django-kafka

This library is using confluent-kafka-python which is a wrapper around the librdkafka (Apache Kafka C/C++ client library).

It helps to integrate kafka with Django.

Quick start

pip install django-kafka

Configure:

Considering you have locally setup kafka instance with no authentication. All you need is to define the bootstrap servers.

# ./settings.py

INSTALLED_APPS = [
  # ...
  "django_kafka",
]

DJANGO_KAFKA = {
    "GLOBAL_CONFIG": {
      "bootstrap.servers": "kafka1:9092",
    },
}

Define a Topic:

Topics define how to handle incoming messages and how to produce an outgoing message.

from confluent_kafka.serialization import MessageField
from django_kafka.topic import Topic


class Topic1(Topic):
    name = "topic1"

    def consume(self, msg):
        key = self.deserialize(msg.key(), MessageField.KEY, msg.headers())
        value = self.deserialize(msg.value(), MessageField.VALUE, msg.headers())
        # ... process values

Define a Consumer:

Consumers define which topics they take care of. Usually you want one consumer per project. If 2 consumers are defined, then they will be started in parallel.

Consumers are auto-discovered and are expected to be located under the consumers.py.

# ./consumers.py

from django_kafka import kafka
from django_kafka.consumer import Consumer, Topics

from my_app.topics import Topic1


# register your consumer using `DjangoKafka` class API decorator
@kafka.consumers()
class MyAppConsumer(Consumer):
    # tell the consumers which topics to process using `django_kafka.consumer.Topics` interface.
    topics = Topics(
        Topic1(),
    )

    config = {
        "group.id": "my-app-consumer",
        "auto.offset.reset": "latest",
        "enable.auto.offset.store": False,
    }

Start the Consumers:

You can use django management command to start defined consumers.

./manage.py kafka_consume

Or you can use DjangoKafka class API.

from django_kafka import kafka

kafka.start_consumers()

Check Confluent Python Consumer for API documentation.

Produce:

Message are produced using a Topic instance.

from my_app.topics import Topic1

# this will send a message to kafka, serializing it using the defined serializer 
Topic1().produce("some message")

Check Confluent Python Producer for API documentation.

Define schema registry:

The library is using Confluent's SchemaRegistryClient. In order to use it define a SCHEMA_REGISTRY setting.

Find available configs in the SchemaRegistryClient docs.

DJANGO_KAFKA = {
    "SCHEMA_REGISTRY": {
      "url": "http://schema-registry",
    },
}

Note: take django_kafka.topic.AvroTopic as an example if you want to implement a custom Topic with your schema.

Settings:

Defaults:

DJANGO_KAFKA = {
    "CLIENT_ID": f"{socket.gethostname()}-python",
    "GLOBAL_CONFIG": {},
    "PRODUCER_CONFIG": {},
    "CONSUMER_CONFIG": {},
    "POLLING_FREQUENCY": 1,  # seconds
    "SCHEMA_REGISTRY": {},
    "ERROR_HANDLER": "django_kafka.error_handlers.ClientErrorHandler",
}

CLIENT_ID

Default: f"{socket.gethostname()}-python"

An id string to pass to the server when making requests. The purpose of this is to be able to track the source of requests beyond just ip/port by allowing a logical application name to be included in server-side request logging.

Note: This parameter is included in the config of both the consumer and producer unless client.id is overwritten within PRODUCER_CONFIG or CONSUMER_CONFIG.

GLOBAL_CONFIG

Default: {}

Defines configurations applied to both consumer and producer. See configs marked with *.

PRODUCER_CONFIG

Default: {}

Defines configurations of the producer. See configs marked with P.

CONSUMER_CONFIG

Default: {}

Defines configurations of the consumer. See configs marked with C.

POLLING_FREQUENCY

Default: 1 # second

How often client polls for events.

SCHEMA_REGISTRY

Default: {}

Configuration for confluent_kafka.schema_registry.SchemaRegistryClient.

ERROR_HANDLER

Default: django_kafka.error_handlers.ClientErrorHandler

This is an error_cb hook (see Kafka Client Configuration for reference). It is triggered for client global errors and in case of fatal error it raises DjangoKafkaError.

Bidirectional data sync with no infinite event loop.

For example, you want to keep a User table in sync in multiple systems.

The idea is to send events from all systems to the same topic, and also consume events from the same topic, marking the record with kafka_skip=True at the consumption time.

• Producer should respect kafka_skip=True and do not produce new events when True.
• Any updates to the User table, which are happening outside the consumer, should set kafka_skip=False which will allow the producer to create an event again.

This way the chronology is strictly kept and the infinite events loop is avoided.

The disadvantage is that each system will still consume its own message.

There are 2 mixins for django Model and for QuerySet:

KafkaSkipMixin

It adds new kafka_skip boolean field, which defaults to False. And overrides Model.save method and sets kafka_skip=False.

Usage:

from django.contrib.auth.base_user import AbstractBaseUser
from django.contrib.auth.models import PermissionsMixin
from django_kafka.models import KafkaSkipMixin

class User(KafkaSkipMixin, PermissionsMixin, AbstractBaseUser):
    # ...

KafkaSkipQueryset

If you have defined a custom manager on your model then you should inherit it from KafkaSkipQueryset. It adds kafka_skip=False when using update method.

Note: kafka_skip=False is only set when it's not provided to the update kwargs. E.g. User.objects.update(first_name="John", kafka_skip=True) will not be changed to kafka_skip=False.

Usage:

from django.contrib.auth.base_user import AbstractBaseUser
from django.contrib.auth.base_user import BaseUserManager
from django.contrib.auth.models import PermissionsMixin
from django_kafka.models import KafkaSkipMixin, KafkaSkipQueryset


class UserManager(BaseUserManager.from_queryset(KafkaSkipQueryset)):
    # ...


class User(KafkaSkipMixin, PermissionsMixin, AbstractBaseUser):
    # ...
    objects = UserManager()

Making a new release

• bump-my-version is used to manage releases.

• Ruff linter is used to validate the code style. Make sure your code complies withg the defined rules. You may use ruff check --fix for that. Ruff is executed by GitHub actions and the workflow will fail if Ruff validation fails.

• Add your changes to the CHANGELOG, then run

docker compose run --rm app bump-my-version bump <major|minor|patch>

This will update version major/minor/patch version respectively and add a tag for release.

• Push including new tag to publish the release to pypi.

git push origin tag <tag_name>