ScalingConsistency Patterns

What Are Consistency Patterns?

Consistency patterns are a set of techniques that can be used to achieve consistency in a distributed system. Consistency is the property of distributed systems that guarantees that all nodes in the system see the same data at the same time. This is important because it allows different parts of the system to remain synchronized with each other. There are many different ways to achieve consistency, and each has its own trade-offs. The most common way to achieve consistency is through replication, where data is copied to multiple nodes in the system. This ensures that if one node goes down, the others will still have a copy of the data. However, replication comes with its own set of challenges, such as ensuring that all replicas are kept up-to-date with each other. 

In order for the data in a distributed system to be consistent, it must be synchronized across all of the computers in the system. That's where consistency patterns come in. 

There are three main types of consistency patterns: 

• Eventual Consistency
• Strong Consistency 
• Causal(Weak) Consistency 

Eventual Consistency 

Eventual consistency is the weakest form of consistency. With eventual consistency, changes made to the data on one computer may not be immediately reflected on other computers in the system. However, given enough time, the data will eventually become consistent across all computers in the system. Eventual consistency is often used in systems where high availability is more important than immediate consistency, such as social media applications. 

Strong Consistency 

Strong consistency is a stronger form of consistency than eventual consistency. With strong consistency, changes made to the data on one computer are immediately reflected on other computers in the system. Strong consistency is often used in systems where immediate consistency is more important than high availability, such as banking applications. 

Causal Consistency 

Causal consistency is a weaker form of strong consistency. With causal consistency, changes made to the data on one computer are not immediately reflected on other computers in the system. However, given enough time, the data will eventually become consistent across all computers in the system. Causal consistency is often used in systems where high availability and immediate consistency are both important, such as e-commerce applications. 

Which to use

When it comes to deciding which pattern to use for your application, it's all about trade-offs. If you need your data to be immediately consistent across all computers in the system, then strong consistency is probably your best bet. However, if you're willing to sacrifice some immediacy for improved availability, then eventual or causal consistency may be more suitable for your needs.