A Patterns Approach to Designing Distributed Systems and Solving Common Implementation Problems

More and more enterprises today are dependent on cloud services from providers like AWS, Microsoft Azure, and GCP. They also use products, such as Kafka and Kubernetes, or databases, such as YugabyteDB, Cassandra, MongoDB, and Neo4j, that are distributed by nature. Because these distributed systems are inherently stateful systems, enterprise architects and developers need to be prepared for all the things that can and will go wrong when data is stored on multiple servers--from process crashes to network delays and unsynchronized clocks.

Patterns of Distributed Systems describes a set of patterns that have been observed in mainstream open-source distributed systems. Studying the common problems and the solutions that are embodied by the patterns in this guide will give you a better understanding of how these systems work, as well as a solid foundation in distributed system design principles.

Featuring real-world code examples from systems like Kafka and Kubernetes, these patterns and solutions will prepare you to confidently traverse open-source codebases and understand implementations you encounter "in the wild."

• Review the building blocks of consensus algorithms, like Paxos and Raft, for ensuring replica consistency in distributed systems
• Understand the use of logical timestamps in databases, a fundamental concept for data versioning
• Explore commonly used partitioning schemes, with an in-depth look at intricacies of two-phase-commit protocol
• Analyze mechanisms used in implementing cluster coordination tasks, such as group membership, failure detection, and enabling robust cluster coordination
• Learn techniques for establishing effective network communication between cluster nodes.
  Along with enterprise architects and data architects, software developers working with cloud services such as Amazon S3, Amazon EKS, and Azure CosmosDB or GCP Cloud Spanner will find this set of patterns to be indispensable.

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Autorentext

Unmesh Joshi is a Principal Consultant at Thoughtworks with 22 years of industry experience. He is a software architecture enthusiast, who believes that understanding principles of distributed systems is as essential today as understanding web architecture or object-oriented programming was in the last decade. For the last two years he has been publishing patterns of distributed systems on martinfowler.com. He has also conducted various training sessions around this topic. Twitter: @unmeshjoshi

Klappentext

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

Inhalt

Foreword xvii
Preface xix
Acknowledgments xxiii
About the Author xxv

Part I: Narratives 1

Chapter 1: The Promise and Perils of Distributed Systems 3
The Limits of a Single Server 3
Separate Business Logic and Data Layer 5
Partitioning Data 6
A Look at Failures 7
Replication: Masking Failures 9
Defining the Term "Distributed Systems" 10
The Patterns Approach 10

Chapter 2: Overview of the Patterns 13
Keeping Data Resilient on a Single Server 14
Competing Updates 15
Dealing with the Leader Failing 17
Multiple Failures Need a Generation Clock 21
Log Entries Cannot Be Committed until They Are Accepted by a Majority Quorum 26
Followers Commit Based on a High-Water Mark 29
Leaders Use a Series of Queues to Remain Responsive to Many Clients 34
Followers Can Handle Read Requests to Reduce Load on the Leader 40
A Large Amount of Data Can Be Partitioned over Multiple Nodes 42
Partitions Can Be Replicated for Resilience 45
A Minimum of Two Phases Are Needed to Maintain Consistency across Partitions 46
In Distributed Systems, Ordering Cannot Depend on System Timestamps 49
A Consistent Core Can Manage the Membership of a Data Cluster 58
Gossip Dissemination for Decentralized Cluster Management 62

Part II: Patterns of Data Replication 69

Chapter 3: Write-Ahead Log 71
Problem 71
Solution 71
Examples 76

Chapter 4: Segmented Log 77
Problem 77
Solution 77
Examples 79

Chapter 5: Low-Water Mark 81
Problem 81
Solution 81
Examples 83

Chapter 6: Leader and Followers 85
Problem 85
Solution 85
Examples 92

Chapter 7: HeartBeat 93
Problem 93
Solution 93
Examples 98

Chapter 8: Majority Quorum 99
Problem 99
Solution 100
Examples 102

Chapter 9: Generation Clock 103
Problem 103
Solution 104
Examples 107

Chapter 10: High-Water Mark 109
Problem 109
Solution 109
Examples 115

Chapter 11: Paxos 117
Problem 117
Solution 117
Examples 132

Chapter 12: Replicated Log 133
Problem 133
Solution 133
Examples 158

Chapter 13: Singular Update Queue 159
Problem 159
Solution 159
Examples 166

Chapter 14: Request Waiting List 167
Problem 167
Solution 167
Examples 173

Chapter 15: Idempotent Receiver 175
Problem 175
Solution 175
Examples 181

Chapter 16: Follower Reads 183
Problem 183
Solution 183
Examples 191

Chapter 17: Versioned Value 193
Problem 193
Solution 193
Examples 201

Chapter 18: Version Vector 203
Problem 203
Solution 203
Examples 216

Part III: Patterns of Data Partitioning 217

Chapter 19: Fixed Partitions 219
Problem 219
Solution 220
Examples 241

Chapter 20: Key-Range Partitions 243
Problem 243
Solution 244
Examples 255

**Ch…