The CAP Theorem: How to Choose Between Consistency, Availability, and Partition Tolerance in Distributed Systems
5 min read
Have you ever heard of the CAP theorem? No, it's not a theorem about caps or headwear (although that would be a pretty cool theorem). The CAP theorem, also known as Brewer's theorem, is a fundamental concept in the field of distributed systems.
Let's take a look.
The CAP Theorem
So, what does it say? Well, the CAP theorem states that in a distributed system (a system with multiple components that operate and communicate over a network), it is impossible to simultaneously guarantee all of the following three properties:
Consistency: All components of the system have the same data at all times.
Availability: All requests to the system receive a response within a certain timeframe.
Partition tolerance: The system continues to operate despite the occurrence of network failures or partitions.
In other words, you can only have two out of three! You can have consistency and availability, but you'll have to sacrifice partition tolerance. You can have consistency and partition tolerance, but you'll have to sacrifice availability. Or you can have availability and partition tolerance, but you'll have to sacrifice consistency.
But don't worry, this doesn't mean that all distributed systems are doomed to failure. It just means that designers and developers of distributed systems have to make trade-offs and choose which properties are most important for their specific use case.
Let's say you're building a distributed system for a grocery store. You want to ensure that all components of the system have the same data at all times (consistency) so that customers can't buy the same item twice and deplete the store's inventory. You also want to ensure that the system is always available (availability) so that customers can make purchases even if one component goes down. In this case, you might have to sacrifice partition tolerance, meaning that the system might not continue to operate if there is a network failure or partition.
On the other hand, let's say you're building a distributed system for a social media platform. You want to ensure that the system is always available (availability) so that users can post, comment, and like to their heart's content. You also want to ensure that the system can continue to operate even if there are network failures or partitions (partition tolerance). In this case, you might have to sacrifice consistency, meaning that data might not be the same across all components of the system at all times.
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.”
CAP Theorem in NoSQL Databases
The CAP theorem is especially relevant when it comes to NoSQL databases. NoSQL databases, which stands for "not only SQL," are designed to handle large amounts of data that don't fit neatly into a traditional relational database structure. They often prioritize availability and partition tolerance over consistency, as they are designed to scale horizontally and handle large numbers of users and requests. However, this means that they may not provide the same level of consistency as a traditional SQL database.
Note: One important consideration when choosing between a NoSQL and SQL database is how they scale. NoSQL databases are designed to scale horizontally, meaning they can handle large amounts of data and users by adding more machines to the system. SQL databases, on the other hand, are designed to scale vertically, meaning they handle large amounts of data and users by adding more power (e.g. CPU, memory) to a single machine.
CAP Theorem in MongoDB and Cassandra
Two popular NoSQL databases, MongoDB and Cassandra, have different approaches to the CAP theorem. MongoDB is considered a "CAP-optional" database, meaning that it allows users to choose which properties are most important for their specific use case. By default, it prioritizes consistency, but users can choose to sacrifice consistency for availability in the event of a partition. Cassandra, on the other hand, is a "CAP-aware" database, meaning that it is designed to prioritize partition tolerance over consistency and availability. This means that in the event of a partition, Cassandra may sacrifice consistency in order to maintain availability.
The CAP theorem is a fundamental concept, but it does have certain limitations.
The CAP theorem only applies to systems that use a network to communicate and operate. It doesn't apply to systems that only use one computer.
The CAP theorem only talks about the trade-off between consistency, availability, and partition tolerance. It doesn't address other properties like latency, performance or security.
The CAP theorem assumes you can only choose two out of the three properties. But in reality, you might be able to achieve a little bit of all three properties.
The CAP theorem assumes that network issues are really bad and that computers can't communicate with each other right away. But this might not always be true in real life.
In conclusion, the CAP theorem is a helpful way to understand the trade-offs between different properties in distributed systems, but it's important to remember that it doesn't cover everything and might not be exactly true in all situations.
So, a variant of the CAP Theorem known as the "PACLEC Theorem" was introduced by Dr Eric Brewer in 2012. It provides a better understanding of the trade-offs between consistency and availability in distributed systems. We will discuss the PACELC Theorem in more detail in a separate article.
See, the CAP theorem isn't so scary after all! Just remember, in a distributed system, you can only have two out of three. And always choose wisely!
Thanks for reading, and I hope you learned something new about the CAP theorem. Happy distributed computing!
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