The Cap Theorem

What is the CAP theorem?

Before talk about CAP theroem. we need to know about distributed system. and why.

As you know in the mobile era, the amount of request and data has increased exponentially.

In accordance with this situation, in the database environment, there are also requirements that can be easily extended and data must be delivered quickly.

A distributed system environment was devised to solve these requirements.

The CAP theorem is also called Brewer’s Theorem, because it was first advanced by Professor Eric A. Brewer during a talk he gave on distributed computing in 2000.

Two years later, MIT professors Seth Gilbert and Nancy Lynch published a proof of “Brewer’s Conjecture.”

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The CAP theorem

Let’s take a look at the three distributed system characteristics to which the CAP theorem refers.

Consistency

It means that all clients see the same data at the same time.

This word can be interpreted in several meanings. please don't be confused with ACID's consistency.

From a database point of view

It's meaning `transaction`. The transaction is unit of interaction in such database system. actually, in database transaction has ACID properties.

From an atomic point of view

Single request/response operation sequence.
All clients see the same data at the same time.

Availability

It means that that any client making a request for data gets a response, even if one or more nodes are down.

Another way to state this—all working nodes in the distributed system return a valid response for any request, without exception

Partition tolerance

A partition is a communications break within a distributed system—a lost or temporarily delayed connection between two nodes. Partition tolerance means that the cluster must continue to work despite any number of communication breakdowns between nodes in the system.

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CAP theorem NoSQL database types

These days NoSQL ( Not Relationship ) databases consider horizontal scale not only vertical. also, they can rapidly scale across a growing network consisting of multiple interconnected nodes.

There are several types of base on the two CAP properties.

• CP Database : A CP database delivers consistency and partition tolerance at the expense of availability. When a partition occurs between any two nodes, the system has to shut down the non-consistent node (i.e., make it unavailable) until the partition is resolved.
• AP Database : An AP database delivers availability and partition tolerance at the expense of consistency. When a partition occurs, all nodes remain available but those at the wrong end of a partition might return an older version of data than others. ( When the partition is resolved, the AP databases typically resync the nodes to repair all inconsistencies in the system )
• CA Database : A CA database delivers consistency and availability across all nodes. It can’t do this if there is a partition between any two nodes in the system, however, and therefore can’t deliver fault tolerance. ( fault tolerance is the property that enables a system to continue operating properly in the event of the failure of some of its components. )

as you know in distributed system partitions can’t be avoided. so, a CA distributed database can’t exist. but, this doesn’t mean you can’t have a CA database for your distributed application if you need one. Many relational databases, such as PostgreSQL, deliver consistency and availability and can be deployed to multiple nodes using replication. and also sharding.

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MongoDB and the CAP theorem ( CP )

MongoDB is a popular NoSQL database management system that stores data as BSON (binary JSON) documents. It's frequently used for big data and real-time applications running at multiple different locations. Relative to the CAP theorem, MongoDB is a CP data store—it resolves network partitions by maintaining consistency, while compromising on availability.

MongoDB is a single-master system—each replica set can have only one primary node that receives all the write operations. All other nodes in the same replica set are secondary nodes that replicate the primary node's operation log and apply it to their own data set. By default, clients also read from the primary node, but they can also specify a read preference that allows them to read from secondary nodes.

When the primary node becomes unavailable, the secondary node with the most recent operation log will be elected as the new primary node. Once all the other secondary nodes catch up with the new master, the cluster becomes available again. As clients can't make any write requests during this interval, the data remains consistent across the entire network.

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Casandria ( AP )

Apache Cassandra is an open source NoSQL database maintained by the Apache Software Foundation. It’s a wide-column database that lets you store data on a distributed network. However, unlike MongoDB, Cassandra has a masterless architecture, and as a result, it has multiple points of failure, rather than a single one.

Relative to the CAP theorem, Cassandra is an AP database—it delivers availability and partition tolerance but can't deliver consistency all the time. Because Cassandra doesn't have a master node, all the nodes must be available continuously. However, Cassandra provides eventual consistency by allowing clients to write to any nodes at any time and reconciling inconsistencies as quickly as possible.

As data only becomes inconsistent in the case of a network partition and inconsistencies are quickly resolved, Cassandra offers “repair” functionality to help nodes catch up with their peers. However, constant availability results in a highly performant system that might be worth the trade-off in many cases.

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Conclusion

Looking at the databases based on the CAP theory in a distributed system, we could better understand the advantages of each database. Finally, I would like to express my gratitude to IBM for organizing the CAP theory, distributed processing system, and database.

Stay Hunger, Stay Foolish

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Appendix

• IBM CAP theorem
• Consistency
• MongoDB