CAP Theorem in Distributed Systems
Posted: Thu May 22, 2025 9:42 am
The CAP Theorem, also known as Brewer's Theorem, is a fundamental principle in distributed systems that outlines the trade-offs between three core system properties: Consistency, Availability, and Partition Tolerance. Introduced by computer scientist Eric Brewer in 2000 and formally proven later, the theorem states that in the presence of a network partition, a distributed system can guarantee only two of the three properties at any given time—not all three simultaneously.
The Three CAP Properties
Consistency (C):
Every read receives the most recent write or an error. This means that all nodes in the system return the same data at a given point in time.
Availability (A):
Every request (read or write) receives a non-error response—even if it is not the most recent data. The system remains operational and responsive.
Partition Tolerance (P):
The system continues to operate despite arbitrary partitioning due to network failures. Messages may be lost or delayed, but the system must handle this without complete failure.
Why All Three Can't Be Achieved Simultaneously
In a distributed system, network partitions are inevitable due iceland phone number list to failures, delays, or disconnections between nodes. When a partition occurs, the system must choose between:
Consistency: Rejecting requests to keep all nodes in sync.
Availability: Serving requests with potentially outdated data to keep the system running.
Thus, a distributed system must sacrifice either consistency or availability during a partition.
CAP Combinations
CP (Consistency + Partition Tolerance):
The system maintains accurate data even during a partition but may become unavailable.
Example: HBase, MongoDB (in strong consistency mode)
AP (Availability + Partition Tolerance):
The system remains responsive but may serve stale data during a partition.
Example: Couchbase, Cassandra
CA (Consistency + Availability):
Possible only in systems that do not experience partitions—essentially single-node systems or tightly coupled clusters.
Real-World Implications
Designing a distributed system requires making conscious trade-offs depending on the application’s needs:
Financial systems may prioritize consistency.
Real-time messaging systems may prioritize availability.
Conclusion
The CAP Theorem is a critical concept for architects and developers building distributed systems. By understanding and applying its principles, one can make informed decisions about how to handle data consistency, system availability, and fault tolerance in the face of network partitions.
The Three CAP Properties
Consistency (C):
Every read receives the most recent write or an error. This means that all nodes in the system return the same data at a given point in time.
Availability (A):
Every request (read or write) receives a non-error response—even if it is not the most recent data. The system remains operational and responsive.
Partition Tolerance (P):
The system continues to operate despite arbitrary partitioning due to network failures. Messages may be lost or delayed, but the system must handle this without complete failure.
Why All Three Can't Be Achieved Simultaneously
In a distributed system, network partitions are inevitable due iceland phone number list to failures, delays, or disconnections between nodes. When a partition occurs, the system must choose between:
Consistency: Rejecting requests to keep all nodes in sync.
Availability: Serving requests with potentially outdated data to keep the system running.
Thus, a distributed system must sacrifice either consistency or availability during a partition.
CAP Combinations
CP (Consistency + Partition Tolerance):
The system maintains accurate data even during a partition but may become unavailable.
Example: HBase, MongoDB (in strong consistency mode)
AP (Availability + Partition Tolerance):
The system remains responsive but may serve stale data during a partition.
Example: Couchbase, Cassandra
CA (Consistency + Availability):
Possible only in systems that do not experience partitions—essentially single-node systems or tightly coupled clusters.
Real-World Implications
Designing a distributed system requires making conscious trade-offs depending on the application’s needs:
Financial systems may prioritize consistency.
Real-time messaging systems may prioritize availability.
Conclusion
The CAP Theorem is a critical concept for architects and developers building distributed systems. By understanding and applying its principles, one can make informed decisions about how to handle data consistency, system availability, and fault tolerance in the face of network partitions.