CAP theorem
Computer scientist Eric Brewer, states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees:
- Consistency: Every read receives the most recent write or an error
- Availability: Every request receives a (non-error) response – without the guarantee that it contains the most recent write
- Partition tolerance: The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes
The primary goal of distributed systems is to scale better. In an ideal world, the data shared by loosely coupled services could be replicated without any trouble. That would require optimal consistency, Availability, and Partition Tolerance
No distributed system is safe from network failures, thus network partitioning generally has to be tolerated. In the presence of a partition, one is then left with two options: consistency or availability.
When choosing consistency over availability, the system will return an error or a time-out if particular information cannot be guaranteed to be up to date due to network partitioning
When choosing availability over consistency, the system will always process the query and try to return the most recent available version of the information, even if it cannot guarantee it is up to date due to network partitioning.
However, consistency itself has many levels. Distributed database technologies such as Azure Cosmos DB supports five of them. Google Cloud Spanner technology, on the other hand, is challenging the CAP theorem by claiming to offer high consistency along with availability and partition tolerance. We need to keep these conditions in mind while deciding on database technologies for our systems.