book

Implementing Event-Driven Microservices Architecture in .NET 7

by Joshua Garverick, Omar Dean McIver

March 2023

Intermediate to advanced

326 pages

7h 27m

English

Packt Publishing

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ForewordContributorsAbout the authorsAbout the reviewers
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Technical requirementsExploring business drivers and the applicationReviewing the domain modelAssessing architectural structures and paradigmsA high-level logical architectureEvent sourcingCommand-Query Responsibility SegregationReviewing the implementation detailsThe Visual Studio solution topologyIdentity and Access Management considerationsEvent structure and schemaLocal development and debuggingNew .NET 7 featuresSummaryQuestionsFurther reading
Technical requirementsExamining producers and consumersRelating to real-world examplesEnabling event-driven architecturesUnderstanding the adoption curveExploring implementation details in codeThe producer codeThe consumer codeEvent handlingReviewing implementation details in infrastructureTopicsStreams and tablesAggregate storageSummaryQuestionsFurther reading
Technical requirementsWhat is a message broker?Queue-based technologyCache-based technologyStream-based technologyInspecting messaging protocols, schemas, and delivery patternsMessaging protocolsStandard and custom schemasMessage delivery patternsImplementing message broker technologiesReviewing essential Kafka componentsEnabling resiliency and scalabilitySummaryQuestionsFurther reading
Technical requirementsSolution structureCore library reviewReviewing domain structures and componentsEquipmentStationMaintenanceSchedulingNotificationsPassengerIdentificationUsing asynchronous actionsBenefits of asynchronous programmingAsynchronous parallel processingSummaryQuestionsFurther reading
Technical requirementsReviewing containerization fundamentalsDevelopment benefitsSetting up the local environmentCreating local infrastructure using DockerLeveraging GitHub Codespaces and Dev ContainersUsing Dockerfiles to build and run locallySequential versus multi-stage filesAdding services to the Docker Compose fileSummaryQuestionsFurther reading
Technical requirementsConfiguring orchestration and containersEverything as Code (EaC)Creating container imagesDebugging in containersDebugging individual microservicesOrchestrating and debugging all servicesFixing the Debug custom imageTesting against containersFunctionality testingLoad testingSummaryQuestionsFurther reading

Technical requirementsObservabilityMetricsLogsTracesLiveness and readinessLiveness endpointsReadiness endpointsAggregation of logsCreating an Azure Application Insights instanceStreaming telemetry to Application InsightsStreaming logs to Application InsightsCorrelation and causationProducing logging and an event payload with correlation and causation IDsConsumer logging with consistent correlation and causation IDsSummaryQuestionsFurther reading
Reviewing common CI/CD patternsEnvironment-basedArtifact managementTriggers and gatingFeature flagsEnabling GitHub Actions for CI/CD implementationGitHub Actions for continuous integrationGitHub Actions for continuous deploymentChoosing an Integration Test Suite MethodologySummaryQuestionsFurther reading
Technical requirementsFault tolerance and fault injectionAnticipating and tolerating faultsUsing your faults against youChaos testingThe order of chaos engineeringImplementing fault injection and chaos testsStarting with Chaos MeshUsing Azure Chaos StudioSummaryQuestionsFurther reading
Mechanisms for autoscalingCompute and CPU loadDisk and I/O loadRequest and network loadMemory-intensive operationsImplementing autoscaling for Kubernetes servicesNative Kubernetes optionsCloud platform optionsThird-party pluginsImplementing autoscaling for Azure App ServiceCommon platform optionsAdjustments for Web AppsAdjustments for API AppsSummaryQuestionsFurther reading
Technical requirementsPreventing data lossData consistency paradigmsACID paradigm/immediate consistencyBASE paradigm / eventual consistencyThe Saga patternCommand query responsibility segregation (CQRS)Identifying acceptable data lossAcceptable and unacceptable data lossMethods of data lossDelivery guaranteesAt-most-once deliveryAt-least-once deliveryEffectively-once deliveryData loss implicationsSummaryQuestionsFurther reading
Technical requirementsResiliency through cloud-native patternsRedundancy and enabling business continuityEvent retries and continuityLocal data redundancyInfrastructure continuityGraceful communication between servicesCommon resultsSummaryQuestionsFurther reading
Technical requirementsApplication- versus service- versus component-level telemetryImplementing non-intrusive telemetry captureImplementing custom telemetry captureBubbling up meaningful informationMetric ingestion with the Prometheus exporterDistributed tracing using the Jaeger exporterSummaryQuestionsFurther reading
Technical requirementsSharing API servicesGenerating service informationOpenAPI specificationA brief history of SwaggerGenerating the Swagger documentationService discoveryIt’s not quite load balancing…Exploring ConsulService discovery/registrationService resolutionService publishingAzure API ManagementService catalogingSummaryQuestionsFurther reading
Chapter 1, The Sample ApplicationChapter 2, The Producer-Consumer PatternChapter 3, Message BrokersChapter 4, Domain Model and Asynchronous EventsChapter 5, Containerization and Local Environment SetupChapter 6, Localized Testing and Debugging of MicroservicesChapter 7, Microservice ObservabilityChapter 8, CI/CD Pipelines and Integrated TestingChapter 9, Fault Injection and Chaos TestingChapter 10, Modern Design Patterns for ScalabilityChapter 11, Minimizing Data LossChapter 12, Service and Application ResiliencyChapter 13, Telemetry Capture and IntegrationChapter 14, Observability Revisited
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Publisher Resources

ISBN: 9781803232782Supplemental Content