Consistency (database systems)
Return to ACID (Atomicity, Consistency, Isolation, Durability)
In the context of database systems, consistency refers to the property that ensures that all nodes in a distributed system have the same data after any transaction. Consistency guarantees that once a transaction is completed, all subsequent reads will return the most recent, committed version of the data. This means that a system will not allow transactions that violate data integrity or produce conflicting results. In a distributed system, consistency is one of the critical components of the CAP theorem, where it often comes into conflict with availability and partition tolerance.
https://en.wikipedia.org/wiki/Consistency_(distributed_databases)
For example, a relational database like MySQL or PostgreSQL generally enforces consistency through the use of ACID (Atomicity, Consistency, Isolation, Durability) properties. This ensures that any data modification operation will leave the system in a valid state. In a distributed database, maintaining consistency can be more challenging, particularly when there are network partitions or when multiple copies of the data are distributed across different servers or geographical locations. For systems that prioritize consistency, such as Google Spanner, ensuring that all copies of the data remain synchronized is paramount, even at the cost of availability in some cases.
https://en.wikipedia.org/wiki/ACID
However, systems that prioritize availability or partition tolerance over consistency, such as Cassandra or MongoDB, may sacrifice consistency temporarily in favor of ensuring that the system remains responsive. These systems often employ consistency models such as eventual consistency, which allows data to be temporarily inconsistent across nodes but ensures that all updates will eventually propagate to all nodes. Choosing the appropriate consistency model depends on the specific requirements of the application, as some applications can tolerate temporary inconsistency, while others may require strong consistency at all times.
https://en.wikipedia.org/wiki/Eventual_consistency
Consistency in database systems refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including Integrity constraints | constraints, Cascading rollback | cascades, Database trigger | triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined database constraints.“1)
- Snippet from Wikipedia: Consistency (database systems)
In database systems, consistency (or correctness) refers to the requirement that any given database transaction must change affected data only in allowed ways. Any data written to the database must be valid according to all defined rules, including constraints, cascades, triggers, and any combination thereof. This does not guarantee correctness of the transaction in all ways the application programmer might have wanted (that is the responsibility of application-level code) but merely that any programming errors cannot result in the violation of any defined database constraints.
In a distributed system, referencing CAP theorem, consistency can also be understood as after a successful write, update or delete of a Record, any read request immediately receives the latest value of the Record.