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Showing content from https://learn.microsoft.com/en-us/python/api/overview/azure/schemaregistry-readme below:

Azure Schema Registry client library for Python

Azure Schema Registry is a schema repository service hosted by Azure Event Hubs, providing schema storage, versioning, and management. The registry is leveraged by encoders to reduce payload size while describing payload structure with schema identifiers rather than full schemas. This package provides:

1. A client library to register and retrieve schemas and their respective properties.

2. An JSON schema-based encoder capable of encoding and decoding payloads containing Schema Registry schema identifiers, corresponding to JSON schemas used for validation, and encoded content.

Source code | Package (PyPi) | Package (Conda) | API reference documentation | Samples | Changelog

Azure SDK Python packages support for Python 2.7 has ended on 01 January 2022. For more information and questions, please refer to https://github.com/Azure/azure-sdk-for-python/issues/20691

Install the Azure Schema Registry client library for Python with pip:

pip install azure-schemaregistry

To use the built-in jsonschema validators with the JSON Schema Encoder, install jsonencoder extras:

pip install azure-schemaregistry[jsonencoder]

To use this package, you must have:

• Azure subscription - Create a free account
• Azure Schema Registry - Here is the quickstart guide to create a Schema Registry group using the Azure portal.
• Python 3.8 or later - Install Python

Interaction with Schema Registry starts with an instance of SchemaRegistryClient class. The client constructor takes an Azure Event Hubs fully qualified namespace and an Azure Active Directory credential:

• The fully qualified namespace of the Schema Registry instance should follow the format: <yournamespace>.servicebus.windows.net.

• An AAD credential that implements the TokenCredential protocol should be passed to the constructor. There are implementations of the TokenCredential protocol available in the azure-identity package. To use the credential types provided by azure-identity, please install the Azure Identity client library for Python with pip:

pip install azure-identity

• Additionally, to use the async API, you must first install an async transport, such as aiohttp:

pip install aiohttp

Create client using the azure-identity library:

from azure.schemaregistry import SchemaRegistryClient
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()
# Namespace should be similar to: '<your-eventhub-namespace>.servicebus.windows.net/'
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_FULLY_QUALIFIED_NAMESPACE']
schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, credential)

Create JsonSchemaEncoder using the azure-schemaregistry library:

import os
from azure.schemaregistry import SchemaRegistryClient
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()
# Namespace should be similar to: '<your-eventhub-namespace>.servicebus.windows.net'
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMAREGISTRY_GROUP']
schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, credential)
encoder = JsonSchemaEncoder(client=schema_registry_client, group_name=group_name)

• Schema: Schema is the organization or structure for data. More detailed information can be found here.

• Schema Group: A logical group of similar schemas based on business criteria, which can hold multiple versions of a schema. More detailed information can be found here.

• SchemaRegistryClient: SchemaRegistryClient provides the API for storing and retrieving schemas in schema registry.

• JsonSchemaEncoder: Provides API to encode content to and decode content from Binary Encoding, validate content against a JSON Schema, and cache schemas/schema IDs retrived from the registry using the SchemaRegistryClient locally.

• OutboundMessageContent: Protocol defined under azure.schemaregistry that allows for JsonSchemaEncoder.encode interoperability with certain Azure Messaging SDK message types. Support has been added to:

  • azure.eventhub.EventData for azure-eventhub>=5.9.0

• InboundMessageContent: Protocol defined under azure.schemaregistry that allows for JsonSchemaEncoder.decode interoperability with certain Azure Messaging SDK message types. Support has been added to:

  • azure.eventhub.EventData for azure-eventhub>=5.9.0

If a message type that follows the OutboundMessageContent protocol is provided to the JsonSchemaEncoder, it will set the corresponding content and content type properties. If a message type object that follows the InboundMessageContent protocol is provided to the encoder, it will get the corresponding content and content type properties. These are defined as:

• content: Binary-encoded, JSON schema-validated payload (in general, format-specific payload)

• content type: a string of the format application/json;serialization=Json+<schema ID>, where:

  • application/json;serialization=Json is the format indicator
  • <schema ID> is the hexadecimal representation of GUID, same format and byte order as the string from the Schema Registry service.

If EventData is passed in as the message type, the following properties will be set on the EventData object:

• The body property will be set to the encoded content value.

• The content_type property will be set to the content type value.

If message type is not provided, and by default, the encoder will create the following dict: {"content": <encoded payload>, "content_type": 'application/json;serialization=Json+<schema ID>'}

The following sections provide several code snippets covering some of the most common Schema Registry and Json Schema Encoder tasks, including:

• Register a schema

• Get the schema by id

• Get the schema by version

• Get the id of a schema

• Encode

• Decode

• Event Hubs Send Integration

• Event Hubs Receive Integration

Use SchemaRegistryClient.register_schema method to register a schema.

import os

from azure.identity import DefaultAzureCredential
from azure.schemaregistry import SchemaRegistryClient

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_AVRO_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMA_REGISTRY_GROUP']
name = "your-schema-name"
format = "Avro"
definition = """
{"namespace": "example.avro",
 "type": "record",
 "name": "User",
 "fields": [
     {"name": "name", "type": "string"},
     {"name": "favorite_number",  "type": ["int", "null"]},
     {"name": "favorite_color", "type": ["string", "null"]}
 ]
}
"""

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace=fully_qualified_namespace, credential=token_credential)
with schema_registry_client:
    schema_properties = schema_registry_client.register_schema(group_name, name, definition, format)
    id = schema_properties.id

Get the schema definition and its properties by schema id.

import os

from azure.identity import DefaultAzureCredential
from azure.schemaregistry import SchemaRegistryClient

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_AVRO_FULLY_QUALIFIED_NAMESPACE']
schema_id = 'your-schema-id'

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace=fully_qualified_namespace, credential=token_credential)
with schema_registry_client:
    schema = schema_registry_client.get_schema(schema_id)
    definition = schema.definition
    properties = schema.properties

Get the schema definition and its properties by schema version.

import os

from azure.identity import DefaultAzureCredential
from azure.schemaregistry import SchemaRegistryClient

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_AVRO_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ["SCHEMAREGISTRY_GROUP"]
name = "your-schema-name"
version = int("<your schema version>")

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace=fully_qualified_namespace, credential=token_credential)
with schema_registry_client:
    schema = schema_registry_client.get_schema(group_name=group_name, name=name, version=version)
    definition = schema.definition
    properties = schema.properties

Get the schema id of a schema by schema definition and its properties.

import os

from azure.identity import DefaultAzureCredential
from azure.schemaregistry import SchemaRegistryClient

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_AVRO_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMA_REGISTRY_GROUP']
name = "your-schema-name"
format = "Avro"
definition = """
{"namespace": "example.avro",
 "type": "record",
 "name": "User",
 "fields": [
     {"name": "name", "type": "string"},
     {"name": "favorite_number",  "type": ["int", "null"]},
     {"name": "favorite_color", "type": ["string", "null"]}
 ]
}
"""

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace=fully_qualified_namespace, credential=token_credential)
with schema_registry_client:
    schema_properties = schema_registry_client.register_schema(group_name, name, definition, format)
    id = schema_properties.id

Use the SchemaRegistryClient to pre-register the schema. Encode and validate the content with the JsonSchemaEncoder.

The encode method automatically retrieves the schema from the Schema Registry Service, validates against the content, and caches the schema locally.

import os
import json
from azure.schemaregistry import SchemaRegistryClient, SchemaFormat
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential
from azure.eventhub import EventData

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_JSON_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMAREGISTRY_GROUP']
format = SchemaFormat.JSON
DRAFT2020_12_SCHEMA_IDENTIFIER = "https://json-schema.org/draft/2020-12/schema"

schema = {
    "$id": "https://example.com/person.schema.json",
    "$schema": "https://json-schema.org/draft/2020-12/schema",
    "title": "Person",
    "type": "object",
    "properties": {
        "name": {
            "type": "string",
            "description": "Person's name."
        },
        "favorite_color": {
            "type": "string",
            "description": "Favorite color."
        },
        "favorite_number": {
            "description": "Favorite number.",
            "type": "integer",
        }
    }
}
name = schema["title"]
definition = json.dumps(schema)

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, token_credential)
schema_properties = schema_registry_client.register_schema(group_name, name, definition, format)
schema_id = schema_properties.id

# group_name only needed if passing `schema` to encode
encoder = JsonSchemaEncoder(client=schema_registry_client, validate=DRAFT2020_12_SCHEMA_IDENTIFIER, group_name=group_name)

with encoder:
    dict_content = {"name": "Ben", "favorite_number": 7, "favorite_color": "red"}
    event_data = encoder.encode(dict_content, schema_id=schema_id, message_type=EventData)

    # OR

    message_content_dict = encoder.encode(dict_content, schema_id=schema_id)
    event_data = EventData.from_message_content(message_content_dict["content"], message_content_dict["content_type"])

    # OR

    dict_content = {"name": "Ben", "favorite_number": 7, "favorite_color": "red"}
    message_content = encoder.encode(dict_content, schema=definition)  # group_name required in constructor when `schema` is passed

Decode the content with the JsonSchemaEncoder.

The decode method automatically retrieves the schema from the Schema Registry Service, validates against the content, and caches the schema locally.

import os
from azure.schemaregistry import SchemaRegistryClient
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ["SCHEMAREGISTRY_GROUP"]
DRAFT2020_12_SCHEMA_IDENTIFIER = "https://json-schema.org/draft/2020-12/schema"

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, token_credential)
encoder = JsonSchemaEncoder(client=schema_registry_client, validate=DRAFT2020_12_SCHEMA_IDENTIFIER)

with encoder:
    # event_data is an EventData object with encoded body
    decoded_content = encoder.decode(event_data)

    # OR 

    # content_dict is a TypedDict with encoded content and JSON content type 
    decoded_content = encoder.decode(content_dict)

Integration with Event Hubs to send an EventData object with body set to encoded content and corresponding content_type.

import os
from azure.eventhub import EventHubProducerClient, EventData
from azure.schemaregistry import SchemaRegistryClient
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_JSON_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMAREGISTRY_GROUP']
eventhub_connection_str = os.environ['EVENT_HUB_CONN_STR']
eventhub_name = os.environ['EVENT_HUB_NAME']

schema_id = os.environ['PERSON_JSON_SCHEMA_ID']
DRAFT2020_12_SCHEMA_IDENTIFIER = "https://json-schema.org/draft/2020-12/schema"

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, token_credential)
json_schema_encoder = JsonSchemaEncoder(client=schema_registry_client, validate=DRAFT2020_12_SCHEMA_IDENTIFIER)

eventhub_producer = EventHubProducerClient.from_connection_string(
    conn_str=eventhub_connection_str,
    eventhub_name=eventhub_name
)

with eventhub_producer, json_schema_encoder:
    event_data_batch = eventhub_producer.create_batch()
    dict_content = {"name": "Bob", "favorite_number": 7, "favorite_color": "red"}
    event_data = json_schema_encoder.encode(dict_content, schema_id=schema_id, message_type=EventData)
    event_data_batch.add(event_data)
    eventhub_producer.send_batch(event_data_batch)

Integration with Event Hubs to receive an EventData object and decode the encoded body value.

import os
from azure.eventhub import EventHubConsumerClient
from azure.schemaregistry import SchemaRegistryClient
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential

token_credential = DefaultAzureCredential()
fully_qualified_namespace = os.environ['SCHEMAREGISTRY_JSON_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMAREGISTRY_GROUP']
eventhub_connection_str = os.environ['EVENT_HUB_CONN_STR']
eventhub_name = os.environ['EVENT_HUB_NAME']
DRAFT2020_12_SCHEMA_IDENTIFIER = "https://json-schema.org/draft/2020-12/schema"

schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, token_credential)
json_schema_encoder = JsonSchemaEncoder(client=schema_registry_client, validate=DRAFT2020_12_SCHEMA_IDENTIFIER)

eventhub_consumer = EventHubConsumerClient.from_connection_string(
    conn_str=eventhub_connection_str,
    consumer_group='$Default',
    eventhub_name=eventhub_name,
)

def on_event(partition_context, event):
    decoded_content = json_schema_encoder.decode(event)

with eventhub_consumer, json_schema_encoder:
    eventhub_consumer.receive(on_event=on_event, starting_position="-1")

Schema Registry clients raise exceptions defined in Azure Core if errors are encountered when communicating with the Schema Registry service.

Errors when encoding and decoding related to invalid content/content types will be raised as azure.schemaregistry.encoder.jsonencoder.InvalidContentError, where __cause__ will possibly contain an underlying exception.

This library uses the standard logging library for logging. Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Detailed DEBUG level logging, including request/response bodies and unredacted headers, can be enabled on a client with the logging_enable argument:

import sys
import os
import logging
from azure.schemaregistry import SchemaRegistryClient
from azure.schemaregistry.encoder.jsonencoder import JsonSchemaEncoder
from azure.identity import DefaultAzureCredential

# Create a logger for the SDK
logger = logging.getLogger('azure.schemaregistry')
logger.setLevel(logging.DEBUG)

# Configure a console output
handler = logging.StreamHandler(stream=sys.stdout)
logger.addHandler(handler)

fully_qualified_namespace = os.environ['SCHEMAREGISTRY_FULLY_QUALIFIED_NAMESPACE']
group_name = os.environ['SCHEMAREGISTRY_GROUP']
DRAFT2020_12_SCHEMA_IDENTIFIER = "https://json-schema.org/draft/2020-12/schema"
credential = DefaultAzureCredential()
# This client will log detailed information about its HTTP sessions, at DEBUG level
schema_registry_client = SchemaRegistryClient(fully_qualified_namespace, credential, logging_enable=True)
encoder = JsonSchemaEncoder(client=schema_registry_client, validate=DRAFT2020_12_SCHEMA_IDENTIFIER)

Similarly, logging_enable can enable detailed logging for a single operation, even when it isn't enabled for the client:

schema_registry_client.get_schema(schema_id, logging_enable=True)

Please take a look at the samples directory for detailed examples of how to use this library to register and retrieve schema to/from Schema Registry.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

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