Embracing Messaging and Eventual Consistency in your Microservices Solutions
summary
Argues that messaging plus eventual consistency yields manageable microservices with concrete practices and demos: local Docker Compose with Postgres and Kafka, cloud deploy to Azure AKS using Event Hubs and Kafka as the backbone, ASP.NET Core services, SQL Database and Cosmos DB for write/read stores. Describes CQRS patterns with a projector per service, SLA goals of sub-second/sub-300ms projections, the ability to replayable event history for audits and rebuilds, and a local dev story for debugging and tracing.
AI-written summary · about
chapters (24)
- 0:00Introduction
- 0:53Hello World
- 2:22Microservices Architecture
- 3:48SelfHealing
- 5:29Distributed Data
- 8:43Data Services
- 9:34Twophase Commit
- 12:56Design First
- 14:58Design Pattern
- 16:00Identity Domain
- 18:21Identity Server
- 27:36Message Based Solution
- 28:11Config Runtime
- 29:15Microservices
- 29:53Writestore approach
- 31:10Readstore approach
- 34:26Messagebased architecture
- 36:38Technology stack
- 37:40Cloud stack
- 41:32Demo
- 46:46Backend
- 49:13Event Hub
- 52:45Create Message
- 56:11Send Message
related talks
58mImplementing Event-driven Microservices
01h 03mHow to build .NET Microservices
47mEvent-Driven Microservices = Microservices Done Right
51mPatterns & Practices of Implementing Event-driven MicroSvc...
39mManaging Consistency, State and Identity in Distributed Microservices
42mCreating event-driven microservices: the why, how and what
47mMicroservices Architecture and Data Consistency: So How Do We Do It for Real?
01h 01mMessage Brokering and Event Driven Architecture on Azure