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taskscheduler

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60-Lesson Course Curriculum : Hands-on System Design with Java Spring Boot - TaskSechuler implementation

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- - .gitignore
  - Dockerfile
  - pom.xml
  - README.md.backup
  - SCRIPTS_README.md
  - start.sh
  - status.sh
  - stop.sh
- - .gitignore
  - docker-compose.yml
  - docker-start.sh
  - docker-stop.sh
  - Dockerfile
  - pom.xml
  - README.md
  - start.sh
  - stop.sh
- - .gitignore
  - .zshrc
  - docker-compose.yml
  - pom.xml
  - start.sh
  - stop.sh
- - mvnw
  - mvnw.cmd
  - pom.xml
  - README.md
- - Dockerfile
  - pom.xml
  - README.md
  - start-docker.sh
  - start.sh
  - stop-docker.sh
  - stop.sh
README.md

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Days 1–5 of the 60-lesson course: Hands-on System Design with Java Spring Boot — Task Scheduler implementation.

Paid lessons (days 6–60) live in [taskscheduler-p](https://github.com/sysdr/taskscheduler-p).

Access this course here : https://javatsc.substack.com/
# Hands-on Task Scheduler Implementation with Java Spring Boot: Ultra Scalable Systems

This course provides an in-depth, hands-on journey into designing and implementing ultra-scalable task schedulers using Java Spring Boot. You will learn every core system design concept, explore diverse use cases, and gain practical experience through daily code lessons, aiming to handle millions of requests per second.

## Course Highlights

### What is this course about?

This course is a deep dive into building robust, highly scalable, and fault-tolerant task scheduling systems using Java and Spring Boot. It covers fundamental scheduling concepts, distributed system design principles, advanced Spring features, persistence strategies, monitoring, and operational best practices to handle massive workloads.

### Why learn Task Scheduler Implementation?

In modern applications, managing background jobs, recurring tasks, and asynchronous operations efficiently is crucial. Learning to implement a scalable task scheduler allows you to build systems capable of processing millions of events, automating business processes, performing batch operations, and ensuring data consistency in complex distributed environments. This skill is vital for backend engineers, system architects, and anyone dealing with high-throughput, event-driven systems.

### Who is this course for?

- **Backend Developers (Java/Spring Boot):** Looking to build robust and scalable background processing capabilities.
- **System Architects:** Designing distributed systems that require reliable task execution.
- **DevOps Engineers:** Responsible for deploying, managing, and monitoring high-performance applications.
- **Data Engineers:** Implementing batch processing, ETL pipelines, and data synchronization tasks.
- **Anyone** interested in distributed systems, concurrency, and building high-throughput services.

### What's different about this course?

- **Hands-On Focus:** Every concept is reinforced with practical, runnable Java/Spring Boot code examples and exercises.
- **Ultra Scalability:** Emphasizes design patterns, architectural choices, and performance tuning techniques to handle millions of requests per second.
- **Comprehensive System Design:** Integrates distributed system concepts (consistency, fault tolerance, leader election, distributed locks) directly into scheduler implementation.
- **Real-world Use Cases:** Explores how large-scale task schedulers are used in various industries and applications.
- **Structured Learning:** A meticulously organized curriculum with 185 distinct lessons for a thorough understanding.

### Key Topics Covered

- Core Scheduling Concepts (`@Scheduled`, Cron, Fixed-Rate, Fixed-Delay)
- Concurrency and Thread Pool Management in Spring Boot
- System Design for Distributed Schedulers (CAP Theorem, Consistency Models)
- Leader Election Mechanisms (ZooKeeper, Consul, Database-based)
- Distributed Locks and Mutual Exclusion
- Task Persistence and State Management (Database, Redis, etc.)
- Fault Tolerance, Retries, and Idempotency
- Monitoring, Logging, and Alerting for Distributed Systems
- Performance Tuning and Bottleneck Identification
- Security Considerations for Task Execution
- Deployment Strategies (Docker, Kubernetes, Cloud Platforms)
- Integration with Messaging Queues (Kafka, RabbitMQ) for Task Distribution
- Building Custom Distributed Scheduling Frameworks
- Real-time Task Processing and Event-Driven Architectures
- Batch Processing and Workflow Orchestration