CAP Theorem

Aniket Patel — Tue, 03 Sep 2024 07:48:47 +0000

It is impossible for a distributed data store to provide all three guarantees simultaneously.

1. Consistency (C): Every read receives the most recent write or an error.

This means that all working nodes in a distributed system will return the same data at any given time.
Consistency is crucial for applications where having the most up-to-date data is critical: Banking System

2. Availability (A): It guarantees that every request (read/write) receives a response, without ensuring that it contains the more recent writes.

This ensures that the system remains operational and responsive, even if responses from some of the nodes don't reflect the most up-to-date.
Availability is crucial for applications requiring continuous operation, such as online retail systems.

3. Partition Tolerance (P): It means that the system can continue to function even if some of the nodes in the network are unable to communicate with each other due to network partitions.

A network partition occurs when a network failure causes a distributed system to split into 2 or more groups of nodes that cannot communicate with each other.

When there is a network partition, the system must choose between Consistency and Availability.

The CAP Trade-off: Choosing 2 out of 3

1. CP (Consistency and Partition Tolerance): Traditional relational databases, such as MySQL and PostgreSQL, when configured for strong consistency, prioritize consistency over availability during network partitions.

When a system is being partitioned, it might reject certain requests to preserve consistency.
Example: Banking Systems typically prioritize consistency and accuracy of data over availability. Consider an ATM network for a bank. When you withdraw money, the system must ensure that your balance is updated accurately across all nodes (consistency) to prevent overdrafts or other errors.

2. AP (Availability and Partition Tolerance): NoSQL databases such as Cassandra and DynamoDB are designed to prioritize high availability and partition tolerance, even at the expense of strong consistency.

Example: Amazon's shopping cart is designed to always accept items which is like prioritizing the availability, even during the pick high traffic periods like Black Friday.

3. CA (Consistency and Availability): In the absence of network partitions, a system can be consistent and available. However, network partitions are inevitable in distributed systems, making this combination impractical.

Example: In the world of distributed systems, the quest for a database that offers both consistency and availability is a challenging one. Single-node databases excel in providing these qualities but falter when it comes to tolerating partitions. This creates a theoretical impasse in a distributed environment.

Forem: Aniket Patel

CAP Theorem

The CAP Trade-off: Choosing 2 out of 3