What is CAP?
CAP stands for Consistency, Availability, and Partition tolerance - three guarantees that a distributed data system can try to provide. The CAP theorem says a system can only guarantee two of these three at the same time.
Let's break it down
- Consistency: Every read receives the most recent write or an error, so all nodes see the same data at the same time.
- Availability: Every request gets a response (success or failure) without waiting for other nodes, so the system is always up.
- Partition tolerance: The system keeps working even if network links between some nodes break (a “partition” occurs).
Why does it matter?
Understanding CAP helps developers design systems that behave predictably under failures, choose the right trade-offs for their app, and avoid surprises like lost data or downtime.
Where is it used?
- Cloud databases (e.g., Amazon DynamoDB chooses availability and partition tolerance).
- Distributed caches like Redis Cluster prioritize availability and partition tolerance.
- Global file storage services (e.g., Google Cloud Spanner aims for consistency and partition tolerance).
- Messaging platforms such as Apache Kafka balance availability and partition tolerance while offering strong ordering guarantees.
Good things about it
- Provides a clear framework for thinking about trade-offs in distributed design.
- Helps teams pick the right database or architecture for their specific needs.
- Encourages building resilient systems that can survive network failures.
- Makes it easier to explain complex behavior to non-technical stakeholders.
Not-so-good things
- The theorem is a simplification; real systems can offer “soft” guarantees that blur the lines.
- Focusing only on two properties may hide other important concerns like latency or security.
- Some implementations claim to achieve all three, but they often sacrifice performance or add hidden complexity.
- It can lead to over-engineering if teams try to optimize for a property they don’t actually need.