CAP Theorem in DBMS

The CAP Theorem is an important concept in distributed database systems that helps architects and designers understand the trade offs while designing a system.

Introduction:

What is CAP THEOREM?

Almost a couple of decades ago ,Eric brewer introduced the idea that there exist a tradeoff among consistency , availability and partition tolerance. This tradeoff is Commonly known as CAP Theorem and has been extensively discussed ever since .

The CAP Theorem states that disturbuted database can have at most two of these properties

Consistency, Availability and Partition tolerance .

One can make trade-offs between the three parameters based on the system requirements.

Table of content:

• Consistency
• Availability
• Partition tolerance
• Importance of CAP Theorem
• CAP Theorem Database Architecture

Consistency:

In CAP Theorem , Consistency means that all nodes in a distributed database system should have the same data at any given point . This ensures that all clients accessing the system will see the same data , regardless of the node they are connected to . When a client performs a read operation ,they should recieve the most recent write value.

To maintain consistency, the node where the write operation is performed has the responsibility of instantly replicating the data to all other nodes. So , Consistency is crucial for preserving the integrity and accuracy of the data in a distributed database system .

Availability:

In the CAP Theorem , Availability means : A distributed database system should always be able to respond to clients requests , so when a client requests data ,it should recieve a response , regardless of any state of the individual node i.e system must be operational at all times .

Ensuring availability is important for maintaining functionality and usability . To achieve availability, a distributed system must have redundant nodes or mechanism in place to ensure it can continue to operate even if some nodes fall .

Partition Tolerance:

Sometimes due to some disruption in the network , nodes get seperated into groups such that they cannot communicate with each other . This is called network partition , In the CAP Theorem , partition tolerance means: A distributed database system should continue to work even if there is a network partition .

This is one essential requirements because it allows the system to handle network partition without compromising availability or consistency. To achieve this we can use approaches like replication , redundancy, consensus Algorithm ,load balancing.

Importance of CAP Theorem:

In a distributed database system , network failures can disrupt the systems ability to access and update data . Therefore partition tolerance is a must have requirement .But achieving partition tolerance often requires making tradeoffs with consistency and availability. Why ?

Maintaining consistency can come at a cost of increased latency and reduced availability as the system may need to wait for all nodes to agree on the current state of the data before responding to clients requests . Meanwhile maintaining availability can lead to data inconsistency or conflicts , as nodes may return different or stale version of the data .

CAP Theorem Database Architecture:

In practice, different distributed database systems make different trade-offs based on their specific use cases and requirements.

For example:

Relational Databases (e.g., MySQL) typically prioritize consistency and availability (CA) because they are often used in situations where strong consistency is crucial.

NoSQL Databases (e.g., Cassandra) often prioritize availability and partition tolerance (AP) because they are designed to handle large-scale distributed data with a tolerance for eventual consistency.

NewSQL Databases (e.g., Google Spanner) aim for a balance between CA and CP by using advanced techniques like synchronized clocks to provide strong consistency while maintaining high availability and partition tolerance.

CA: The system prioritizes consistency and availability but doesn't handle network partitions well. In the case of a network partition, it might choose to become unavailable to maintain consistency.

CP: The system prioritizes consistency and partition tolerance but sacrifices availability. In the face of a network partition, it will maintain consistency by refusing some requests.

AP: The system prioritizes availability and partition tolerance but sacrifices strong consistency. It continues to operate and respond to requests even if it means returning stale data or data that is not consistent across all nodes.

In conclusion, Understanding the CAP theorem helps architects and developers make informed decisions when selecting a database system for their specific application and use case, taking into account the desired trade-offs between consistency, availability, and partition tolerance.