CAP Theorem and Caching in System Design Simplified 2023 | Distributed System | Scaler

In this tutorial, Anshuman Singh (co-founder, Scaler) is discusses the CAP theorem and caching in system design which holds true for all distributed systems. Join our FREE Masterclass now: https://www.scaler.com/events?utm_sou...

What is CAP theorem?

CAP theorem states that any distributed data store can only provide two of the following three guarantees: Consistency Every read receives the most recent write or an error. The CAP theorem, originally introduced as the CAP principle, can be used to explain some of the competing requirements in a distributed system with replication. It is a tool used to make system designers aware of the trade-offs while designing networked shared-data systems.

What is Caching?

Caching is a process that stores multiple copies of data or files in a temporary storage location so they can be accessed faster. It temporarily saves data for software applications, servers, and web browsers, which ensures users need not download information every time they access a website or application.

A cache's primary purpose is to increase data retrieval performance by reducing the need to access the underlying slower storage layer. Trading off capacity for speed, a cache typically stores a subset of data transiently, in contrast to databases whose data is usually complete and durable.

The following topics are covered in this tutorial:

0:00 - Introduction
0:11 - What is cap theorem
10:19 - Extension of cap theorem
12:06 - What is caching?
16:20 - Hit & miss in cache
18:08 - How will you ensure caching is consistent?

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