CAP Theorem
Visualize the trade-offs between Consistency, Availability, and Partition Tolerance.
Mode
CA
Throughput
0 ops/sec
Availability
0.0%
Data Loss Rate
0.0%
CAP guarantees
Consistent + Available
High Consistency
0.0%
High Availability
0.0%
Partition Tolerant
0.0%
Operation log
Start simulation to see operations.
Performance metrics
Clients / minute: 0.0
Successful ops / minute: 0.0
Failed ops: 0
Data losses: 0
How this simulator works
The CAP theorem states a distributed data store can only provide two of three guarantees: Consistency (strong reads and writes see the latest state), Availability (every request receives a non-error response), and Partition Tolerance (the system continues operating despite arbitrary message loss).
CAP trade-offs
- CA — No partition tolerance. Writers can serve reads immediately, but any network failure causes unavailability.
- AP — High availability, eventual consistency. Allows writes to complete during partitions, reads may see old data.
- CP — Strong consistency, low availability during partitions. Guarantees linearizable reads/writes, but services fail when partitions occur.
Real-world consequences
In practice, all distributed systems experience network partitions. The CAP theorem is about which two guarantees you prioritize when they conflict.