The CAP theorem, also known as Brewer’s theorem, is a concept in computer science that states that it is impossible for a distributed database to simultaneously provide more than two of the following three guarantees:
-
Consistency: This means that every user sees the same data at the same time.
-
Availability: This means that every request to the database receives a response, regardless of the current state of the system.
-
Partition tolerance: This means that the database can continue to operate, even if there is a failure or disconnection between some of the nodes in the system.
According to the CAP theorem, it is not possible for a distributed database to provide all three of these guarantees at the same time. Instead, a database must choose to prioritize two of the guarantees and potentially sacrifice the third.
For example, a database that prioritizes consistency and availability may sacrifice partition tolerance, meaning that it may become unavailable if there is a failure or disconnection between nodes. Alternatively, a database that prioritizes availability and partition tolerance may sacrifice consistency, meaning that users may see different data at different times.
Overall, the CAP theorem helps to illustrate the trade-offs involved in designing a distributed database, and it serves as a useful framework for understanding the limitations of such systems.
CAP list of databases
Here is a list of some databases and how they relate to the CAP theorem:
-
MySQL: This is a popular relational database management system that is known for its high performance and reliability. MySQL is generally considered to be consistent and available, but it does not offer strong partition tolerance.
-
PostgreSQL: This is a powerful object-relational database management system that is known for its robustness and support for complex SQL. PostgreSQL is generally considered to be consistent and partition tolerant, but it may sacrifice availability in certain failure scenarios.
-
MongoDB: This is a popular NoSQL database management system that is designed to store and retrieve large amounts of data quickly. MongoDB is generally considered to be available and partition tolerant, but it may sacrifice consistency in certain failure scenarios.
-
Cassandra: This is a distributed NoSQL database management system that is designed to be highly available and scalable. Cassandra is generally considered to be available and partition tolerant, but it may sacrifice consistency in certain failure scenarios.
-
Redis: This is an in-memory data store that is designed to provide fast and predictable performance at any scale. Redis is generally considered to be available and partition tolerant, but it may sacrifice consistency in certain failure scenarios.
Overall, different databases prioritize different aspects of the CAP theorem, and it is important to choose the database that best meets the needs of your application and workload.
CAP vs ACID
ACID and CAP are two different concepts that are relevant in the field of database management.
ACID (Atomicity, Consistency, Isolation, Durability) is a set of properties that are used to describe the behavior of a database. These properties are designed to ensure the reliability and consistency of a database, even in the event of failures or errors.
On the other hand, the CAP theorem, also known as Brewer’s theorem, is a concept in computer science that states that it is impossible for a distributed database to simultaneously provide more than two of the following three guarantees: consistency, availability, and partition tolerance.
In summary, ACID is a set of properties that describe the behavior of a database, while the CAP theorem is a concept that applies to distributed databases and explains the trade-offs involved in designing such systems. ACID properties are concerned with ensuring the reliability and consistency of a database, while the CAP theorem deals with the availability and partition tolerance of a distributed database.
CAP with business examples
Here are some examples of how the CAP theorem might apply to business scenarios:
-
Online retail store: An online retail store may prioritize consistency and availability, meaning that every user sees the same data at the same time and can make purchases without interruption. However, this may come at the cost of partition tolerance, as the database may become unavailable if there is a failure or disconnection between nodes.
-
Social media platform: A social media platform may prioritize availability and partition tolerance, meaning that the platform remains accessible and operational, even if there are failures or disconnections between nodes. However, this may come at the cost of consistency, as users may see different data at different times.
-
Banking system: A banking system may prioritize consistency and partition tolerance, meaning that every user sees the same data at the same time and the system remains operational, even if there are failures or disconnections between nodes. However, this may come at the cost of availability, as the database may become unavailable in certain failure scenarios.
Overall, different businesses will have different priorities when it comes to the CAP theorem, and it is important to choose the database that best meets the needs of the business and its workload.
