CAP Theorem Explained

So it's not like there is a main thing, but...

In any case, so CAP theorem, it's actually a concept which was defined by the computer scientist Eric Brewer on a conference which is called PODC in the beginning of this century, in 2000.

And basically, this theorem states that if you have a distributed system, it can only provide two or three properties, which basically create the word CAP, like C-A-P.

And those three letters are actually properties.

And the theorem says it's possible to only make two of them work at the same time, not three of them.

And those properties, its consistency, availability, and partition tolerance.

So it's like consistency C, availability A, P is partition tolerance.

What does those words mean? So consistency of the distributed system, it means that there is a kind of like an illusion that all the operations in our system, they appear on all the nodes of the system at the same time.

all the data in all our distributed systems is consistent.

So it doesn't happen that we update our data and then some part of the system sees this data updated and some doesn't.

So it would be inconsistent.

So consistency means that there is kind of like a global order.

It's like a monolith system.

It kind of imagines that like there is a single system instead of being built by multiple nodes.

Then availability.

It means that all the queries to our distributed systems, they will get an answer.

So all the requests should have a response.

So the system will be available, but then it's not necessary that this data will be correct.

So it will just give an answer, but it doesn't state that this answer will be correct or consistent, right? But we will have an answer.

This is it.

This is the availability.

And partition tolerance means that the system will work even in the case of a failure.

Or if the system is split into isolated sections, so to say.

So if the system will be partitioned, it will still work.