Brewer’s CAP Theorem

Brewer’s CAP theorem is a concept in distributed computing that states that it is impossible for a distributed system to simultaneously provide all three of the following guarantees:

• Consistency: Every read request returns the most recent write or an error.
• Availability: Every request receives a response without a guarantee that it contains the most recent version of the information.
• Partition tolerance: The system continues to function despite arbitrary partitioning due to network failures.

The theorem provides a way of thinking about the trade-offs involved in designing and building distributed systems, and it helps to explain why certain types of systems may be more appropriate for certain use cases. According to Brewer, the theorem states that a distributed system can have at most two of these guarantees.

History and Origin: The theorem was first described by Brewer in 2000 while he was a researcher at UC Berkeley, and it has since become a widely cited concept in distributed systems. 

Remember: It is named after computer scientist Eric Brewer.

Brewer’s CAP theorem is not a formal proof, but rather a theoretical construct meant to highlight the difficulties involved in building highly available, highly consistent distributed systems. However, it has been widely adopted as a tool for understanding the trade-offs involved in distributed systems design and has become a fundamental part of the field of distributed computing. 

Usage of Theorem: It is often used to compare and contrast different types of databases and their suitability for different use cases. Different databases prioritize consistency, availability, and partition tolerance differently, which affects their behavior and performance in different scenarios.

Some Popular Use-cases 

Here are some examples of databases and how they compare to the guarantees of Brewer’s CAP theorem:

1. Relational databases: These databases typically prioritize consistency over availability and partition tolerance. They guarantee that every read returns the most recent write, but may become unavailable during network failures or partitioning.
Examples: MySQL, PostgreSQL

2. NoSQL databases: These databases typically prioritize availability and partition tolerance over consistency. They may return stale data during network failures or partitioning, but they are designed to remain available and continue functioning.

Examples: MongoDB, Cassandra

3. NewSQL databases: These databases attempt to provide a balance between consistency, availability, and partition tolerance. They use advanced distributed systems techniques to provide strong consistency guarantees while also providing high levels of availability and partition tolerance.

Examples: Google Cloud Spanner, CockroachDB

It is important to note that different databases can be configured and optimized in different ways, and the trade-offs involved in choosing a database will depend on the specific requirements of the application. Brewer’s CAP theorem provides a way of thinking about these trade-offs and helps to guide decision-making when choosing a database for a particular use case.

Brewer’s CAP theorem has been the subject of much debate and criticism in the distributed computing community. Some of the problems and alternative perspectives are:

• The theorem is not a proof: Brewer’s CAP theorem is not a formal proof, but rather a theoretical construct meant to provide a framework for thinking about the trade-offs involved in building distributed systems. This means that the theorem should not be used as the sole basis for making design decisions or evaluating the performance of a distributed system.
• The consistency and availability guarantees are not well-defined: The definitions of consistency and availability used in the theorem are not precise, and different people may have different interpretations of what these guarantees mean. This can lead to confusion and misinterpretation of the theorem.
• The partition tolerance guarantee is not applicable to all systems: Not all distributed systems are subject to network failures or partitioning, and the partition tolerance guarantee may not be relevant to some use cases.
• The theorem oversimplifies the trade-offs involved in building distributed systems: Building a highly available, highly consistent distributed system is a complex task, and the trade-offs involved are much more nuanced than the binary choices implied by Brewer’s CAP theorem.

In response to these criticisms, some alternative perspectives on the trade-offs involved in building distributed systems have been proposed. For example, the “PACELC” theorem proposes that the trade-offs involved in building a distributed system can be described by considering both the normal and exceptional operating conditions of the system. The “BASE” (Basically Available, Soft-State, Eventually Consistent) principle proposes that distributed systems should be designed to be available, to allow their state to change over time, and to eventually converge to a consistent state.

In conclusion, while Brewer’s CAP theorem is a useful starting point for understanding the trade-offs involved in building distributed systems, it should not be used as the sole basis for making design decisions or evaluating the performance of a system. It is important to consider a range of alternative perspectives and to understand the specific requirements of the use case when building a distributed system.