CAP Theorem in Distributed Systems

Understanding the Trade-offs in Distributed Systems: Consistency, Availability, and Partition Tolerance

The CAP theorem asserts that in a distributed system, you can only achieve two out of the following three properties simultaneously:

1. Consistency (C): Ensures that every read operation returns the most recent write or an error, maintaining uniform data across nodes.

2. Availability (A): Guarantees that every request receives a response, even if it doesn’t reflect the most recent data.

3. Partition Tolerance ¹ (P): Allows the system to keep functioning even when network partitions prevent nodes from communicating.

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Because network partitions are inevitable in real-world systems, you’re forced to make a trade-off between consistency and availability when partitions occur.
During a network partition, each segment (or partition) of the system may still be operational, but they are unaware of the state or data on the other segments. This leads to challenges in maintaining consistency and availability because:

• Consistency requires nodes to stay synchronized, which may not be possible during a partition.

• Availability requires nodes to respond to requests, even if they are isolated.

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CAP Trade-offs

1. CP (Consistency + Partition Tolerance)

   1. Prioritizes consistent data, even during network partitions.

   2. Availability is reduced, meaning the system may respond with an error or become temporarily unavailable.

   3. Use Cases: Ideal for applications requiring accurate data, like financial transactions or systems needing atomic operations (e.g., bank balances).

2. AP (Availability + Partition Tolerance)

   1. Ensures the system remains available despite network partitions.

   2. Sacrifices strict consistency, but the system can achieve eventual consistency once the partition is resolved.

   3. Use Cases: Suitable for applications where availability is critical, like social media feeds, caches, or real-time messaging services.
      

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Choosing Between CP and AP

• CP Systems: Best for scenarios where data accuracy is paramount, such as banking and payment processing.

• AP Systems: Ideal when the system must remain responsive, even if some data is stale, such as in large-scale content delivery networks or caching systems.

  

1. A network partition occurs when a failure in the network causes a distributed system to split into separate, isolated segments. In this state, some nodes cannot communicate with others, even though they may still be up and running. This can happen due to various reasons, such as:

• Hardware failures (e.g., a faulty router or switch)

• Network congestion or latency issues

• Data center outages

• Geographic distribution challenges