Overview
This tutorial will teach you how to build a Twitter-like distributed service in Azure using Java 21, Dropwizard, Docker, and distributed system patterns.
Prerequisites
- Basic knowledge of Java
- Basic knowledge of cloud computing
- Basic knowledge of Docker
Syllabus
Week 1: Introduction
- What is Java 21?
- Key new features in Java 21
- Why use Java 21?
- Distributed system patterns overview
- Introduction to Docker
- Introduction to Twitter-like application architecture
Week 2: Virtual threads and record patterns
- What are virtual threads?
- Benefits of using virtual threads
- How to use virtual threads in Java 21
- What are record patterns?
- Benefits of using record patterns
- How to use record patterns in Java 21
Week 3: Sequenced collections and key encapsulation mechanism API
- What are sequenced collections?
- Benefits of using sequenced collections
- How to use sequenced collections in Java 21
- What is the key encapsulation mechanism API?
- Benefits of using the key encapsulation mechanism API
- How to use the key encapsulation mechanism API in Java 21
Week 4: Building a Twitter-like Dropwizard example service in Azure using Docker
- Creating a new Dockerfile for the Dropwizard project
- Building and running the Dropwizard project in Docker
- Adding virtual threads, record patterns, and sequenced collections to the project in Docker
- Implementing the basic message and video processing logic in Docker
- Testing the service in Docker
- Deploying the service to Azure App Service using Docker
Week 5: Implementing message and video processing logic
- Implementing message processing logic, such as creating, reading, updating, and deleting messages
- Implementing video processing logic, such as uploading, transcoding, and streaming videos
Week 6: Scaling out the service in Azure using Docker
- Adding Azure Service Bus to the architecture for asynchronous messaging
- Adding Azure Cosmos DB to the architecture for durable storage
- Using Azure Blob Storage for storing videos
- Using Azure Redis Cache for caching
- Deploying the service to Azure App Service using Docker as a Kubernetes cluster
Week 7: Securing the service in Azure using Docker
- Using Azure Application Gateway to secure the service with SSL
- Implementing single sign-on using Azure Active Directory
- Deploying the service to Azure App Service using Docker
Week 8: Data binding and database operations with Azure using Docker
- Binding data to Java objects in Docker
- Using Azure Cosmos DB and Azure PostgreSQL for data storage in Docker
- Performing CRUD operations on data in Docker
- Managing video files in Azure Blob Storage in Docker
Week 9: DevOps with Azure using Docker
- Using Azure Pipelines to build and deploy the service using Docker
- Using Azure Kubernetes Service (AKS) to manage the service using Docker
Week 10: Distributed system patterns in detail
- Microservices architecture
- Event-driven architecture
- API-first design
- Load balancing and caching
- Data consistency and durability
- Failure handling and recovery
How to apply distributed system patterns to our Twitter-like example service
We can apply the distributed system patterns discussed above to our Twitter-like example service in a number of ways. For example:
- We can decompose our Twitter-like service into a set of microservices, each of which is responsible for a specific task, such as user management, message processing, or video processing.
- We can use an event bus to decouple the communication between the different microservices in our Twitter-like service.
- We can expose an API for each of the microservices in our Twitter-like service. This would allow other applications to interact with our Twitter-like service.
- We can use a load balancer to distribute traffic across the different instances of our Twitter-like service. This will improve the performance and scalability of the service.
- We can use a cache to store frequently accessed data, such as user profiles and popular messages. This will improve the performance of the service by reducing the number of database queries that need to be made.
- We can use a database replication strategy to ensure that the data in our Twitter-like service is consistent and durable. For example, we could replicate the database to multiple Azure regions.
- We can use a variety of design patterns and technologies to handle