Learn Microservices

Prerequisites

The course requires a decent understanding of Java. A dedicated course can be found here.

Software

• JDK Development Kit 21
• IntelliJ IDEA [Plugins: Envfile]
• Apache Maven
• Docker Desktop
• mlflow
• EvidentlyAI

Books

• Microservices Patterns; Richardson
• Spring Microservices in Action; Carnell, Sánchez
• Microservices with Spring Boot 3 and Spring Cloud; Larsson

Videos

• MIT 6.824 Distributed Systems

Contributing

Your pull requests are very welcome! To contribute, please refer to this guide. For a more general introduction to GitHub, refer to this page. By contributing to this repository, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree.

Modules

M1 Software architectures

• Monolithic and distributed architectural styles
• The transition towards microservices architectures

M2 Tools: Maven, Spring Boot

• Introduction to Spring Boot as microservice chassis
• Building a basic layered microservice with Spring Boot
• Packaging a project with Maven

M3 Containerization and orchestration

• Introduction to containerization
• Building docker images (single-layer, multi-layer)
• Container orchestration with docker compose

M4 Communication

• Synchronous communications and their limitations (Rest/GraphQL/Protobuf)
• Asynchronous messaging systems (RabbitMQ)
• Spring Cloud Stream and its abstraction over messaging systems

M5 Infrastructure

• Service discovery
• Service routing
• Centralized configuration

M6 Resiliency

• Client-side patterns
• Server-side patterns
• Resilience4j and its integration with Spring Boot

M7 Observability

• Instrumentation and maintenance costs
• Key observability stacks: Prometheus, ELK, Jaeger/Zipkin, Grafana
• OpenTelemetry

M8 Distributed transaction management

• The SAGA pattern
• The CQRS pattern
• The Conductor orchestration framework

M9 DevSecOps

• The DevSecOps model and its benefits in distributed contexts
• Automating code linting, security testing, and deployment with GitHub Actions

M10 MLOps

• The MLOps model and its benefits in distributed contexts
• Tracking ML experiments with MLFlow
• Observing ML models in production with EvidentlyAI

Project Ideas

• Studiare e documentare il problema della modellazione un sistema distribuito (definire microservizi, API, bounded contexts).
• Confrontare REST, GraphQL e Protobuf implementando un servizio con tutte e tre le tecnologie e analizzandone le differenze.
• Continuare a sviluppare progetto già presente di un social network.
• Studiare e documentare l’architettura distribuita di piattaforme come Facebook, Uber o Netflix.
• Creare un laboratorio interattivo per testare la resilienza di microservizi utilizzando strumenti di osservabilità (testare anche signoz e openobserve).
• Eseguire test di performance su un sistema distribuito con strumenti come Istio, Locust, k6 o Gatling.
• Utilizzare Microsim per simulare il comportamento di un’architettura a microservizi in scenari di carico e guasto.
• Esplorare progetti open-source tra quelli elencati in Awesome Distributed System Projects.
• Approfondire tools di deployment automatico (e.g. Chef, Puppet, Ansible)
• Porting degli esempi attuali in Python