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CAP theorem demystified


In a web world, everything is available over the internet. While designing a distributed application for the client, we often face this dilemma of choosing between consistency and availability.  There is no further argument if we don't choose to have a partition tolerant system, that's single node application and it will adhere to consistency and availability. So we will be discussing Partition tolerant systems for a distributed environment.  Lets first understand CAP individual elements;

C (Consistency): 
In a distributed environment of n nodes, a change in one node should be instantly reflected all other nodes. So any data change/state should be reflected all client irrespective of whichever node they access the data. 


A (Availability):

All the non-failing node should be ready to serve any request within a reasonable time. This applies to every node present in the system. 

P (Partition Tolerence):

Distributed systems are meant to build partition tolerant systems. Any node creation and removal should be gracefully handled. 


A distributed system need to partition tolerant, So P axis will be fixed. As there is a contradiction between Consistency and availability, we have to fine-tune them for business cases. 

Example: I have 3 servers with user data in place. Now All three have the same data, so any read request will be having consistent data for any read request to any node.  Now if user A has modified his details and call has been made to server S1 at T0. So data is updated at S1 at T0 but will be propagated to S2 and S3 post T4 (Assume). So either S2 and S3 will choose to decline any request till T4 to maintain consistency and compromise availability or will continue to serve request but with an outdated data. 

And that's gist of CAP theorem. 

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