Eventual Consistency in Practice

This guide explains what eventual consistency means in the context of event-sourced systems and how to design applications that behave correctly even when data is temporarily out of sync. It explores where consistency gaps can occur, how they affect the user experience, and what strategies can be applied to deal with them effectively.

Eventual consistency is not a flaw or limitation. It is a fundamental property of distributed systems and asynchronous architectures — especially when events are used to decouple write models, read models, and downstream consumers. Embracing it helps build scalable, resilient, and responsive systems.

Understanding the Cause

In an event-sourced system like EventSourcingDB, each write represents a fact that is stored immediately and consistently. Thanks to strong guarantees such as atomic writes and global ordering, the database itself is always in a valid state. However, most applications do not operate solely on this write model. They use projections and read models to query data, build user interfaces, or integrate with other services.

These read models are usually updated asynchronously. They consume events either by polling the database or by observing streams in real time. Because of this separation, a short delay may occur between when an event is stored and when it is reflected in a derived view. This delay is the essence of eventual consistency.

The same applies to external consumers, such as notification services, reporting tools, or search engines. They may observe the event with some latency, depending on how quickly they consume and process the event stream.

Where Inconsistencies Show Up

The most visible consequence of eventual consistency is that users may see stale or incomplete data, especially in highly interactive applications. This can lead to surprising situations like:

• A user performs an action, but the UI does not immediately reflect the change.
• A confirmation message appears, but the corresponding item does not yet show up in a list.
• A read model temporarily shows outdated values, even though a change has already occurred in the event stream.

Such gaps are not technical errors. They are expected and acceptable — provided they are understood and managed correctly.

Designing Around Eventual Consistency

To work effectively with eventual consistency, the key is to design systems that are predictable, transparent, and resilient. There are several approaches that can help reduce confusion and maintain user trust.

The first and simplest option is to provide feedback optimistically. If the user performs an action that triggers an event, the UI can immediately reflect the expected result, even before the read model has caught up. This is safe as long as the system is built around idempotent updates and compensating behavior in rare failure cases.

Another strategy is to design task-oriented interfaces rather than state-oriented ones. Instead of trying to show the entire system state at every moment, the interface focuses on guiding the user through tasks. Once a task is completed, the UI moves forward, even if some information is not immediately refreshed. This reduces the perceived inconsistency and avoids redundant refresh cycles.

It also helps to display loading indicators or “pending” states when appropriate. For example, if a user submits a form and the change is not yet visible, the UI can show that the update is in progress. This sets expectations correctly and reduces confusion.

From a domain perspective, it is important to understand which inconsistencies are tolerable and which ones are not. For example, delayed updates in a user profile view may be acceptable, while inconsistencies in financial transactions or inventory counts may not. In critical areas, additional safeguards such as idempotent retries, compensating actions, or confirmation workflows may be needed.

Technical Patterns That Help

EventSourcingDB provides several features that make working with eventual consistency more predictable. Observing event streams allows consumers to receive updates in near real time. By tracking the ID of the last seen event, clients can reconnect after a failure and resume consumption without gaps.

Snapshots can improve performance but do not fundamentally change consistency behavior. They are useful for replay scenarios and aggregate loading, but read models built from events are still asynchronous by nature. The key point is that replaying the same event stream always leads to the same state — just not necessarily at the same time everywhere.

For clients that require stronger consistency at specific moments — for example, during a critical workflow — it is possible to design coordination mechanisms. These might include waiting for a projection to be updated, using acknowledgments, or explicitly checking for consistency before proceeding. However, such mechanisms increase complexity and should be applied selectively.

Embracing the Trade-Off

Eventual consistency is not something to be avoided — it is something to be designed for. It allows systems to scale, decouple, and remain responsive under load. Instead of aiming for immediate consistency everywhere, it is more useful to model the domain so that temporary inconsistencies are either irrelevant or clearly communicated.

This requires collaboration between developers, designers, and domain experts. The goal is not to hide consistency gaps but to make them understandable and manageable. Good system design acknowledges that events take time to propagate, and that the truth may arrive with a delay — but will arrive with certainty and clarity.

EventSourcingDB provides a solid foundation for this kind of architecture. By offering strong consistency at the event level and reliable mechanisms for event observation and replay, it allows teams to build systems that are both correct and eventually consistent — without compromising on either.