When caching schemas, ensure the cache key matches the input

[rayokota]

What

When caching schemas, ensure the cache key matches the input

Checklist

• Contains customer facing changes? Including API/behavior changes
• Did you add sufficient unit test and/or integration test coverage for this PR?
  • Tested with a local SR

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Comments suppressed due to low confidence (4)

src/confluent_kafka/schema_registry/schema_registry_client.py:476

• The new signature introduces an explicit 'schema' parameter which is used as a cache key, but there is no check to ensure it matches registered_schema.schema. Consider validating that both schemas are identical to prevent caching inconsistencies.

def set_registered_schema(self, schema: 'Schema', registered_schema: 'RegisteredSchema'):

src/confluent_kafka/schema_registry/schema_registry_client.py:788

• Ensure that the 'schema' passed into set_schema here is intentionally different from registered_schema.schema, or else validate that they are equivalent, so that the cache key accurately reflects the intended input.

self._cache.set_schema(subject_name, registered_schema.schema_id, schema)

src/confluent_kafka/schema_registry/schema_registry_client.py:868

• Verify that the explicit 'schema' argument passed here correctly represents the schema intended for caching, ensuring consistency with registered_schema.schema.

self._cache.set_registered_schema(schema, registered_schema)

src/confluent_kafka/schema_registry/schema_registry_client.py:1027

• Confirm that using registered_schema.schema directly as the cache key aligns with the new caching logic and that no unintended discrepancies arise compared to other cache update calls.

self._cache.set_registered_schema(registered_schema.schema, registered_schema)

[sonarqube-confluent]

Analysis Details

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Coverage and Duplications

• 100.00% Coverage (61.70% Estimated after merge)
• No duplication information (0.90% Estimated after merge)

Project ID: confluent-kafka-python

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