Skip to main content

Move data around between Python services using Kafka and Django Rest Framework serializers.

Project description

license kit format downloads

This library serves as a universal pipe for moving data around between Django applications and services. It is build on top of Apache Kafka, kafka-python, and Django REST Framework.


Install django-logpipe from pip.:

$ pip install django-logpipe

Add logpipe to your installed apps.:


Add connection settings to your file.:


Run migrations. This will create the model used to store Kafka log position offsets.:

$ python migrate logpipe



The first step in either sending or receiving messages with logpipe is to define a serializer. Serializers for logpipe have a few rules:

  1. Must be either a subclass of rest_framework.serializers.Serializer or a class implementing an interface that mimics rest_framework.serializers.Serializer.
  2. Must have property VERSION defined on the class, representing the schema version number.
  3. Should have property KEY_FIELD defined on the class, representing the name of the field to use as the message key. The message key is used by Kafka when performing log compaction. The property can be omitted for topics which do not require a key.
  4. If the serializer will be used for incoming-messages, it should implement class method lookup_instance(cls, **kwargs). This class method will be with message data as kwargs directly before instantiating the serializer. It should lookup and return the related object (if one exists) so that it can be passed to the serializer’s instance argument during initialization. If not objects exists yet (the message is representing a new object), it should return None.

Below is a sample Django model and it’s accompanying serializer.:

from django.db import models
from rest_framework import serializers
import uuid

class Person(models.Model):
    uuid = models.UUIDField(default=uuid.uuid4, unique=True)
    first_name = models.CharField(max_length=200)
    last_name = models.CharField(max_length=200)

class PersonSerializer(serializers.ModelSerializer):
    VERSION = 1
    KEY_FIELD = 'uuid'

    class Meta:
        model = Person
        fields = ['uuid', 'first_name', 'last_name']

    def lookup_instance(cls, uuid, **kwargs):
            return Person.objects.get(uuid=uuid)
        except models.Person.DoesNotExist:

Sending Messages

Once a serializer exists, you can send a message to Kafka by creating Producer object and calling the send method.:

from logpipe import Producer
joe = Person.objects.create(first_name='Joe', last_name='Schmoe')
producer = Producer('people', PersonSerializer)

The above sample code would result in the following message being sent to the Kafka topic named people.:


Receiving Messages

To processing incoming messages, we can reuse the same model and serializer. We just need to instantiate a Consumer object.:

# Watch for messages, but timeout after 1000ms of no messages
consumer = Consumer('people', consumer_timeout_ms=1000)

# Watch for messages and block forever
consumer = Consumer('people')

The consumer object uses Django REST Framework’s built-in save, create, and update methods to apply the message. If your messages are tied directly to a Django model, skip defining the lookup_instance class method and override the save method to house your custom import logic.

Multiple consumers can be watched simultaneously by the same process by using a MultiConsumer.:

from logpipe import MultiConsumer
people_consumer = Consumer('people')
places_consumer = Consumer('places')
multi = MultiConsumer(people_consumer, places_consumer)

# Watch for 'people' and 'places' topics indefinitely

Finally, consumers can be registered and run automatically by the build in run_kafka_consumer management command.:

# myapp/
from django.apps import AppConfig
from logpipe import Consumer, register_consumer

class MyAppConfig(AppConfig):
    name = 'myapp'

# Register consumers with logpipe
def build_person_consumer():
    consumer = Consumer('people')
    return consumer

Use the register_consumer decorator to register as many consumers and topics as you need to work with. Then, run the run_kafka_consumer command to process messages for all consumers automatically in a round-robin fashion.:

$ python run_kafka_consumer

Dealing with Schema Changes

Schema changes are handled using the VERSION attribute required on every serializer class. When sending, a producer includes the schema version number in the message data. Then, when a consumer receives a message, it looks for a register serializer with a matching version number. If no serializer is found with a matching version number, a logpipe.exceptions.UnknownMessageVersionError exception is raised.

To perform a backwards-incompatible schema change, the following steps should be performed.

  1. Update consumer code to have knowledge of the new schema version.
  2. Update producer code to being sending the new schema version.
  3. After some amount of time (when you are sure no old-version messages still exist in Kafka), remove the code related to the old schema version.

For example, if we wanted to require an email field on the Person model we defined above, the first step would be to update consumers to know about the new field.:

class Person(models.Model):
    uuid = models.UUIDField(default=uuid.uuid4, unique=True)
    first_name = models.CharField(max_length=200)
    last_name = models.CharField(max_length=200)
    email = models.EmailField(max_length=200, null=True)

class PersonSerializerV1(serializers.ModelSerializer):
    VERSION = 1
    KEY_FIELD = 'uuid'
    class Meta:
        model = Person
        fields = ['uuid', 'first_name', 'last_name']

class PersonSerializerV2(PersonSerializerV1):
    VERSION = 2
    class Meta(PersonSerializerV1.META):
        fields = ['uuid', 'first_name', 'last_name', 'email']

consumer = Consumer('people', consumer_timeout_ms=1000)

The consumers will now use the appropriate serializer for the message version. Second, we need to update producer code to being using schema version 2.:

producer = Producer('people', PersonSerializerV2)

Finally, after all the old version 1 messages have been dropped (by log compaction), the PersonSerializerV1 class can be removed form the code base.


The follow settings added to to configure logpipe.

Key Description Default Value
BOOTSTRAP_SERVERS A list of Kafka servers to connect to upon startup. Required
DEFAULT_FORMAT The default serialization format to use when sending new messages. json
OFFSET_BACKEND Path to class used to store offset data. logpipe.offset_backends.ModelOffsetStore
MIN_MESSAGE_LAG_MS Minimum amount of time between when a message is sent and when it will be processed. This is useful is a single service is both producing and consuming the same topic. 500
RETRIES Number of times to retry sending a message. 0
TIMEOUT How many seconds to wait for a message sent confirmation from Kafka. 10



  • Initial release.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
django_logpipe-0.1.0-py3-none-any.whl (23.4 kB) Copy SHA256 hash SHA256 Wheel 3.6
django-logpipe-0.1.0.tar.gz (15.9 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page