Microservice Architecture
Supported by KongMicroXchg 2018: Managing data consistency in a microservice architecture using Sagas
The services in a microservice architecture must be loosely coupled and so cannot share database tables. What’s more, two phase commit (a.k.a. a distributed transaction) is not a viable option for modern applications. Consequently, a microservices application must use the Saga pattern, which maintains data consistency using a series of local transactions.
In this presentation, you will learn how sagas work and how they differ from traditional transactions. We describe how to use sagas to develop business logic in a microservices application. You will learn effective techniques for orchestrating sagas and how to use messaging for reliability. We will describe the design of a saga framework for Java and show a sample application.
Slides
Code
There are three versions of the customers and orders example:
- Choreography-based saga where the services publish domain events using the Eventuate Tram framework
- Orchestration-based saga where the
Order Serviceuses a saga orchestrator implemented using the Eventuate Tram Sagas framework - Choreography and event sourcing-based saga where the services publish domain events using the Eventuate event sourcing framework
Video
Take a advanced microservices training class: distributed data management in a microservice architecture
I offer an advanced microservices class - distributed data management in a microservice architecture - that covers sagas, event sourcing and CQRS.
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My virtual bootcamp, distributed data patterns in a microservice architecture, is now open for enrollment!
It covers the key distributed data management patterns including Saga, API Composition, and CQRS.
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Topics
Note: tagging is work-in-process
Cynefin · DDD · GitOps · Microservices adoption · ancient lore · anti-patterns · api gateway · application api · application architecture · architecting · architecture · architecture documentation · assemblage · automation · beer · books · build vs buy · containers · dark energy and dark matter · decision making · deployment · deployment pipeline · design-time coupling · developer experience · development · devops · docker · eventuate platform · fast flow · generative AI · glossary · health · hexagonal architecture · implementing commands · implementing queries · inter-service communication · kubernetes · loose coupling · microservice architecture · microservice chassis · microservices adoption · microservices rules · microservicesio updates · modular monolith · multi-architecture docker images · observability · pattern · pattern language · refactoring · refactoring to microservices · resilience · sagas · security · service api · service architecture · service blueprint · service collaboration · service design · service discovery · service granularity · service template · software delivery metrics · success triangle · survey · tacos · team topologies · technical debt · testing · transaction management · transactional messaging · wardley mapping
The patterns
Application architecture patterns
Decomposition
- Decompose by business capability
- Decompose by subdomain
- Self-contained Servicenew
- Service per teamnew
Refactoring to microservicesnew
Data management
- Database per Service
- Shared database
- Saga
- Command-side replica
- API Composition
- CQRS
- Domain event
- Event sourcing
Transactional messaging
Testing
Deployment patterns
- Multiple service instances per host
- Service instance per host
- Service instance per VM
- Service instance per Container
- Serverless deployment
- Service deployment platform
Cross cutting concerns
Communication style
External API
Service discovery
- Client-side discovery
- Server-side discovery
- Service registry
- Self registration
- 3rd party registration
Reliability
Security
Observability
- Log aggregation
- Application metrics
- Audit logging
- Distributed tracing
- Exception tracking
- Health check API
- Log deployments and changes
UI patterns