CAP Theorem (Consistency, Availability, Partition Tolerance)

Introduction

In distributed system design, ensuring reliability and efficiency is challenging. CAP Theorem, introduced by Eric Brewer in 2000, helps architects understand the trade-offs in distributed databases and how to choose the right system based on business needs.

What is the CAP Theorem?

The CAP Theorem states that in a distributed system, it is impossible to achieve all three of the following properties simultaneously:

1. Consistency (C) – Every read receives the latest write or an error.
2. Availability (A) – Every request gets a response (even if it is outdated).
3. Partition Tolerance (P) – The system continues to operate despite network failures.

Since network failures (partitions) are inevitable in distributed systems, we can only choose between Consistency and Availability.

Understanding CAP Theorem Properties

1️⃣ Consistency (C)

• Every read receives the most recent write.
• If data is written to one node, all nodes should reflect the same data.
• Example: Traditional SQL databases (e.g., PostgreSQL, MySQL with ACID transactions) prioritize consistency.

✅ Pros: Reliable and accurate data across nodes.
❌ Cons: Can lead to delays or errors when network issues occur.

2️⃣ Availability (A)

• Every request receives a response, even if the data is stale.
• The system does not fail, even when some nodes are unreachable.
• Example: NoSQL databases like Cassandra prioritize availability.

✅ Pros: High uptime and responsiveness.
❌ Cons: Some data may be outdated.

3️⃣ Partition Tolerance (P)

• The system continues to function even when network communication is lost.
• A must-have for distributed databases.
• Example: Almost all modern databases (MongoDB, Cassandra, DynamoDB) focus on partition tolerance.

✅ Pros: Ensures the system stays operational despite failures.
❌ Cons: Requires trade-offs between consistency and availability.

CAP Theorem in Action: CP vs. AP Systems

Since Partition Tolerance (P) is essential, systems must choose between Consistency (CP) and Availability (AP):

| Type | Description | Example Databases | |------|------------|-------------------| | CP (Consistency + Partition Tolerance) | Ensures consistency at the cost of availability. If a network partition occurs, some nodes may become unavailable. | MongoDB (default), HBase, Redis (in master-slave mode) | | AP (Availability + Partition Tolerance) | Ensures availability but may return outdated data. Useful for high-read, high-availability applications. | Cassandra, DynamoDB, CouchDB |

📌 Note: Some databases allow tuning CAP properties. For example, MongoDB can be CP by default but can be tuned for AP in specific configurations.

Real-World Use Cases

✅ Choose CP (Consistency + Partition Tolerance) If:

• Your application requires strict consistency, such as banking or financial transactions.
• You cannot tolerate stale or outdated data (e.g., stock trading systems).
• Example: SQL databases, MongoDB (default).

✅ Choose AP (Availability + Partition Tolerance) If:

• Your system needs to be highly available even with network failures.
• You can tolerate eventual consistency (e.g., social media feeds, recommendation systems).
• Example: Cassandra, DynamoDB, CouchDB.

Conclusion

The CAP Theorem is a fundamental principle in distributed systems that helps engineers balance Consistency, Availability, and Partition Tolerance based on application needs. Since network failures are unavoidable, distributed databases must compromise between CP (Consistency + Partition Tolerance) and AP (Availability + Partition Tolerance). Understanding these trade-offs allows developers to choose the right database architecture.

🚀 What type of system are you designing—CP or AP? Let us know in the comments! 🚀