The CAP Theorem asserts that in the presence of a network partition. A distributed system must choose between consistency and availability. It cannot guarantee both. This is because:
If you prioritize Consistency and Partition tolerance (CP):
When a network partition occurs, and communication accurate cleaned numbers list from frist database between nodes is broken, nodes on different sides of the partition might have conflicting updates.
To maintain consistency, the system must refuse requests (become unavailable) on one side of the partition until the partition is resolved and data can be synchronized.
This ensures that all data remains consistent, Many traditional relational databases that are sharded across multiple machines often fall into this category.
They prioritize strong consistency and thus sacrifice availability during network partitions.
If you prioritize Availability and Partition tolerance (AP):
When a network partition occurs, the system the diverse nature of modern data also plays a significant role continues to accept requests and provide responses on both sides of the partition.
This means that nodes on different sides of the partition might process updates independently, leading to divergent data. While the system remains available, consistency is sacrificed.
The system will eventually try to reconcile the data once the partition is resolved, but there will be a period of inconsistency.
Many NoSQL databases like Cassandra and Amazon DynamoDB are designed to be AP systems, prioritizing continuous availability over strong consistency.
If you prioritize Consistency and Availability (CA):
This scenario is generally not considered european data practical in a truly distributed system because a distributed system by definition is susceptible to network partitions.
A system that is CA effectively assumes that network partitions will never happen.
Which is an unrealistic assumption for any large-scale, geographically dispersed system.
A single, monolithic database running on a single machine could be CA, but it’s not a distributed system.