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DataMountaineer Python 3 Confluent Schema Registry Client

Project Description

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# Python Schema Registry Client and Serializers/Deserializers

A Python client used to interact with [Confluent]('s
[schema registry]( Supports Python 3.5. This also works within a virtual env.

The API is heavily based off of the existing Java API of [Confluent schema registry](

The serializers/deserializers use [fastavro]( for reading and writing by default.
When one does not want to use `fastavro`, it can be disabled by providing `fast_avro=False` to the `MessageSerializer` constructor and Apache Avro's `avro` package will be used instead.

# Installation

Run `python install` from the source root.

or via pip

pip3 install datamountaineer-schemaregistry

# Example Usage


from datamountaineer.schemaregistry.client import SchemaRegistryClient
from datamountaineer.schemaregistry.serializers import MessageSerializer, Util

# Initialize the client
client = SchemaRegistryClient(url='')

Schema operations

# register a schema for a subject
schema_id = client.register('my_subject', avro_schema)

# fetch a schema by ID
avro_schema = client.get_by_id(schema_id)

# get the latest schema info for a subject
schema_id,avro_schema,schema_version = client.get_latest_schema('my_subject')

# get the version of a schema
schema_version = client.get_version('my_subject', avro_schema)

# Compatibility tests
is_compatible = client.test_compatibility('my_subject', another_schema)

new_level = client.update_compatibility('NONE','my_subject')
current_level = client.get_compatibility('my_subject')

Encoding to write back to Kafka. Encoding by id is the most efficent as it avoids an extra trip to the Schema Registry to
lookup the schema id.

# Message operations

# encode a record to put onto kafka
serializer = MessageSerializer(client)

#build your avro
record = get_obj_to_put_into_kafka()

# use the schema id directly
encoded = serializer.encode_record_with_schema_id(schema_id, record)

Encode by schema only.

# use an existing schema and topic
# this will register the schema to the right subject based
# on the topic name and then serialize
encoded = serializer.encode_record_with_schema('my_topic', avro_schema, record)

Reading messages

# decode a message from kafka
message = get_message_from_kafka()
decoded_object = serializer.decode_message(message)
# Release Notes

* Testing, setup, and import improvements from PR #4

# Testing

pip3 install pytest mock
py.test -s -rxs -v

# License

The project is licensed under the Apache 2 license.

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