aws-glue-schema-registry 1.0.0rc4

Use the AWS Glue Schema Registry.

AWS Glue Schema Registry for Python

Use the AWS Glue Schema Registry in Python projects.

This library is a partial port of aws-glue-schema-registry which implements a subset of its features with full compatibility.

Feature Support

Feature	Java Library	Python Library	Notes
Serialization and deserialization using schema registry	✔️	✔️
Avro message format	✔️	✔️
JSON Schema message format	✔️	❌
Kafka Streams support	✔️		N/A for Python, Kafka Streams is Java-only
Compression	✔️	✔️
Local schema cache	✔️	✔️
Schema auto-registration	✔️	✔️
Evolution checks	✔️	✔️
Migration from a third party Schema Registry	✔️	✔️
Flink support	✔️	❌
Kafka Connect support	✔️		N/A for Python, Kafka Connect is Java-only

Installation

Clone this repository and install it:

python setup.py install -e .

You most likely want to install one or more extras. For example, to use the schema registry with Avro schemas and kafka-python, you should install the avro and kafka-python extra:

python setup.py install -e .[avro, kafka-python]

Extra name	Purpose
avro	Allows using Apache Avro schemas, using fastavro as a backend
kafka-python	Provides adapter classes to plug into kafka-python

Usage

First use boto3 to create a low-level AWS Glue client:

import boto3

# Pass your AWS credentials or profile information here
session = boto3.Session(access_key_id=xxx, secret_access_key=xxx, region_name='us-west-2')

glue_client = session.client('glue')

See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html#configuration for more information on configuring boto3.

Send Kafka messages with SchemaRegistrySerializer:

from aws_schema_registry import DataAndSchema, SchemaRegistryClient
from aws_schema_registry.avro import AvroSchema

# In this example we will use kafka-python as our Kafka client,
# so we need to have the `kafka-python` extras installed and use
# the kafka adapter.
from aws_schema_registry.adapter.kafka import SchemaRegistrySerializer
from kafka import KafkaConsumer

# Create the schema registry client, which is a façade around the boto3 glue client
client = SchemaRegistryClient(glue_client,
                              registry_name='my-registry')

# Create the serializer
serializer = SchemaRegistrySerializer(client)

# Create the producer
producer = KafkaProducer(value_serializer=serializer)

# Our producer needs a schema to send along with the data.
# In this example we're using Avro, so we'll load an .avsc file.
with open('user.avsc', 'r') as schema_file:
    schema = AvroSchema(schema_file.read())

# Send message data along with schema
data = {
    'name': 'John Doe',
    'favorite_number': 6
}
producer.send('my-topic', value=(data, schema))
# the value MUST be a tuple when we're using the SchemaRegistrySerializer

Read Kafka messages with SchemaRegistryDeserializer:

from aws_schema_registry import SchemaRegistryClient

# In this example we will use kafka-python as our Kafka client,
# so we need to have the `kafka-python` extras installed and use
# the kafka adapter.
from aws_schema_registry.adapter.kafka import SchemaRegistryDeserializer
from kafka import KafkaConsumer

# Create the schema registry client, which is a façade around the boto3 glue client
client = SchemaRegistryClient(glue_client,
                              registry_name='my-registry')

# Create the deserializer
deserializer = SchemaRegistryDeserializer(client)

# Create the consumer
consumer = KafkaConsumer('my-topic', value_deserializer=deserializer)

# Now use the consumer normally
for message in consumer:
    # The deserializer produces DataAndSchema instances
    value: DataAndSchema = message.value
    # which are NamedTuples with a `data` and `schema` property
    value.data == value[0]
    value.schema == value[1]
    # and can be deconstructed
    data, schema = value

Contributing

Clone this repository and install development dependencies:

pip install -e .[dev]

Run the linter and tests with tox before committing. After committing, check Github Actions to see the result of the automated checks.

Linting

Lint the code with:

flake8

Run the type checker with:

mypy

Tests

Tests go under the tests/ directory. All tests outside of tests/integration are unit tests with no external dependencies.

Tests under tests/integration are integration test that interact with external resources and/or real AWS schema registries. They generally run slower and require some additional configuration.

Run just the unit tests with:

pytest --ignore tests/integration

All integration tests use the following environment variables:

• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY
• AWS_SESSION_TOKEN
• AWS_REGION
• AWS_PROFILE
• CLEANUP_REGISTRY: Set to any value to prevent the test from destroying the registry created during the test, allowing you to inspect its contents.

If no AWS_ environment variables are set, boto3 will try to load credentials from your default AWS profile.

See individual integration test directories for additional requirements and setup instructions.

Tox

This project uses Tox to run tests across multiple Python versions.

Install Tox with:

pip install tox

and run it with:

tox

Note that Tox requires the tested python versions to be installed. One convenient way to manage this is using pyenv. See the .python-versions file for the Python versions that need to be installed.