# Optimizing Event Replays

This guide explains how to optimize the performance and structure of event replays in event-sourced systems. 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

Reading and observing events **as streams** allows clients to **process events incrementally without having to buffer large amounts of data in memory**.

> **Try it with EventSourcingDB**
>
> EventSourcingDB provides endpoints for **reading and observing events as streams**, which return events in **NDJSON format** – see **[Understanding NDJSON](/docs/eventsourcingdb/api#understanding-ndjson)**.

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, **snapshots can 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**. Even if the event store guarantees a **global order of events**, consumers do not need 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**.