Optimizing Event Replays

This guide explains how to optimize the performance and structure of event replays in systems built on EventSourcingDB. Replaying events is a fundamental capability of event-sourced architectures. It allows you to rebuild state, regenerate read models, migrate data structures, or recover from errors. While the ability to replay is always available, doing so efficiently — especially at scale — requires careful design.

There is a significant difference between replaying ten events to rehydrate an aggregate and processing ten million events to rebuild a system-wide read model. This guide provides practical strategies to keep replay times manageable, prevent unnecessary computations, and ensure your systems behave correctly during and after replay.

Replay Use Cases

Event replays occur in several situations. Rebuilding a single aggregate from its stream of events is a common pattern, and typically involves only a handful of events. Larger replays happen when entire read models are regenerated, especially during schema evolution, data migration, or onboarding of new consumers. Replays also occur implicitly in systems that respond to events reactively — for example, when an event handler restarts and needs to catch up.

While the underlying storage remains the same, these use cases differ in scale, in performance requirements, and in how side effects are handled. For aggregate reconstruction, fast response times are essential. For bulk processing of historical data, throughput and resilience become more important.

Stream Processing and Incremental Consumption

EventSourcingDB provides endpoints for reading and observing events as streams. These interfaces return events in NDJSON format, allowing clients to process events incrementally without having to buffer large amounts of data in memory.

Using streaming APIs enables backpressure-aware processing and supports scenarios where progress tracking and checkpointing are necessary. Rather than treating the replay as a monolithic operation, break it into manageable chunks and process them step by step. This approach is particularly valuable when billions of events need to be processed during a full-system rebuild.

Filtering and Selective Replay

Not every event is relevant for every replay. If a read model or projection only depends on a few specific event types, it is wasteful to process unrelated data. Apply early filtering to reduce the volume of events being handled. This can be achieved either by filtering at the source — for example, through subject constraints or event type selection — or within the application logic, by discarding events that are not needed.

Even small reductions in event volume can yield significant improvements in performance, especially when the cost per event is nontrivial.

Managing Side Effects

One of the most important considerations during a replay is whether or not it is safe to trigger side effects. Events that are replayed for the purpose of building internal state or read models should not cause external actions such as sending emails, updating third-party systems, or writing to external databases.

Replay-safe logic should be idempotent and isolated. Components that are responsible for side effects should be designed to distinguish between live processing and replay, and to avoid duplicating actions. This is especially important in systems where side effects are not reversible or where duplication would cause inconsistencies.

Snapshots and Aggregate Replays

When replaying the state of an individual aggregate, EventSourcingDB supports snapshots that store a precomputed state at a certain point in time. This allows you to resume replay from the last known snapshot rather than from the beginning of the stream. Especially for aggregates with long histories, this significantly reduces the time required to reconstruct the current state.

However, snapshots are not useful in scenarios where the entire event history is needed, such as regenerating read models or computing analytics. In those cases, all relevant events must be processed, regardless of whether a snapshot exists.

Parallel Processing and Partitioning

When replaying events across multiple subjects, it is often possible to process streams independently. If the logic of your read models or projections does not depend on cross-subject ordering, you can partition the workload and distribute it across multiple workers or processes. This approach increases throughput and reduces total replay time.

To ensure correctness, each worker should operate on a disjoint subset of the event space, such as a defined range of subjects or event types. EventSourcingDB guarantees a global order of events, but does not require consumers to process them strictly in that order unless your application depends on it.

Progress Tracking and Checkpoints

Replays that span large datasets or take significant time to complete should be designed with explicit progress tracking. Rather than processing the entire history in one pass, divide the replay into batches and record the last successfully processed event. This makes it possible to resume after failures without starting over.

Checkpoints can be as simple as storing the last processed event ID in a file or database. More advanced setups may include transaction logs, timestamps, or version tags. The goal is always the same: make replays restartable and observable.

Replaying in Production

While replays are powerful, they can place considerable load on the event store, the network, and downstream consumers. When running large-scale replays in production environments, isolate the replay process where possible. Run it in dedicated services, outside of time-critical workflows, and monitor its resource usage closely.

It is also advisable to pace the replay using rate limits or scheduled windows, especially when the processing includes writes to external systems. This reduces the risk of unexpected interference with live operations.

Design for Replay from the Start

In event-sourced systems, replay is not a fallback — it is a primary feature. Designing your application to support efficient and correct replay from the beginning avoids many problems later. Event handlers, read models, and derived state should be written with replayability in mind. Side effects must be controlled, checkpoints must be recorded, and processing must be resumable.

By treating replay as a first-class concern, you gain one of the most powerful benefits of event sourcing: the ability to revisit, reinterpret, and reuse historical data to support new use cases and evolve your system over time.