Understanding Eventual Consistency ✨

What is Eventual consistency? 🤔

In a distributed data warehouse system, Updates made to the data stored are eventually reflected to data stored in all the nodes.

This means that while there might be a temporary period where different nodes show different versions of the data, all nodes will eventually be synchronized to reflect the same updated data.

This approach prioritizes high availability and scalability over strict real-time consistency.

Temporary inconsistencies ⭐

Different nodes in a distributed data warehouse may have slightly different versions of the data at any given time due to asynchronous updates.

Asynchronous replication 🎈

Data updates are propagated to different replicas without requiring immediate synchronization, allowing for faster operations.

Why is Consistency needed? 👀

Consistency ensures that all users, applications, or services interacting with the system see a unified and up-to-date view of the data, regardless of which node they access.

Users performing async actions 🎉

Another common pattern is when users perform async operation. A command is sent to perform an action, and an async operation kicks in to process the action. The user then goes to read the information but it’s not complete yet or returns old stale information, this means the task or data has not yet been replicated from where the user / api is reading it from.

When data is inconsistent it can affect your users experience (users reading their own writes), so it’s worth considering UX patterns you can introduce to help (eg webhooks back to client). Also, it’s worth asking yourself does your business mind if these tasks are async and eventually consistent? Like most things, trade-offs need to be considered.

Performance vs. Consistency 🍀

In a strong consistency model, a system guarantees that once a write operation is completed, any subsequent read operation will reflect that write. In other words, all users see the same data at the same time.

Strong consistency prioritizes consistency which can affect performance and scalability. Eventual consistency prioritizes performance and availability, with a trade-off in immediate data consistency.

Applications that require strict data accuracy (like financial systems) typically opt for strong consistency, while applications that can tolerate some temporary inconsistency for better performance and availability (like social media feeds) might choose eventual consistency.

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