Does Exactly-Once processing impact recovery latency during state restoration?

In high-volume data processing pipelines, maintaining state consistency during a failure requires restoring a snapshot and replaying events from the last checkpoint. To prevent duplicate side effects in downstream sinks, the system must implement idempotency to ensure exactly-once processing guarantees.

There is a technical trade-off between the frequency of these state snapshots and the resulting latency overhead. While frequent checkpoints reduce the volume of data to be replayed after a crash, they may introduce significant processing pauses during normal operation.

Which mechanisms effectively balance the latency overhead of frequent snapshotting against the recovery time required for large-state restoration? How does the choice of checkpointing interval specifically affect the window of instability during a recovery event?