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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Table of Contents:
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
Chapter 21: Two-Phase Commit 257
Problem 257
Solution 257
Examples 297
Part IV: Patterns of Distributed Time 299
Chapter 22: Lamport Clock 301
Problem 301
Solution 301
Examples 307
Chapter 23: Hybrid Clock 309
Problem 309
Solution 309
Examples 316
Chapter 24: Clock-Bound Wait 317
Problem 317
Solution 318
Examples 332
Part V: Patterns of Cluster Management 335
Chapter 25: Consistent Core 337
Problem 337
Solution 337
Examples 342
Chapter 26: Lease 345
Problem 345
Solution 345
Examples 354
Chapter 27: State Watch 355
Problem 355
Solution 355
Examples 362
Chapter 28: Gossip Dissemination 363
Problem 363
Solution 363
Examples 373
Chapter 29: Emergent Leader 375
Problem 375
Solution 375
Examples 392
Part VI: Patterns of Communication between Nodes 393
Chapter 30: Single-Socket Channel 395
Problem 395
Solution 395
Examples 397
Chapter 31: Request Batch 399
Problem 399
Solution 399
Examples 404
Chapter 32: Request Pipeline 405
Problem 405
Solution 405
Examples 408
References 409
Index 413
About the Author :
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
