They solve different problems. A pure reducer defines how game state changes in response to an input; event sourcing defines how accepted changes are retained and used to rebuild state later. You can use reducer-style logic in an event-sourced game, or use reducers without keeping a permanent event log. Neither choice determines which machine is authoritative in multiplayer.
What each approach means
Pure reducers compute transitions
A reducer is a function that takes a current state and an action or event, then returns the next state: nextState = reduce(currentState, action). Keeping this transformation pure—without hidden I/O or side effects—makes transition rules explicit and easier to test. Given the same state and input, it should produce the same result.
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A reducer does not, by itself, save its inputs, decide which network messages are valid, establish message order, or prevent a client from cheating. It is a way to organize transition logic, not a persistence or authority model.
Event sourcing retains a durable history
In event sourcing, an application stores accepted domain events in an append-only stream. An entity’s ordered events describe changes to that entity, and the current state can be rebuilt by replaying them. Microsoft’s Event Sourcing pattern describes this approach and its associated concerns.
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Events should communicate meaningful intent—such as a match being completed or a purchase being accepted—not merely duplicate every changed field. The retained history can support audit, recovery, replay, and projections for other uses. It also becomes a long-lived compatibility obligation: future code must continue to interpret old events correctly.
How the choice interacts with multiplayer authority
Authority is a separate architecture decision. In a server-authoritative game, clients send requests or commands; the server decides which are valid and owns the accepted game state. Epic’s Unreal Engine 5.8 networking overview describes the server as holding authoritative state while clients maintain approximations using replicated information. Unreal also emphasizes choosing what information is sent to which connections, rather than replicating everything indiscriminately.
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Unity’s Netcode for GameObjects authority guidance describes server authority as a way to centralize conflict decisions and support rollback and competitive client prediction, with latency and server-resource costs. Its distributed-authority model assigns authority over subdivisions of objects to different game instances, reducing reliance on a single simulator but requiring clear ownership boundaries.
Neither a reducer nor an event store chooses between these topologies. A common arrangement is for an authoritative server to validate commands, apply reducer-like transitions, persist selected consequential events, and send clients snapshots or deltas. Clients can predict local outcomes for responsiveness, then reconcile with server decisions. Prediction and rollback need repeatable simulation and reconciliation rules; keeping an event log alone does not provide either.
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When should a multiplayer game persist events?
Persist an event when its history has lasting product or operational value and the cost of preserving and maintaining that history is justified. Do not treat every input packet or simulation update as a durable domain event.
- Good candidates: accepted match outcomes, inventory changes, purchases, ranked results, or other consequential actions where audit, recovery, or replay matters.
- Potentially poor candidates: high-frequency movement, physics samples, and other transient updates when only the latest live state is needed. Storing every tick can create large streams and make recovery expensive.
- Alternatives for transient state: keep it in live simulation memory, replicate selected state or deltas, and use snapshots or bounded histories where recovery or debugging requires them. This is an engineering tradeoff, not a universal rule.
Event sourcing can also require ordered per-entity streams and concurrency controls. Microsoft’s guidance covers optimistic concurrency and retries, event versioning and upcasting, compensating events, and snapshots. Snapshots reduce the amount of history that must be replayed to restore state, but do not remove the need to handle older events as schemas and rules evolve. An event store should not be confused with a message broker: a broker can distribute events without necessarily providing durable per-entity stream queries or optimistic concurrency.
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Decision framework
| Question | Event-sourced approach | Reducer-centered approach | Multiplayer consequence |
|---|---|---|---|
| Does the history itself matter? | Retain accepted domain events for audit, replay, recovery, or projections. | Keep current state; retain only a bounded history if debugging or replay requires it. | Persist consequential outcomes when their history is worth maintaining; do not equate transport messages with durable events. |
| How frequent are the updates? | Plan for event volume and replay cost; snapshots or projections can limit rebuild work. | Often simpler when transitions are frequent and only current state matters. | Budget storage separately from replication. Movement and physics may be high-rate, so persisting each update needs a clear justification. |
| Is replay or rollback required? | Replay can rebuild state if event application and version handling remain correct. | Transitions can be replayed in memory from inputs without making those inputs the permanent system of record. | Prediction and rollback require repeatable simulation and reconciliation; event sourcing does not guarantee determinism. |
| How are conflicts resolved? | Define ordering and concurrency controls for streams, including what happens on competing writes. | Serialize transitions through an owner or loop, or implement explicit conflict rules. | Server authority centralizes decisions; distributed authority requires explicit ownership and reconciliation boundaries. |
| What do clients receive? | Replicate selected events, projections, snapshots, or deltas—not necessarily the full authoritative log. | Replicate selected state or deltas after transitions. | Choose data per client and connection; bandwidth and relevance constrain replication. |
A practical hybrid design
For many games, the useful question is not which label to adopt, but which state belongs in which mechanism. One workable pattern is:
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- Apply explicit transitions. Use reducer-like logic for state changes where repeatability and testability matter. Keep network I/O and persistence outside the pure transition function.
- Record meaningful outcomes selectively. Emit durable domain events for actions whose history supports recovery, audit, or product requirements. Keep ephemeral simulation updates out of the permanent log unless there is a specific reason to retain them.
- Bound recovery work. Use snapshots or materialized projections when replaying an entity’s full event stream would be too costly. Maintain compatibility with older event versions.
- Replicate for gameplay, not for storage symmetry. Send clients the state, events, or deltas they need. Include sufficient sequencing or version information for clients to reconcile predicted state with authoritative updates.
Colyseus provides an example of these concerns coexisting: its multiplayer framework documentation describes server-defined room state and binary delta synchronization, and lists prediction and rollback features. A server can therefore own current state and send compact changes without permanently storing every simulation step as an event-sourced history.
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Costs to account for before choosing event sourcing
- Replay and recovery: long streams take more work to rehydrate; snapshots and projections add their own design and maintenance needs.
- Schema evolution: old events outlive the code that first wrote them, so versioning, upcasting, or other compatibility handling is necessary.
- Ordering and concurrency: concurrent accepted actions need defined ordering and conflict behavior, often with retries or serialization.
- Corrections: an append-only history generally records a compensating event rather than silently rewriting the past.
- Operational complexity: durable event streams, projections, and recovery paths are additional systems to build and monitor. A reducer alone avoids that persistence commitment, though it does not solve multiplayer networking or authority.
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