CAP Theorem
As a CTO, you need to balance trade-offs between Consistency, Availability, and Partition Tolerance when designing distributed systems. The CAP theorem formulated by computer scientist Eric Brewer states that in any distributed system, you can only guarantee two out of three properties at any given time.
Breaking Down CAP
1. Consistency (C)
Every node in the system always has the latest data. A read request will return the most recent write.
- Implication: Strong consistency often means sacrificing availability (e.g., using leader-based replication).
- Example: Traditional relational databases (e.g., PostgreSQL with strong ACID properties).
2. Availability (A)
Every request gets a response, even if some nodes are down. However, the response may not be the most recent version of the data.
- Implication: Prioritizing availability means some nodes might serve stale data.
- Example: NoSQL databases like Cassandra or DynamoDB favor availability over strict consistency.
3. Partition Tolerance (P)
The system continues to function despite network partitions (i.e., some nodes can't communicate with others).
- Implication: Any practical distributed system must be partition-tolerant, so the trade-off is usually between C and A.
- Example: If a data center loses connectivity, does the system halt (favoring C) or keep serving responses (favoring A)?
Real-World Considerations
| Industry/System | CAP Preference | Example |
|---|---|---|
| Financial systems (banking, transactions) | CP (Consistency + Partition Tolerance) | A payment system must not allow double spending, even if it means temporary downtime. |
| E-commerce & social media | AP (Availability + Partition Tolerance) | Amazon or Facebook can serve slightly stale data, but the site must always be available. |
| Hybrid architectures | Eventual Consistency (balancing C and A) | AWS DynamoDB (AP with eventual consistency but configurable strong consistency). |