Simplifying CAP Theorem For System Design

Simplifying CAP Theorem through a burger joint example.

The CAP theorem stands for Consistency, Availability, and Partitions and expresses that a distributed system generally produces two of three properties simultaneously and characterizes the compromise between consistency and availability when a partition exists.

In Short, the CAP theorem says you can’t always have all three qualities perfectly at the same time.?

C - Consistency refers to the performance of reading and writing operations for all users connected via a distributed system irrespective of which a user accesses the node.

A - Availability refers to the persistence of data to flow through at all times regardless of failed nodes. The entire distributed network should remain functional even if several nodes malfunction.

P - Partition tolerance in CAP theorem refers to an interrupted communication between nodes of a distributed system. The distributed system must have partitional tolerance to overcome malfunctioning nodes and continue read/write operations.

Let's break it down with our burger joint example:

Consistency: This is like when you always get exactly what you ordered. You know your burger will have the right toppings, the right cheese, and the right sauce every single time. In database terms, it means every user sees the same data at the same time, no matter which server they're connected to.

Availability: This is when the burger joint is always open and ready to serve. You can walk in at any time and expect to get your cheeseburger. In database terms, it means the system is always up and running, ready to respond to requests even if some parts of it fail.

Partition Tolerance: This is like when the burger joint can still serve you even if the kitchen runs out of tomatoes or the cashier's register breaks down. In database terms, it means the system can keep working even if some parts of it can't communicate with each other.

Now, here's the catch: according to the CAP theorem, you can't have all three qualities perfectly all the time. It's like trying to balance a stack of three burgers on one finger – it's bound to topple over.

Let's see how it plays out:

Scenario 1: Consistency and Availability, but no Partition Tolerance: Imagine the burger joint guarantees that you always get your cheeseburger exactly as you ordered, and it's always open. But if something goes wrong, like the grill breaks down, they might shut down until it's fixed. In database terms, this means ensuring that every user sees the same up-to-date data, but if there's a network problem, the system might stop working altogether.

Scenario 2: Availability and Partition Tolerance, but no Consistency: Now, the burger joint is always open, and even if they run out of cheese or lettuce, they can still serve you something. But they might sometimes mix up orders or forget the special sauce. In database terms, this means the system is always up and running, and it can still serve data even if some parts of it can't talk to each other. However, different users might see different versions of the data at the same time.

Scenario 3: Consistency and Partition Tolerance, but no Availability: Lastly, the burger joint ensures that every customer gets their order exactly right every time, and they can still serve you even if the kitchen is short-staffed or the delivery truck is late. But, if they get too busy, they might start turning customers away. In database terms, this means ensuring that every user sees the same up-to-date data, even if there's a network problem. However, if there's a major issue, the system might stop responding to requests until it's resolved.

So, there you have it – the CAP theorem explained with burgers! Just remember, when designing systems, you've got to decide which qualities matter most to you and your users, because you can't have it all.