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. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

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