book

Designing Distributed Systems

by Brendan Burns

February 2018

Intermediate to advanced

162 pages

4h 8m

English

O'Reilly Media, Inc.

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Who Should Read This BookWhy I Wrote This BookThe World of Distributed Systems TodayNavigating This BookConventions Used in This BookOnline ResourcesUsing Code ExamplesO’Reilly SafariHow 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
MotivationsSummary
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% Experiments
MonitoringHands On: Using Prometheus for MonitoringLoggingHands On: Normalizing Different Logging Formats with FluentdAdding a Health MonitorHands On: Adding Rich Health Monitoring for MySQL
Introduction to Microservices
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 Systems
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 Scale

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 Signup
Determining If You Even Need Master ElectionThe Basics of Master ElectionHands On: Deploying etcdImplementing LocksHands On: Implementing Locks in etcdImplementing OwnershipHands On: Implementing Leases in etcdHandling Concurrent Data Manipulation
A Generic Work Queue SystemThe Source Container InterfaceThe Worker Container InterfaceThe Shared Work Queue InfrastructureHands On: Implementing a Video ThumbnailerDynamic Scaling of the WorkersThe Multi-Worker Pattern
Patterns of Event-Driven ProcessingCopierFilterSplitterSharderMergerHands On: Building an Event-Driven Flow for New User Sign-UpPublisher/Subscriber InfrastructureHands On: Deploying Kafka
Join (or Barrier Synchronization)ReduceHands On: CountSumHistogramHands On: An Image Tagging and Processing Pipeline

Content preview from Designing Distributed Systems

Chapter 5. Replicated Load-Balanced Services

The simplest distributed pattern, and one that most are familiar with, is a replicated load-balanced service. In such a service, every server is identical to every other server and all are capable of supporting traffic. The pattern consists of a scalable number of servers with a load balancer in front of them. The load balancer is typically either completely round-robin or uses some form of session stickiness. The chapter will give a concrete example of how to deploy such a service in Kubernetes.

Stateless Services

Stateless services are ones that don’t require saved state to operate correctly. In the simplest stateless applications, even individual requests may be routed to separate instances of the service (see Figure 5-1). Examples of stateless services include things like static content servers and complex middleware systems that receive and aggregate responses from numerous different backend systems.

Stateless systems are replicated to provide redundancy and scale. No matter how small your service is, you need at least two replicas to provide a service with a “highly available” service level agreement (SLA). To understand why this is true, consider trying to deliver a three-nines (99.9% availability). In a three-nines service, you get 1.4 minutes of downtime per day (24 × 60 × 0.001). ...