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System Design Guide for Software Professionals

You're reading from   System Design Guide for Software Professionals Build scalable solutions – from fundamental concepts to cracking top tech company interviews

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805124993
Length 384 pages
Edition 1st Edition
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Authors (2):
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Dhirendra Sinha Dhirendra Sinha
Author Profile Icon Dhirendra Sinha
Dhirendra Sinha
Tejas Chopra Tejas Chopra
Author Profile Icon Tejas Chopra
Tejas Chopra
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Toc

Table of Contents (21) Chapters Close

Preface 1. Part 1: Foundations of System Design
2. Chapter 1: Basics of System Design FREE CHAPTER 3. Chapter 2: Distributed System Attributes 4. Chapter 3: Distributed Systems Theorems and Data Structures 5. Part 2: Core Components of Distributed Systems
6. Chapter 4: Distributed Systems Building Blocks: DNS, Load Balancers, and Application Gateways 7. Chapter 5: Design and Implementation of System Components –Databases and Storage 8. Chapter 6: Distributed Cache 9. Chapter 7: Pub/Sub and Distributed Queues 10. Part 3: System Design in Practice
11. Chapter 8: Design and Implementation of System Components: API, Security, and Metrics 12. Chapter 9: System Design – URL Shortener 13. Chapter 10: System Design – Proximity Service 14. Chapter 11: Designing a Service Like Twitter 15. Chapter 12: Designing a Service Like Instagram 16. Chapter 13: Designing a Service Like Google Docs 17. Chapter 14: Designing a Service Like Netflix 18. Chapter 15: Tips for Interviewees 19. Chapter 16: System Design Cheat Sheet 20. Index

PACELC theorem

The PACELC theorem, an extension of the CAP theorem, considers the trade-offs in distributed systems when confronted with network partitions. Let’s take a look at what PACELC stands for:

  • Partition tolerance (P): Partition tolerance refers to a distributed system’s ability to function and provide services even when network partitions or communication failures occur. It means the system can tolerate the loss of network connectivity between different nodes.
  • Availability (A): Availability ensures that every request made to the distributed system eventually receives a response, regardless of the state of individual nodes or network partitions. The focus is on providing timely responses, even if it means sacrificing strong consistency.
  • Consistency (C): Consistency entails all nodes in a distributed system agreeing on the current state of the system. Strong consistency guarantees that every read operation sees the most recent write operation. However...
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