Patterns of Distributed Systems

Length: 390 pages
Edition: 1
Language: English
Publisher: Addison-Wesley Professional
Publication Date: 2023-09-25
ISBN-10: 0138221987
ISBN-13: 9780138221980
Sales Rank: #0 (See Top 100 Books)

Learn How to Better Understand Distributed System Design and Solve Common Problems

Enterprises today rely on a range of distributed software handling data storage, messaging, system management, and compute capability. Distributed system designs need to be implemented in some programming language, and there are common problems that these implementations need to solve. These problems have common recurring solutions. A patterns approach is very suitable to describe these implementation aspects.

Patterns by nature are generic enough to cover a broad range of products from cloud services like Amazon S3 to message brokers like Apache Kafka to infrastructure frameworks like Kubernetes to databases like MongoDB or Actor frameworks like Akka. At the same time the pattern structure is specific enough to be able to show real code. The beauty of this approach is that even if the code structure is shown in one programming language (Java in this case), the structure applies to many other programming languages. Patterns also form a “system of names,” with each name having specific meaning in terms of the code structure.

The set of patterns presented in Patterns of Distributed Systems will be useful to all developers–even if they are not directly involved in building these kinds of systems, and mostly use them as a black box. Learning these patterns will help readers develop a deeper understanding of the challenges presented by distributed systems and will also help them choose appropriate cloud services and products. Coverage includes Patterns of Data Replication, Patterns of Data Partitioning, Patterns of Distributed Time, Patterns of Cluster Management, and Patterns of Communication Between Nodes.

The patterns approach used here will help you

Learn what a distributed system is and why distributed systems are needed
Understand the implementation of a wide range of systems such as databases, in-memory data grids, message brokers, and various cloud services
Prepare you to confidently traverse through open source codebases and discover how patterns and solutions map to real world systems like Kafka and Kubernetes

Cover Page
Title Page
Contents
Table of Contents
Part I: Narratives
Chapter 1. Why Distribute?
The four fundamental resources
Queuing and its impact on system throughput
Partitioning - Divide and Conquer
Chapter 2. Overview of the Patterns
Keeping data resilient on a single server
Competing Updates
Dealing with the leader failing
Multiple failures need a Generation Clock
Log entries cannot be committed until they are accepted by a Quorum
Followers commit based on a High-Water Mark
Leaders use a series of queues to remain responsive to many clients
Followers can handle read requests to reduce load on the leader
A large amount of data can be partitioned over multiple nodes
Partitions can be replicated for resilience
Two phases are needed to maintain consistency across partitions
In a distributed system, time is complicated
A Consistent Core can manage the membership of a data cluster
Gossip Dissemination can be used to manage a cluster without a centralized controller
Part II: Patterns of Data Replication
Chapter 3. Write-Ahead Log
Problem
Solution
Examples
Chapter 4. Segmented Log
Problem
Solution
Examples
Chapter 5. Low-Water Mark
Problem
Solution
Examples
Chapter 6. Leader and Followers
Problem
Solution
Examples
Chapter 7. HeartBeat
Problem
Solution
Examples
Chapter 8. Paxos
Problem
Solution
Examples
Chapter 9. Replicated Log
Problem
Solution
Examples
Chapter 10. Quorum
Problem
Solution
Examples
Chapter 11. Generation Clock
Problem
Solution
Examples
Chapter 12. High-Water Mark
Problem
Solution
Examples
Chapter 13. Singular Update Queue
Problem
Solution
Examples
Chapter 14. Request Waiting List
Problem
Solution
Examples
Chapter 15. Idempotent Receiver
Problem
Solution
Examples
Chapter 16. Follower Reads
Problem
Solution
Examples
Chapter 17. Versioned Value
Problem
Solution
Examples
Chapter 18. Version Vector
Problem
Solution
Examples
Part III: Patterns of Data Partitioning
Chapter 19. Fixed Partitions
Problem
Solution
Examples
Chapter 20. Key-Range Partitions
Problem
Solution
Examples
Chapter 21. Two Phase Commit
Problem
Solution
Examples
Part IV: Patterns of Distributed Time
Chapter 22. Lamport Clock
Problem
Solution
Examples
Chapter 23. Hybrid Clock
Problem
Solution
Examples
Chapter 24. Clock-Bound Wait
Problem
Solution
Examples
Part V: Patterns of Cluster Management
Chapter 25. Consistent Core
Problem
Solution
Examples
Chapter 26. Lease
Problem
Solution
Examples
Chapter 27. State Watch
Problem
Solution
Examples
Chapter 28. Gossip Dissemination
Problem
Solution
Examples
Chapter 29. Emergent Leader
Problem
Solution
Examples
Part VI: Patterns of communication between nodes
Chapter 30. Single Socket Channel
Problem
Solution
Examples
Chapter 31. Request Batch
Problem
Solution
Examples
Chapter 32. Request Pipeline
Problem
Solution
Examples
Bibliography