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        "author_email": "andrew@datamountaineer.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Software Development :: Libraries"
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        "description": "[![Build Status](https://travis-ci.org/datamountaineer/python-serializers.svg?branch=master)](https://travis-ci.org/datamountaineer/python-serializers)\n[![PyPI](https://img.shields.io/badge/PyPi-0.3-blue.svg)](https://pypi.python.org/pypi/datamountaineer-schemaregistry/0.3)\n\n# Python Schema Registry Client and Serializers/Deserializers\n\nA Python client used to interact with [Confluent](http://confluent.io/)'s\n[schema registry](https://github.com/confluentinc/schema-registry).  Supports Python 3.5.  This also works within a virtual env.\n\nThe API is heavily based off of the existing Java API of [Confluent schema registry](https://github.com/confluentinc/schema-registry).\n\nThe serializers/deserializers use [fastavro](https://github.com/tebeka/fastavro) for reading and writing by default.\nWhen 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.\n\n# Installation\n\nRun `python setup.py install` from the source root.\n\nor via pip\n\n```\npip3 install datamountaineer-schemaregistry \n```\n\n# Example Usage\n\nSetup\n\n```python\nfrom datamountaineer.schemaregistry.client import SchemaRegistryClient\nfrom datamountaineer.schemaregistry.serializers import MessageSerializer, Util\n\n# Initialize the client\nclient = SchemaRegistryClient(url='http://registry.host')\n```\n\nSchema operations\n\n```python\n# register a schema for a subject\nschema_id = client.register('my_subject', avro_schema)\n\n# fetch a schema by ID\navro_schema = client.get_by_id(schema_id)\n\n# get the latest schema info for a subject\nschema_id,avro_schema,schema_version = client.get_latest_schema('my_subject')\n\n# get the version of a schema\nschema_version = client.get_version('my_subject', avro_schema)\n\n# Compatibility tests\nis_compatible = client.test_compatibility('my_subject', another_schema)\n\n# One of NONE, FULL, FORWARD, BACKWARD\nnew_level = client.update_compatibility('NONE','my_subject')\ncurrent_level = client.get_compatibility('my_subject')\n```\n\nEncoding to write back to Kafka. Encoding by id is the most efficent as it avoids an extra trip to the Schema Registry to\nlookup the schema id.\n\n```python\n# Message operations\n\n# encode a record to put onto kafka\nserializer = MessageSerializer(client)\n\n#build your avro\nrecord = get_obj_to_put_into_kafka()\n\n# use the schema id directly\nencoded = serializer.encode_record_with_schema_id(schema_id, record)\n```\n\nEncode by schema only.\n\n```python\n# use an existing schema and topic\n# this will register the schema to the right subject based\n# on the topic name and then serialize\nencoded = serializer.encode_record_with_schema('my_topic', avro_schema, record)\n```\n\nReading messages\n\n```python\n# decode a message from kafka\nmessage = get_message_from_kafka()\ndecoded_object = serializer.decode_message(message)\n```\n# Release Notes\n\n**0.3**\n* Testing, setup, and import improvements from PR #4\n\n# Testing\n\n```\npip3 install pytest mock\npy.test -s -rxs -v\n```\n\n\n\n# License\n\nThe project is licensed under the Apache 2 license.",
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