Offset-Based Replay

Offset-based replay restarts event processing from a specified message offset and continues through subsequent records. It is useful for rebuilding state, recovering from processing errors, or reprocessing a portion of an event stream.

An offset identifies a position in an ordered partition of an event stream. To replay from an offset, a consumer starts reading at that position instead of only consuming newly arriving records.

For example, if a partition contains offsets `100` through `105`, starting replay at `102` processes records at `102`, `103`, `104`, and `105`, subject to the consumer and broker's retention rules. The exact behavior depends on the messaging system and its offset semantics.

Replay can be used to reconstruct application state or rerun business logic after a bug is fixed. The application should account for the fact that replaying an event may execute side effects again.

A common design is to isolate replay from normal live consumption, or to make processing idempotent. Idempotency helps ensure that processing the same event more than once does not incorrectly duplicate the resulting state or external effects.

Key points

• Replay begins at an explicitly selected message offset.
• Subsequent records in the partition are processed in order.
• Replay can rebuild state or reprocess events after a processing problem.
• Repeated processing can duplicate side effects unless the consumer is idempotent.
• Offsets are scoped to partitions in systems such as Kafka.

Interview questions

What is offset-based replay?

Offset-based replay means starting event consumption from a specified offset rather than from the consumer's current position. The consumer then processes that record and subsequent records available in the partition. This is useful when an application needs to rebuild state or rerun processing for historical events. Care is needed because replay can execute side effects more than once.

Why would a backend service use event replay?

A service may replay events to reconstruct derived state after a bug, populate a new data store, or rerun processing after changing business logic. Instead of requiring every state transition to be manually recreated, the service can process the historical event stream again. The replay should be isolated or controlled so it does not unintentionally interfere with live processing.

What can go wrong when replaying events?

The main risk is repeating side effects such as sending an email, charging a customer, or writing duplicate records. Replay can also expose compatibility problems if old events no longer match the current event schema or application logic. A robust system uses idempotency, version-aware event handling, and appropriate isolation for replay workloads. Operational controls should also prevent an accidental replay from consuming a large historical range.

How would you replay a Kafka partition from a specific offset?

I would identify the target topic and partition, determine the desired starting offset, and run a consumer configured to begin from that position. I would generally use a dedicated consumer group or an otherwise isolated replay process so normal production consumption is not unintentionally moved. Before running it, I would verify retention, permissions, event compatibility, and the behavior of all side effects. I would also monitor throughput and errors and define whether the replay should commit offsets at all.