Understanding the CAP Theorem in Distributed Systems

In a last few weeks while learning about details of distributed systems I came across an important concept. Although many of us may be familiar with it, a quick refresher can be valuable.

What is CAP?

In distributed systems (where data is spread across multiple servers), there are three key goals:

• Consistency (C): Every user sees the same data, no matter which node they connect to.
• Availability (A): The system always responds, even if some nodes are down.
• Partition Tolerance (P): The system keeps working even if there’s a network problem separating some nodes.

The CAP Theorem

The CAP Theorem says you can’t have all three of these goals at the same time.

Here’s why:

1. Consistency + Availability (CA): In a perfect world with no network issues, you could have both. But since network problems cannot be avoided, this isn’t realistic.
2. Consistency + Partition Tolerance (CP): If network failure occur, availability must be sacrificed to maintain consistency.
3. Availability + Partition Tolerance (AP): When network failure happen, consistency is compromised to ensure availability.

Why Do We Need to Choose?

When a network problem happens, we should decide: do we care more about everyone seeing the same data (consistency) or keeping the system running (availability)?

• Example 1: Choosing Consistency If consistent data is more important than availability, the system must restrict writes during the network problem. This avoids the risk of stale data, as there’s no need to replicate updates to the failed node until it’s back online.
• Example 2: Choosing Availability On the other hand, choosing availability means some clients may see stale data due to the network problem. For example, if nodes N1 and N2 are accepting writes but can’t propagate them to N3, clients connected to N3 may see outdated data until the partition is resolved.

In Conclusion

The CAP Theorem reminds that in distributed systems, we can’t have it all. We should choose what’s more important for our needs: consistent data or always being available. Understanding these trade-offs helps us make better decisions when designing systems.