D387 – Advanced Java Multithreaded Application

Overview

This project was completed for WGU's D387 - Advanced Java course, demonstrating the ability to build and modify a full‑stack application using Java/Spring Boot backend** and Angular frontend. Starting from an existing full-stack application (Java/Spring Boot backend, Angular front end), this project adds concurrent backend processing, real-time currency conversion, and prepares the application for containerized cloud deployment. The project focused on applying Java's concurrency utilities correctly in a real request/response flow, integrating an external API, and packaging the application with Docker for deployment to AWS.

Features

✔ Multithreaded Language Translation

• Uses Java multithreading to translate a message into multiple languages concurrently.
• Each translation runs in its own thread.
• Results are combined and returned to the Angular frontend.

✔ Time Zone Messaging

• Generates a message showing the current time in multiple time zones (e.g., EST, PST, UTC).
• Uses Java’s ZonedDateTime and ZoneId classes.
• Returned to the frontend via REST API.

✔ Currency Exchange

• Pulls live exchange rates from an external API.
• Converts a user‑provided amount between currencies.
• Uses Java HTTP client + JSON parsing.

✔ Angular Frontend

• Displays translation results, time‑zone messages, and currency conversions.
• Communicates with the Spring Boot backend via REST endpoints.
• Built and served as part of the full‑stack application.

✔ Docker Containerization

• Spring Boot backend packaged as an executable JAR.
• Dockerfile builds a single image containing the backend.
• Container runs the multithreaded application on a specified port.

Tech Stack

• Java 17
• Spring Boot
• Angular
• TypeScript
• HTML/CSS
• REST API integration
• Docker

Docker Containerization

This project uses Docker to package and run the full multithreaded Spring Boot + Angular application. The Angular front end is bundled automatically into the Spring Boot JAR during the Maven build process.

Step 1 — Build the Application JAR

mvn clean package

This produces a runnable JAR inside target/.

Step 2 — Create the Dockerfile

Place this Dockerfile in the project root:

FROM eclipse-temurin:17-jdk-alpine
WORKDIR /app
COPY target/D387-advanced-java-0.0.2-SNAPSHOT.jar app.jar
EXPOSE 8080
ENTRYPOINT ["java", "-jar", "app.jar"]

This Dockerfile:

• Uses a lightweight Java runtime
• Copies the packaged JAR
• Exposes port 8080
• Runs the application automatically

Step 3 — Build the Docker Image

docker build -t d387-backend .

Step 4 — Run the Docker Container

docker run -p 8080:8080 d387-backend

Application running in a Docker container, with multithreaded translation output visible in the logs:

Container running in Docker Desktop:

Cloud Deployment Plan

This assignment also called for documenting how the containerized application would be deployed to the cloud. AWS was selected as the target platform for its scalable container orchestration and monitoring tools, which integrate well with Dockerized Java applications.

1. Access AWS ECS Sign in to the AWS Management Console and create a new ECS cluster using the "Networking only" template. Configure the VPC, subnets, and security groups, ensuring port 8080 is open for the Spring Boot backend.

2. Package the Application Build the Docker image locally and verify it runs successfully (see Docker Containerization above).

3. Push to a Registry Push the verified image to a container registry — Amazon Elastic Container Registry (ECR) or Docker Hub — by creating a repository and pushing the tagged image.

4. Deploy to ECS Create an ECS task definition referencing the image, specifying CPU, memory, and network settings. Run the task for initial testing, then create an ECS Service for continuous operation.

5. Monitor and Scale Enable Amazon CloudWatch Logs to capture application output, and configure Auto Scaling to adjust running tasks based on CPU or memory utilization. Updates would follow the same pattern: rebuild the image, push to the registry, and update the ECS task definition to redeploy.