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Version: 2.10.1

Schema evolution and compatibility

Normally, schemas do not stay the same over a long period of time. Instead, they undergo evolutions to satisfy new needs.

This chapter examines how Pulsar schema evolves and what Pulsar schema compatibility check strategies are.

Schema evolution​

Pulsar schema is defined in a data structure called SchemaInfo.

Each SchemaInfo stored with a topic has a version. The version is used to manage the schema changes happening within a topic.

The message produced with SchemaInfo is tagged with a schema version. When a message is consumed by a Pulsar client, the Pulsar client can use the schema version to retrieve the corresponding SchemaInfo and use the correct schema information to deserialize data.

What is schema evolution?​

Schemas store the details of attributes and types. To satisfy new business requirements, you need to update schemas inevitably over time, which is called schema evolution.

Any schema changes affect downstream consumers. Schema evolution ensures that the downstream consumers can seamlessly handle data encoded with both old schemas and new schemas.

How Pulsar schema should evolve?​

The answer is Pulsar schema compatibility check strategy. It determines how schema compares old schemas with new schemas in topics.

For more information, see Schema compatibility check strategy.

How does Pulsar support schema evolution?​

  1. When a producer/consumer/reader connects to a broker, the broker deploys the schema compatibility checker configured by schemaRegistryCompatibilityCheckers to enforce schema compatibility check.

    The schema compatibility checker is one instance per schema type.

    Currently, Avro and JSON have their own compatibility checkers, while all the other schema types share the default compatibility checker which disables schema evolution.

  2. The producer/consumer/reader sends its client SchemaInfo to the broker.

  3. The broker knows the schema type and locates the schema compatibility checker for that type.

  4. The broker uses the checker to check if the SchemaInfo is compatible with the latest schema of the topic by applying its compatibility check strategy.

    Currently, the compatibility check strategy is configured at the namespace level and applied to all the topics within that namespace.

Schema compatibility check strategy​

Pulsar has 8 schema compatibility check strategies, which are summarized in the following table.

Suppose that you have a topic containing three schemas (V1, V2, and V3), V1 is the oldest and V3 is the latest:

Compatibility check strategyDefinitionChanges allowedCheck against which schemaUpgrade first
ALWAYS_COMPATIBLEDisable schema compatibility check.All changes are allowedAll previous versionsAny order
ALWAYS_INCOMPATIBLEDisable schema evolution.All changes are disabledNoneNone
BACKWARDConsumers using the schema V3 can process data written by producers using the schema V3 or V2.
  • Add optional fields
  • Delete fields
  • Latest versionConsumers
    BACKWARD_TRANSITIVEConsumers using the schema V3 can process data written by producers using the schema V3, V2 or V1.
  • Add optional fields
  • Delete fields
  • All previous versionsConsumers
    FORWARDConsumers using the schema V3 or V2 can process data written by producers using the schema V3.
  • Add fields
  • Delete optional fields
  • Latest versionProducers
    FORWARD_TRANSITIVEConsumers using the schema V3, V2 or V1 can process data written by producers using the schema V3.
  • Add fields
  • Delete optional fields
  • All previous versionsProducers
    FULLBackward and forward compatible between the schema V3 and V2.
  • Modify optional fields
  • Latest versionAny order
    FULL_TRANSITIVEBackward and forward compatible among the schema V3, V2, and V1.
  • Modify optional fields
  • All previous versionsAny order

    ALWAYS_COMPATIBLE and ALWAYS_INCOMPATIBLE​

    Compatibility check strategyDefinitionNote
    ALWAYS_COMPATIBLEDisable schema compatibility check.None
    ALWAYS_INCOMPATIBLEDisable schema evolution, that is, any schema change is rejected.
  • For all schema types except Avro and JSON, the default schema compatibility check strategy is ALWAYS_INCOMPATIBLE.
  • For Avro and JSON, the default schema compatibility check strategy is FULL.
  • Example​

    • Example 1

      In some situations, an application needs to store events of several different types in the same Pulsar topic.

      In particular, when developing a data model in an Event Sourcing style, you might have several kinds of events that affect the state of an entity.

      For example, for a user entity, there are userCreated, userAddressChanged and userEnquiryReceived events. The application requires that those events are always read in the same order.

      Consequently, those events need to go in the same Pulsar partition to maintain order. This application can use ALWAYS_COMPATIBLE to allow different kinds of events co-exist in the same topic.

    • Example 2

      Sometimes we also make incompatible changes.

      For example, you are modifying a field type from string to int.

      In this case, you need to:

      • Upgrade all producers and consumers to the new schema versions at the same time.

      • Optionally, create a new topic and start migrating applications to use the new topic and the new schema, avoiding the need to handle two incompatible versions in the same topic.

    BACKWARD and BACKWARD_TRANSITIVE​

    Suppose that you have a topic containing three schemas (V1, V2, and V3), V1 is the oldest and V3 is the latest:

    Compatibility check strategyDefinitionDescription

    BACKWARD | Consumers using the new schema can process data written by producers using the last schema. | The consumers using the schema V3 can process data written by producers using the schema V3 or V2. | BACKWARD_TRANSITIVE | Consumers using the new schema can process data written by producers using all previous schemas. | The consumers using the schema V3 can process data written by producers using the schema V3, V2, or V1. |

    Example​

    • Example 1

      Remove a field.

      A consumer constructed to process events without one field can process events written with the old schema containing the field, and the consumer will ignore that field.

    • Example 2

      You want to load all Pulsar data into a Hive data warehouse and run SQL queries against the data.

      Same SQL queries must continue to work even the data is changed. To support it, you can evolve the schemas using the BACKWARD strategy.

    FORWARD and FORWARD_TRANSITIVE​

    Suppose that you have a topic containing three schemas (V1, V2, and V3), V1 is the oldest and V3 is the latest:

    Compatibility check strategyDefinitionDescription

    FORWARD | Consumers using the last schema can process data written by producers using a new schema, even though they may not be able to use the full capabilities of the new schema. | The consumers using the schema V3 or V2 can process data written by producers using the schema V3. | FORWARD_TRANSITIVE | Consumers using all previous schemas can process data written by producers using a new schema. | The consumers using the schema V3, V2, or V1 can process data written by producers using the schema V3.

    Example​

    • Example 1

      Add a field.

      In most data formats, consumers written to process events without new fields can continue doing so even when they receive new events containing new fields.

    • Example 2

      If a consumer has an application logic tied to a full version of a schema, the application logic may not be updated instantly when the schema evolves.

      In this case, you need to project data with a new schema onto an old schema that the application understands.

      Consequently, you can evolve the schemas using the FORWARD strategy to ensure that the old schema can process data encoded with the new schema.

    FULL and FULL_TRANSITIVE​

    Suppose that you have a topic containing three schemas (V1, V2, and V3), V1 is the oldest and V3 is the latest:

    Compatibility check strategyDefinitionDescriptionNote
    FULLSchemas are both backward and forward compatible, which means: Consumers using the last schema can process data written by producers using the new schema. AND Consumers using the new schema can process data written by producers using the last schema.Consumers using the schema V3 can process data written by producers using the schema V3 or V2. AND Consumers using the schema V3 or V2 can process data written by producers using the schema V3.
  • For Avro and JSON, the default schema compatibility check strategy is FULL.
  • For all schema types except Avro and JSON, the default schema compatibility check strategy is ALWAYS_INCOMPATIBLE.
  • FULL_TRANSITIVEThe new schema is backward and forward compatible with all previously registered schemas.Consumers using the schema V3 can process data written by producers using the schema V3, V2 or V1. AND Consumers using the schema V3, V2 or V1 can process data written by producers using the schema V3.None

    Example​

    In some data formats, for example, Avro, you can define fields with default values. Consequently, adding or removing a field with a default value is a fully compatible change.

    tip

    You can set schema compatibility check strategy at the topic, namespace or broker level. For how to set the strategy, see here.

    Schema verification​

    When a producer or a consumer tries to connect to a topic, a broker performs some checks to verify a schema.

    Producer​

    When a producer tries to connect to a topic (suppose ignore the schema auto creation), a broker does the following checks:

    • Check if the schema carried by the producer exists in the schema registry or not.

      • If the schema is already registered, then the producer is connected to a broker and produce messages with that schema.

      • If the schema is not registered, then Pulsar verifies if the schema is allowed to be registered based on the configured compatibility check strategy.

    Consumer​

    When a consumer tries to connect to a topic, a broker checks if a carried schema is compatible with a registered schema based on the configured schema compatibility check strategy.

    Compatibility check strategyCheck logic
    ALWAYS_COMPATIBLEAll pass
    ALWAYS_INCOMPATIBLENo pass
    BACKWARDCan read the last schema
    BACKWARD_TRANSITIVECan read all schemas
    FORWARDCan read the last schema
    FORWARD_TRANSITIVECan read the last schema
    FULLCan read the last schema
    FULL_TRANSITIVECan read all schemas

    Order of upgrading clients​

    The order of upgrading client applications is determined by the compatibility check strategy.

    For example, the producers using schemas to write data to Pulsar and the consumers using schemas to read data from Pulsar.

    Compatibility check strategyUpgrade firstDescription
    ALWAYS_COMPATIBLEAny orderThe compatibility check is disabled. Consequently, you can upgrade the producers and consumers in any order.
    ALWAYS_INCOMPATIBLENoneThe schema evolution is disabled.
  • BACKWARD
  • BACKWARD_TRANSITIVE
  • ConsumersThere is no guarantee that consumers using the old schema can read data produced using the new schema. Consequently, upgrade all consumers first, and then start producing new data.
  • FORWARD
  • FORWARD_TRANSITIVE
  • ProducersThere is no guarantee that consumers using the new schema can read data produced using the old schema. Consequently, upgrade all producers first
  • to use the new schema and ensure that the data already produced using the old schemas are not available to consumers, and then upgrade the consumers.
  • FULL
  • FULL_TRANSITIVE
  • Any orderThere is no guarantee that consumers using the old schema can read data produced using the new schema and consumers using the new schema can read data produced using the old schema. Consequently, you can upgrade the producers and consumers in any order.