Best Practices for Evolving Schemas in Schema Registry

You can achieve dynamic schema evolution through system-level design in your applications, not through a single feature. Dynamic schema evolution enables you to update schemas without redeploying your applications, which ensures continuous event governance and data validation across distributed systems. With Solace Schema Registry, you can implement a strategy for managing schema changes while maintaining compatibility between message producers and message consumers.

• Key Concepts of Dynamic Schema Evolution
• Using Best Practices for Schema Evolution
• Implementing Best Practices
• Avoiding Anti-Patterns

Key Concepts of Dynamic Schema Evolution

The core principle of dynamic schema evolution is to push schema updates to Solace Schema Registry to ensure producer and consumer applications use validated data, even when schemas evolve over time. This approach centralizes schema management and provides a single source of truth for all applications in your event-driven architecture.

When you push schema changes to the registry to accommodate evolving requirements in your applications, you may encounter application failures during message processing. These failures can occur when your updated schema becomes incompatible with existing message producers or message consumers. To minimize disruption, you can use strategies that allow schema evolution without requiring application restarts or reconfigurations. For example, you can use forward-compatible schema designs, implement graceful degradation patterns, and leverage validation features that are compatible with Schema Registry.

Several serializer/deserializer (SERDES) options can help you achieve dynamic event governance:

• Use the latest schema—Enforces governance by automatically binding apps to the most recent, compatibility-checked schema version.
• Use explicit schemas—When you reference exact schema versions in your configuration file, you prevent schema drift—where producers and consumers unintentionally rely on different schema versions and risk data incompatibility.
• Use application-provided schemas with Schema Registry presence check—This ensures producers emit events only with schemas already tracked and governed in Schema Registry.

For more information about SERDES and these options, see Serialization and Deserialization with Solace Schema Registry.

To achieve true dynamic event governance, you must ensure any new schemas remain compatible with your previous or existing schemas. The best practices outlined in the following sections will guide you through compatibility rules and schema design principles that enable seamless schema evolution.

To achieve dynamic event governance, you must ensure that new schemas you introduce are compatible with previous existing schemas. The following are some best practices to help maintain compatibility and evolve your designs for future evolution:

• Using Best Practices for Schema Evolution
• Implementing Best Practices
• Avoiding Anti-Patterns

Using Best Practices for Schema Evolution

As your data model evolves, you'll need to update your schemas. The following best practices will help you manage schema evolution effectively while minimizing disruption to your applications and ensuring data integrity across your event-driven architecture.

1. Compatibility Rules and Strategies
2. Schema Design Principles for Evolution
3. Updating Schemas in Solace Schema Registry

Compatibility Rules and Strategies

Use compatibility rules to control how schemas evolve while preserving interoperability between versions. Understanding these rules is important for successful schema evolution:

Forward Compatibility

Forward compatibility ensures that older consumers can process data produced with newer schema versions. This is useful when consumer updates may lag behind producer updates. Producers can add new fields without breaking existing consumers, because unknown fields are simply ignored. To support forward compatibility, consumers must handle unknown fields gracefully, a common pattern in many serialization frameworks.

Backward Compatibility

Backward compatibility ensures that newer consumers can process data produced with older schema versions. This is ideal when producer updates need to happen before consumer updates or when consumer changes are more complex. New schema versions must handle potentially missing fields, typically by providing default values, so new consumers can seamlessly read old data without disruption.

Full Compatibility (Recommended but Most Restrictive)

Full compatibility combines both forward and backward compatibility, ensuring that all consumers and producers can interoperate regardless of deployment order. While more restrictive, it provides maximum flexibility and safety, allowing independent updates of producers and consumers without risking data incompatibility. This approach is particularly valuable for critical systems with strict governance requirements.

Compatibility Feature Comparison

Choosing the right compatibility strategy is important for successful schema evolution. Each strategy offers different tradeoffs between flexibility and safety. Consider your specific use case requirements when selecting a strategy. The following table summarizes how each type handles common schema evolution scenarios:

Compatibility Type	Add New Fields	Remove Fields	Old Consumers Can Read New Data	New Consumers Can Read Old Data
Backwards Compatibility	Allowed because new fields have defaults so new consumers can handle missing fields.	Not allowed because old consumers expect the field to exist.	May fail because old consumers may not recognize new fields.	Works because new consumers can handle missing fields with defaults.
Forwards Compatibility	Allowed because old consumers can ignore new fields.	Allowed because new consumers can handle missing fields.	Works because old consumers safely ignore unknown fields.	May fail because new consumers may require fields missing from old data.
Full Compatibility	Allowed because new fields have defaults and all consumers can safely handle them.	Not allowed because removing fields breaks backward compatibility.	Works because old consumers safely ignore new fields (forward compatible).	Works because new consumers fill missing fields with defaults (backward compatible).

Schema Design Principles for Evolution

You can follow these principles to make your designs more resilient to change and easier to evolve:

• Versioning—Use semantic versioning for your schemas. This helps track changes systematically and allows consumers to understand the nature of changes (major, minor, patch) without examining schema details.
• Default Values—Provide default values for new fields to maintain backward compatibility and prevent deserialization failures. When older producers don't include new fields, consumers can still process the data using these defaults.
• Avoid Renaming—Instead of renaming fields, add new fields and deprecate old ones. Renaming breaks compatibility, while adding new fields with the desired name and gradually transitioning preserves it.
• Use Optional Fields—Make new fields optional to ensure backward compatibility when adding fields. This allows older consumers to ignore fields they don't understand and newer consumers to use them when available.
• Use Union Types—Union types allow a field to accept multiple data types, providing flexibility for future changes. This technique helps when you need to evolve a field's type without breaking existing consumers.

Updating Schemas in Solace Schema Registry

When updating a schema in Solace Schema Registry, follow this approach to minimize disruption:

1. Create a new version of the existing schema. For more information, see Using Solace Schema Registry Web Console.
2. Make your changes, following Schema Design Principles for Evolution.
3. Use the compatibility rule in the registry to ensure your changes do not break existing message consumers.

Implementing Best Practices

Implementing schema evolution in practice requires careful planning and consideration of your specific system requirements. Here are practical examples of the design principles that demonstrate how to implement compatibility in real schemas.

Optional Fields with Default Values

The following examples show how to add a new field to an existing schema while maintaining backward compatibility. The newField is defined as a union type that can be either null or string, with a default value of null. This approach ensures that new consumers can use the field when it's provided, old consumers can safely ignore the field, and if the field is missing a default value is used.

Avro Implementation

{
    "type": "record",
    "name": "CustomerEvent",
    "fields": [
        {"name": "customerId", "type": "string"},
        {"name": "email", "type": "string"},
        {"name": "newField", "type": ["null", "string"], "default": null}
    ]
}

JSON Schema Implementation

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "title": "CustomerEvent",
  "properties": {
    "customerId": {
      "type": "string"
    },
    "email": {
      "type": "string"
    },
    "newField": {
      "type": ["string", "null"],
      "default": null
    }
  },
  "required": ["customerId", "email"]
}

Union Types for Flexibility

The following examples show how to use union types to allow a field to accept multiple data types. The status field can be either a string or an integer, with a default value of "active". This provides flexibility for future changes where the status might need to be represented as a numeric code.

Avro Implementation

{"name": "status", "type": ["string", "int"], "default": "active"}

JSON Schema Implementation

{
  "status": {
    "type": ["string", "integer"],
    "default": "active",
    "examples": ["active", 1, 2]
  }
}

Avoiding Anti-Patterns

This section lists some common mistakes that you should avoid to ensure you have a successful schema evolution strategy. The following anti-patterns can lead to compatibility issues and system failures:

• Breaking changes without planning—Avoid removing required fields, changing field types incompatibly, or renaming fields without aliases.
• Tight coupling to schema versions—Avoid hard-coding schema versions directly in application code, use a configurable properties file instead. Handle unknown fields gracefully, and do not assume every message consumer is always up to date.
• Ignoring compatibility rules—Avoid deploying incompatible changes without validation, not testing schema evolution scenarios, or missing monitoring for schema-related failures.