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System Design Guide for Software Professionals

System Design Guide for Software Professionals

By : Dhirendra Sinha, Tejas Chopra
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System Design Guide for Software Professionals

System Design Guide for Software Professionals

By: Dhirendra Sinha, Tejas Chopra

Overview of this book

Building scalable software systems is more critical than ever. Yet, many software professionals struggle to navigate the complexities of system design, especially when aiming for positions at top tech companies. Written by Dhirendra Sinha, a seasoned Engineering Leader at Google with a blend of experience working at large companies such as Cisco, Oracle, and Yahoo, and Tejas Chopra, a Senior Software Engineer at Netflix, a TEDx speaker, and a Co-Founder of GoEB1, this comprehensive and authoritative resource on system design offers invaluable insights and strategies to help you excel in interviews with all major tech companies. This guide covers the basics of system design, including the principles and techniques of distributed systems, and delves into core building blocks such as distributed system theorems, attributes, and the design and implementation of system components. Following examples of popular applications such as Uber, Twitter, Instagram, Google Docs, and Netflix, you’ll learn how to apply concepts to real-world scenarios. The book offers expert advice and strategies for preparing and acing system design interviews, along with a mind map/cheat sheet summarizing the key takeaways. By the end of this book, you’ll be equipped with unique techniques and the confidence to solve any coding interview question.
Table of Contents (21 chapters)
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1
Part 1: Foundations of System Design
5
Part 2: Core Components of Distributed Systems
10
Part 3: System Design in Practice

CAP theorem

The CAP theorem, also known as Brewer’s theorem, is a fundamental principle in distributed systems. It states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance all at once. The acronym CAP represents the three properties.

In distributed systems, network partitions are an inevitable occurrence due to various reasons, such as hardware failures, network outages, or even routine maintenance. These partitions lead to nodes being split into isolated groups, disrupting the normal flow of communication. Consequently, the system faces a crucial decision in the face of such partitions: prioritizing between consistency and availability. On one hand, if a system opts for availability and partition tolerance (AP), it continues to function despite the partition but may sacrifice consistency, meaning all nodes might not have the same data at the same time. On the other hand, prioritizing consistency and partition tolerance...

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