CAP Theorem (Consistency - Availability - Partition Tolerance)
- states that a distributed system cannot simultaneously guarantee all three properties:
- Consistency
- Availability
- Partition Tolerance
- this means that when designing distributed systems, developers must make trade-offs, choosing which two properties are most important for their application
CAP - Introduction
CAP Properties
| Consistency | - all nodes in the system see the same data at the same time
- this means all reads receive the most recent write or an error
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| Availability | - every request receives a (non-error) response, even if the data is not the most recent
- this ensures that the system is always operational and can respond to requests, even if some nodes are unavailable
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| Partition Tolerance | - the system continues to operate despite network partitions or communication failures between nodes
- this means the system can handle situations where nodes are disconnected from each other due to network issues
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CAP Trade-Offs
| CP (Consistency and Partition Tolerance) | - Sacrifices availability
- The system prioritizes keeping data consistent even during network partitions, but may become unavailable or have limited availability if nodes are disconnected
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| AP (Availability and Partition Tolerance) | - Sacrifices consistency
- The system prioritizes being available and continuing to operate during network partitions, but may not guarantee data consistency across all nodes
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| CA (Consistency and Availability) | - Cannot guarantee partition tolerance
- In a distributed setting, this combination is theoretically impossible because network partitions are inevitable
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In summary, when designing a distributed system, you must choose which two of these properties are most important for your application and its specific needs, understanding that you will need to make a trade-off and sacrifice the third property.