CAP theorem

The CAP theorem, where C stands for Consistency, A for Availability, and P for Partition Tolerance, states that in a distributed system, it is impossible to guarantee all three properties simultaneously. A system can achieve at most two out of the three at any given time. Let's go through each of these terms to better understand the theorem. CAP Consistency Consistency ensures that every request to the system receives the most up-to-date and valid data, meaning that once a data update occurs, all subsequent requests will see the updated data. It guarantees that the data provided is not stale. Availability Availability refers to a system being operational and responsive at all times, even if some of its nodes are not working. Nodes could refer to different components of the system or to full replicas of the system, which are often managed behind a load balancer to distribute traffic. Partition Tolerance Partition Tolerance means the system can continue to operate and handle requests even if network partitions occur, meaning some nodes or parts of the system are unable to communicate with each other. It guarantees that the system will remain functional despite disruptions in network connectivity between nodes. Distributed System A Distributed System is a network of independent computers or nodes that work together to achieve a common goal, often appearing as a single system to users. These nodes collaborate to handle tasks like data storage, processing, and request handling, but they operate in different locations and may communicate over a network. Why CAP theorem exist? The CAP theorem applies specifically to distributed systems. Imagine a system with nodes distributed across multiple geographical locations. In such a system, network partitions can occur, leading to Partition Tolerance. As a result, the system must choose between Consistency or Availability. The reason for this limitation is that if the system prioritizes Consistency (i.e., ensuring data is up-to-date), it requires communication between nodes to synchronize data. This process takes time, during which the system may be unavailable, thereby affecting Availability. On the other hand, if the system prioritizes Availability, it might allow data to become inconsistent by ignoring the latest updates. This keeps the system available without waiting for data synchronization, but it sacrifices Consistency, as different nodes may have outdated or conflicting data. To achieve both Availability and Consistency, the system would need to avoid network partitions, meaning nodes must be able to communicate without delay. However, this would sacrifice Partition Tolerance, as the system could fail to operate properly in the event of network issues.

Apr 6, 2025 - 03:02

The CAP theorem, where C stands for Consistency, A for Availability, and P for Partition Tolerance, states that in a distributed system, it is impossible to guarantee all three properties simultaneously. A system can achieve at most two out of the three at any given time. Let's go through each of these terms to better understand the theorem.

CAP

Consistency

Consistency ensures that every request to the system receives the most up-to-date and valid data, meaning that once a data update occurs, all subsequent requests will see the updated data. It guarantees that the data provided is not stale.

Availability

Availability refers to a system being operational and responsive at all times, even if some of its nodes are not working. Nodes could refer to different components of the system or to full replicas of the system, which are often managed behind a load balancer to distribute traffic.

Partition Tolerance

Partition Tolerance means the system can continue to operate and handle requests even if network partitions occur, meaning some nodes or parts of the system are unable to communicate with each other. It guarantees that the system will remain functional despite disruptions in network connectivity between nodes.

Distributed System

A Distributed System is a network of independent computers or nodes that work together to achieve a common goal, often appearing as a single system to users. These nodes collaborate to handle tasks like data storage, processing, and request handling, but they operate in different locations and may communicate over a network.

Why CAP theorem exist?

The CAP theorem applies specifically to distributed systems. Imagine a system with nodes distributed across multiple geographical locations. In such a system, network partitions can occur, leading to Partition Tolerance. As a result, the system must choose between Consistency or Availability.

The reason for this limitation is that if the system prioritizes Consistency (i.e., ensuring data is up-to-date), it requires communication between nodes to synchronize data. This process takes time, during which the system may be unavailable, thereby affecting Availability.

On the other hand, if the system prioritizes Availability, it might allow data to become inconsistent by ignoring the latest updates. This keeps the system available without waiting for data synchronization, but it sacrifices Consistency, as different nodes may have outdated or conflicting data.

To achieve both Availability and Consistency, the system would need to avoid network partitions, meaning nodes must be able to communicate without delay. However, this would sacrifice Partition Tolerance, as the system could fail to operate properly in the event of network issues.