book

Designing Distributed Systems, 2nd Edition

by Brendan Burns

December 2024

Intermediate to advanced

220 pages

5h 59m

English

O'Reilly Media, Inc.

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Related skills

Distributed Systems

Associated roles

Android developer
Data architect
Data/ML engineer
iOS developer

ML/AI architect
Senior software developer
Software architect

+3 more

Who Should Read This BookWhy I Wrote This BookThe World of Distributed Systems TodayNavigating This BookConventions Used in This BookOnline ResourcesUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
A Brief History of Systems DevelopmentA Brief History of Patterns in Software DevelopmentFormalization of Algorithmic ProgrammingPatterns for Object-Oriented ProgrammingThe Rise of Open Source SoftwareThe Value of Patterns, Practices, and ComponentsStanding on the Shoulders of GiantsA Shared Language for Discussing Our PracticeShared Components for Easy ReuseSummary
APIs and RPCsLatencyReliabilityPercentilesIdempotencyDelivery SemanticsRelational IntegrityData ConsistencyOrchestration and KubernetesHealth ChecksSummary
An Example Sidecar: Adding HTTPS to a Legacy ServiceDynamic Configuration with SidecarsModular Application ContainersHands On: Deploying the topz ContainerBuilding a Simple PaaS with SidecarsDesigning Sidecars for Modularity and ReusabilityParameterized ContainersDefine Each Container’s APIDocumenting Your ContainersSummary
Using an Ambassador to Shard a ServiceHands On: Implementing a Sharded RedisUsing an Ambassador for Service BrokeringUsing an Ambassador to Do Experimentation or Request SplittingHands On: Implementing 10% ExperimentsSummary
MonitoringHands On: Using Prometheus for MonitoringLoggingHands On: Normalizing Different Logging Formats with fluentdAdding a Health MonitorHands On: Adding Rich Health Monitoring for MySQLSummary
Stateless ServicesReadiness Probes for Load BalancingHands On: Creating a Replicated Service in KubernetesSession Tracked ServicesApplication-Layer Replicated ServicesIntroducing a Caching LayerDeploying Your CacheHands On: Deploying the Caching LayerExpanding the Caching LayerRate Limiting and Denial-of-Service DefenseSSL TerminationHands On: Deploying nginx and SSL TerminationSummary

Sharded CachingWhy You Might Need a Sharded CacheThe Role of the Cache in System PerformanceReplicated Sharded CachesHands On: Deploying an Ambassador and Memcache for a Sharded CacheAn Examination of Sharding FunctionsSelecting a KeyConsistent Hashing FunctionsHands On: Building a Consistent HTTP Sharding ProxySharded Replicated ServingHot Sharding SystemsSummary
Scatter/Gather with Root DistributionHands On: Distributed Document SearchScatter/Gather with Leaf ShardingHands On: Sharded Document SearchChoosing the Right Number of LeavesScaling Scatter/Gather for Reliability and ScaleSummary
Determining When FaaS Makes SenseThe Benefits of FaaSThe Challenges of FaaSThe Need for Background ProcessingThe Need to Hold Data in MemoryThe Costs of Sustained Request-Based ProcessingPatterns for FaaSThe Decorator Pattern: Request or Response TransformationHands On: Adding Request Defaulting Prior to Request ProcessingHandling EventsHands On: Implementing Two-Factor AuthenticationEvent-Based PipelinesHands On: Implementing a Pipeline for New User SignupSummary
Determining If You Even Need Leader ElectionThe Basics of Leader ElectionHands On: Deploying etcdImplementing LocksHands On: Implementing Locks in etcdImplementing OwnershipHands On: Implementing Leases in etcdHandling Concurrent Data ManipulationSummary
A Generic Work Queue SystemThe Source Container InterfaceWork Queue APIThe Worker Container InterfaceThe Shared Work Queue InfrastructureHands On: Implementing a Video ThumbnailerDynamic Scaling of the WorkersThe Multiworker PatternSummary
Patterns of Event-Driven ProcessingCopierFilterSplitterSharderMergerHands On: Building an Event-Driven Flow for New User SignupPublisher/Subscriber InfrastructureHands On: Deploying KafkaResiliency and Performance in Work QueuesWork StealingErrors, Priority, and RetrySummary
Join (or Barrier Synchronization)ReduceHands On: CountSumHistogramHands On: An Image Tagging and Processing PipelineSummary
Monitoring and Observability BasicsLoggingMetricsBasic Request MonitoringAdvanced Request MonitoringAlertingTracingAggregating InformationSummary
The Basics of AI SystemsHosting a ModelDistributing a ModelDevelopment with ModelsRetrieval-Augmented GenerationTesting and DeploymentSummary
The Thundering HerdThe Absence of Errors Is an Error“Client” and “Expected” ErrorsVersioning ErrorsThe Myth of Optional ComponentsOops, We “Cleaned Up” EverythingChallenges with the Breadth of InputsProcessing Obsolete WorkThe “Second System” ProblemSummary

Overview

Every distributed system strives for reliability, performance, and quality, but building such a system is hard. Establishing a set of design patterns enables software developers and system architects to use a common language to describe their systems and learn from the patterns and practices developed by others.

The popularity of containers and Kubernetes paves the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help guide the systems you build using common patterns and practices drawn from some of the highest performing distributed systems in use today. These common patterns make the systems you build far more approachable and efficient, even if you've never built a distributed system before.

Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system.

This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.

Understand how patterns and reusable components enable the rapid development of reliable distributed systems
Use the sidecar, adapter, and ambassador patterns to split your application into a group of containers on a single machine
Explore loosely coupled multinode distributed patterns for replication, scaling, and communication between components
Learn distributed system patterns for large-scale batch data processing covering work queues, event-based processing, and coordinated workflows