---
title: "Top Eventual Consistency Patterns You Must Know"
description: "Explore eventual consistency patterns for distributed database design."
image: "https://assets.bytebytego.com/diagrams/0100-eventual-consistency-patterns-you-must-know.png"
createdAt: "2024-02-15"
draft: false
categories:
  - database-and-storage
tags:
  - "Consistency"
  - "Databases"
---

![](https://assets.bytebytego.com/diagrams/0100-eventual-consistency-patterns-you-must-know.png)

Eventual consistency is a data consistency model that ensures that updates to a distributed database are eventually reflected across all nodes. Techniques like async replication help achieve eventual consistency.

However, eventual consistency can also result in data inconsistency. Here are 4 patterns that can help you design applications.

## Event-based Eventual Consistency

Services emit events and other services listen to these events to update their database instances. This makes services loosely coupled but delays data consistency.

## Background Sync Eventual Consistency

In this pattern, a background job makes the data across databases consistent. It results in slower eventual consistency since the background job runs on a specific schedule.

## Saga-based Eventual Consistency

Saga is a sequence of local transactions where each transaction updates data with a single service. It is used to manage long-lived transactions that are eventually consistent.

## CQRS-based Eventual Consistency

Separate read and write operations into different databases that are eventually consistent. Read and write models can be optimized for specific requirements.