The CAP theorem, originally introduced as the CAP principle, can be used to explain some of the competing requirements in a distributed system with replication. It is a tool used to makes system designers aware of the trade-offs while designing networked shared-data systems.
The three letters in CAP refer to three desirable properties of distributed systems with replicated data: consistency (among replicated copies), availability of the system for read and write operations) and partition tolerance in the face of the nodes in the system being partitioned by a network fault).
The CAP theorem states that it is not possible to guarantee all three of the desirable properties – consistency, availability, and partition tolerance at the same time in a distributed system with data replication.
The theorem states that networked shared-data systems can only strongly support two of the following three properties:
- Consistency –
Consistency means that the nodes will have the same copies of a replicated data item visible for various transactions. A guarantee that every node in a distributed cluster returns the same, most recent, successful write. Consistency refers to every client having the same view of the data. There are various types of consistency models. Consistency in CAP refers to sequential consistency, a very strong form of consistency.
- Availability –
Availability means that each read or write request for a data item will either be processed successfully or will receive a message that the operation cannot be completed. Every non-failing node returns a response for all read and write requests in a reasonable amount of time. The key word here is every. To be available, every node on (either side of a network partition) must be able to respond in a reasonable amount of time.
- Partition Tolerant –
Partition tolerance means that the system can continue operating if the network connecting the nodes has a fault that results in two or more partitions, where the nodes in each partition can only communicate among each other. That means, the system continues to function and upholds its consistency guarantees in spite of network partitions. Network partitions are a fact of life. Distributed systems guaranteeing partition tolerance can gracefully recover from partitions once the partition heals.
The use of the word consistency in CAP and its use in ACID do not refer to the same identical concept.
In CAP, the term consistency refers to the consistency of the values in different copies of the same data item in a replicated distributed system. In ACID, it refers to the fact that a transaction will not violate the integrity constraints specified on the database schema.
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- Various properties of CAP Theorem
- Lossless Decomposition in DBMS
- Introduction of Relational Algebra in DBMS
- Need for DBMS
- Commonly asked DBMS interview questions | Set 1
- Normal Forms in DBMS
- View Serializability in DBMS Transactions
- Relational Model in DBMS
- Commonly asked DBMS interview questions | Set 2
- Concurrency Control in DBMS
- Recoverability in DBMS
- Last Minute Notes - DBMS
- ACID Properties in DBMS
- DBMS Architecture 2-Level, 3-Level
- Introduction of DBMS (Database Management System) | Set 1
- Introduction of 3-Tier Architecture in DBMS | Set 2
- Introduction of Relational Model and Codd Rules in DBMS
- Precedence Graph For Testing Conflict Serializability in DBMS
- Canonical Cover of Functional Dependencies in DBMS
- Thomas Write Rule in DBMS
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