Reliable Job Queue

A production-grade distributed job processing system built using Spring Boot, Apache Kafka, Redis, PostgreSQL, and Docker. The platform enables asynchronous execution of long-running tasks with automatic retries, fault tolerance, distributed locking, persistent job tracking, and horizontal scalability.

Instead of processing heavy requests synchronously, jobs are queued and executed by worker services, ensuring high throughput, system reliability, and improved user experience.

✨ Key Features

⚡ Asynchronous Job Processing
📬 Apache Kafka Message Queue
🔄 Automatic Retry with Exponential Backoff
💀 Dead Letter Queue (DLQ)
🔒 Distributed Locking using Redis
📊 Persistent Job Tracking
📈 Horizontal Worker Scaling
🐳 Dockerized Deployment
🛡 Fault-Tolerant Architecture
🔁 Idempotent Job Processing

🏗 Architecture

Insert the architecture diagram here.

🔄 End-to-End Workflow

User Request
      │
      ▼
Spring Boot REST API
      │
Validate Request
      │
Generate Job ID
      │
Store Job (QUEUED)
      │
Publish Event
      ▼
Apache Kafka Topic
      │
      ▼
Worker Service
      │
Lock Job (Redis)
      │
Process Job
      │
Store Result
      │
Update Status
      ▼
PostgreSQL
      │
      ▼
Client Polls Status API
      │
      ▼
Completed Result

🔁 Job Lifecycle

QUEUED
   │
   ▼
PROCESSING
   │
   ├────────► COMPLETED
   │
   ▼
FAILED
   │
Retry?
   │
 ├── Yes ─────► QUEUED
 │
 └── No ──────► DEAD LETTER QUEUE

🔄 Retry Strategy

Failed jobs are retried automatically using Exponential Backoff to avoid overwhelming downstream services.

Attempt 1
   │
Wait 2 Seconds
   │
Attempt 2
   │
Wait 4 Seconds
   │
Attempt 3
   │
Wait 8 Seconds
   │
Move to Dead Letter Queue

⚙ Tech Stack

Backend

Java
Spring Boot
Spring Web
Spring Data JPA

Messaging

Apache Kafka
Kafka Consumer Groups

Database

PostgreSQL

Caching & Coordination

Redis
Distributed Locks

Infrastructure

Docker
Docker Compose

📂 Project Modules

Reliable-Job-Queue
│
├── API Service
│    ├── Job Controller
│    ├── Job Service
│    ├── Kafka Producer
│    └── Status API
│
├── Worker Service
│    ├── Kafka Consumer
│    ├── Job Processor
│    ├── Retry Handler
│    └── Dead Letter Handler
│
├── Shared Library
│    ├── DTOs
│    ├── Events
│    └── Common Utilities
│
├── Redis
├── PostgreSQL
└── Docker

Core Components

API Service

Receives client requests, validates input, generates unique job IDs, stores job metadata, and publishes events to Kafka for asynchronous processing.

Kafka Message Broker

Acts as the central event bus, decoupling producers from consumers and enabling scalable, fault-tolerant communication.

Worker Service

Consumes queued jobs, acquires distributed locks, executes processing, updates job status, and stores results.

Redis

Provides distributed locking, fast caching, and coordination to prevent duplicate processing across multiple workers.

PostgreSQL

Persists job metadata, execution history, retry counts, processing status, and final results.

🛡 Reliability Features

At-least-once message delivery
Idempotent job execution
Automatic retry mechanism
Dead Letter Queue for failed jobs
Distributed locking with Redis
Persistent job recovery after failures
Horizontal scaling with multiple workers
Fault-tolerant asynchronous architecture

📊 Job States

🟡 QUEUED
🔵 PROCESSING
🟢 COMPLETED
🔴 FAILED
⚫ DEAD LETTER QUEUE

🎯 Use Cases

AI Code Generation
Resume Analysis
PDF Processing
Email & Notification Services
Video/Image Processing
Report Generation
Payment Processing
Background Data Synchronization

🔮 Future Enhancements

Kubernetes deployment
WebSocket-based live job updates
Prometheus & Grafana monitoring
Scheduled and recurring jobs
Job priority queues
Multi-tenant support
Admin dashboard with analytics
OpenTelemetry distributed tracing

📌 Conclusion

Reliable Job Queue demonstrates how modern distributed systems handle high-volume asynchronous workloads efficiently. By combining Spring Boot, Apache Kafka, Redis, PostgreSQL, and Docker, the platform provides reliable message processing, automatic retries, distributed coordination, and scalable worker execution. It reflects production-ready backend engineering practices used in large-scale systems such as e-commerce, fintech, cloud platforms, and AI applications.