar around these ideas.


Using pydantic but can be done with pure JSON.

import kafkaesk
from pydantic import BaseModel

app = kafkaesk.Application()

@app.schema("Content", version=1, retention=24 * 60 * 60)
class ContentMessage(BaseModel):
    foo: str

async def foobar():
    # ...
    # doing something in an async func
    await app.publish("content.edited.Resource", data=ContentMessage(foo="bar"))

A convenience method is available in the subscriber dependency instance, this allow to header propagation from the consumed message.

import kafkaesk
from pydantic import BaseModel

app = kafkaesk.Application()

@app.schema("Content", version=1, retention=24 * 60 * 60)
class ContentMessage(BaseModel):
    foo: str

@app.subscribe("content.*", "group_id")
async def get_messages(data: ContentMessage, subscriber):
    # This will propagate `data` record headers
    await subscriber.publish("content.edited.Resource", data=ContentMessage(foo="bar"))


import kafkaesk
from pydantic import BaseModel

app = kafkaesk.Application()

@app.schema("Content", version=1, retention=24 * 60 * 60)
class ContentMessage(BaseModel):
    foo: str

@app.subscribe("content.*", "group_id")
async def get_messages(data: ContentMessage):

Avoiding global object

If you do not want to have global application configuration, you can lazily configure the application and register schemas/subscribers separately.

import kafkaesk
from pydantic import BaseModel

router = kafkaesk.Router()

@router.schema("Content", version=1, retention=24 * 60 * 60)
class ContentMessage(BaseModel):
    foo: str

@router.subscribe("content.*", "group_id")
async def get_messages(data: ContentMessage):

if __name__ == "__main__":
    app = kafkaesk.Application()

Optional consumer injected parameters:

  • schema: str
  • record: aiokafka.structs.ConsumerRecord
  • app:
  • subscriber:

Depending on the type annotation for the first parameter, you will get different data injected:

  • async def get_messages(data: ContentMessage): parses pydantic schema
  • async def get_messages(data: bytes): give raw byte data
  • async def get_messages(record: aiokafka.structs.ConsumerRecord): give kafka record object
  • async def get_messages(data): raw json data in message

Manual commit

To accomplish a manual commit strategy yourself:

app = kafkaesk.Application(auto_commit=False)

@app.subscribe("content.*", "group_id")
async def get_messages(data: ContentMessage, subscriber):
    await subscriber.consumer.commit()


Add these values to your kafka_settings:

  • ssl_context - this should be a placeholder as the SSL Context is generally created within the application
  • security_protocol - one of SSL or PLAINTEXT
  • sasl_mechanism - one of PLAIN, GSSAPI, SCRAM-SHA-256, SCRAM-SHA-512, OAUTHBEARER
  • sasl_plain_username .
  • sasl_plain_password .

kafkaesk contract

This is a library around using kafka. Kafka itself does not enforce these concepts.

  • Every message must provide a json schema
  • Messages produced will be validated against json schema
  • Each topic will have only one schema
  • A single schema can be used for multiple topics
  • Consumed message schema validation is up to the consumer
  • Messages will be consumed at least once. Considering this, your handling should be idempotent

Message format

    "schema": "schema_name:1",
    "data": { ... }


kafkaesk mymodule:app --kafka-servers=localhost:9092



  • stream_id: str: name of stream to send data to
  • data: class that inherits from pydantic.BaseModel
  • key: Optional[bytes]: key for message if it needs one


  • stream_id: str: fnmatch pattern of streams to subscribe to
  • group: Optional[str]: consumer group id to use. Will use name of function if not provided


  • id: str: id of the schema to store
  • version: Optional[int]: version of schema to store
  • streams: Optional[List[str]]: if streams are known ahead of time, you can pre-create them before you push data
  • retention: Optional[int]: retention policy in seconds


  • kafka_servers: Optional[List[str]]: kafka servers to connect to
  • topic_prefix: Optional[str]: topic name prefix to subscribe to
  • kafka_settings: Optional[Dict[str, Any]]: additional aiokafka settings to pass in
  • replication_factor: Optional[int]: what replication factor topics should be created with. Defaults to min(number of servers, 3).
  • kafka_api_version: str: default auto
  • auto_commit: bool: default True
  • auto_commit_interval_ms: int: default 5000



poetry install

Run tests:

docker-compose up
KAFKA=localhost:9092 poetry run pytest tests



This extension includes classes to extend Python's logging framework to publish structured log messages to a Kafka topic. This extension is made up of three main components: an extended logging.LogRecord and some custom logging.Handlers.

See in examples directory.

Log Record

kafkaesk.ext.logging.record.factory is a function that will return kafkaesk.ext.logging.record.PydanticLogRecord objects. The factory() function scans through any args passed to a logger and checks each item to determine if it is a subclass of pydantid.BaseModel.

If it is a base model instance and model._is_log_model evaluates to True the model will be removed from args and added to record._pydantic_data. After that factory() will use logging's existing logic to finish creating the log record.


This extensions ships with two handlers capable of handling kafkaesk.ext.logging.handler.PydanticLogModel classes: kafakesk.ext.logging.handler.PydanticStreamHandler and kafkaesk.ext.logging.handler.PydanticKafkaeskHandler.

The stream handler is a very small wrapper around logging.StreamHandler, the signature is the same, the only difference is that the handler will attempt to convert any pydantic models it receives to a human readable log message.

The kafkaesk handler has a few more bits going on in the background.

The handler has two required inputs, a instance and a stream name.

Once initialized any logs emitted by the handler will be saved into an internal queue. There is a worker task that handles pulling logs from the queue and writing those logs to the specified topic.


It's hard and "kafka" is already a fun name. Hopefully this library isn't literally "kafkaesque" for you.

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