book

Foundations of Scalable Systems

by Ian Gorton

June 2022

Intermediate to advanced

337 pages

9h 23m

English

O'Reilly Media, Inc.

Book available

Read now

Unlock full access

Why Scalability?Who This Book Is ForWhat You Will LearnNote for EducatorsConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
What Is Scalability?Examples of System Scale in the Early 2000sHow Did We Get Here? A Brief History of System GrowthScalability Basic Design PrinciplesScalability and CostsScalability and Architecture Trade-OffsPerformanceAvailabilitySecurityManageabilitySummary and Further Reading
Basic System ArchitectureScale OutScaling the Database with CachingDistributing the DatabaseMultiple Processing TiersIncreasing ResponsivenessSystems and Hardware ScalabilitySummary and Further Reading
Communications BasicsCommunications HardwareCommunications SoftwareRemote Method InvocationPartial FailuresConsensus in Distributed SystemsTime in Distributed SystemsSummary and Further Reading
Why Concurrency?ThreadsOrder of Thread ExecutionProblems with ThreadsRace ConditionsDeadlocksThread StatesThread CoordinationThread PoolsBarrier SynchronizationThread-Safe CollectionsSummary and Further Reading
Service DesignApplication Programming Interface (API)Designing ServicesState ManagementApplications ServersHorizontal ScalingLoad BalancingLoad Distribution PoliciesHealth MonitoringElasticitySession AffinitySummary and Further Reading
Application CachingWeb CachingCache-ControlExpires and Last-ModifiedEtagSummary and Further Reading
Introduction to MessagingMessaging PrimitivesMessage PersistencePublish–SubscribeMessage ReplicationExample: RabbitMQMessages, Exchanges, and QueuesDistribution and ConcurrencyData Safety and Performance Trade-offsAvailability and Performance Trade-OffsMessaging PatternsCompeting ConsumersExactly-Once ProcessingPoison MessagesSummary and Further Reading

The Attractions of ServerlessGoogle App EngineThe BasicsGAE Standard EnvironmentAutoscalingAWS LambdaLambda Function Life CycleExecution ConsiderationsScalabilityCase Study: Balancing Throughput and CostsChoosing Parameter ValuesGAE Autoscaling Parameter Study DesignResultsSummary and Further Reading
The Movement to MicroservicesMonolithic ApplicationsBreaking Up the MonolithDeploying MicroservicesPrinciples of MicroservicesResilience in MicroservicesCascading FailuresBulkhead PatternSummary and Further Reading
Distributed DatabasesScaling Relational DatabasesScaling UpScaling Out: Read ReplicasScale Out: Partitioning DataExample: Oracle RACThe Movement to NoSQLNoSQL Data ModelsQuery LanguagesData DistributionThe CAP TheoremSummary and Further Reading
What Is Eventual Consistency?Inconsistency WindowRead Your Own WritesTunable ConsistencyQuorum Reads and WritesReplica RepairActive RepairPassive RepairHandling ConflictsLast Writer WinsVersion VectorsSummary and Further Reading
Introduction to Strong ConsistencyConsistency ModelsDistributed TransactionsTwo-Phase Commit2PC Failure ModesDistributed Consensus AlgorithmsRaftLeader ElectionStrong Consistency in PracticeVoltDBGoogle Cloud SpannerSummary and Further Reading
RedisData Model and APIDistribution and ReplicationStrengths and WeaknessesMongoDBData Model and APIDistribution and ReplicationStrengths and WeaknessesAmazon DynamoDBData Model and APIDistribution and ReplicationStrengths and WeaknessesSummary and Further Reading
Event-Driven ArchitecturesApache KafkaTopicsProducers and ConsumersScalabilityAvailabilitySummary and Further Reading
Introduction to Stream ProcessingStream Processing PlatformsCase Study: Apache FlinkDataStream APIScalabilityData SafetyConclusions and Further Reading
AutomationObservabilityDeployment PlatformsData LakesFurther Reading and Conclusions

Content preview from Foundations of Scalable Systems

Part IV. Event and Stream Processing

Part IV switches gears and describes architectures and technologies for processing streaming events at scale. Event-based systems pose their own unique challenges. They require technologies for reliably and efficiently capturing and persisting high-volume event streams. You also need tools to support calculating partial results from the most recent snapshots of the event stream (think trending topics in Twitter), with real-time capabilities and tolerance of processing node failures. I’ll explain the architectural approaches required and illustrate solutions using the widely deployed Apache Kafka and Flink open source technologies.