60-Lesson Course Curriculum : Hands-on System Design with Java Spring Boot
Task Scheduler Implementation
Why This Course?
In the dynamic world of distributed systems, reliable task execution is paramount. From sending scheduled emails to processing nightly reports or managing complex data pipelines, an efficient and robust task scheduler is the backbone of many modern applications. This course isn't just about learning Spring Boot's @Scheduled annotation; it's about understanding the architectural considerations, trade-offs, and advanced patterns required to build a scalable, fault-tolerant, and observable task scheduling system from the ground up.
We'll demystify the complexities of distributed cron jobs, explore strategies for handling failures, and equip you with the insights to design systems that truly stand the test of time and scale. This is system design first - we don't just code, we architect solutions to real-world distributed systems challenges.
What You'll Build
You will progressively build a sophisticated, distributed task scheduling system using Java Spring Boot featuring:
Core task scheduling engine - Capable of handling various task types including one-off, recurring, and cron-based executions
Robust persistence layer - For storing task definitions and maintaining comprehensive execution history
Distributed coordination mechanisms - To prevent duplicate executions and ensure high availability across multiple instances using leader election and distributed locks
Monitoring and observability tools - To track task status, identify bottlenecks, and debug issues in real-time with comprehensive metrics and logging
Error handling and retry mechanisms - For graceful degradation and resilient task execution with circuit breakers and dead letter queues
User-friendly API - For defining, managing, and querying tasks with full REST endpoints and optional web interface
Message queue integration - Using Kafka or RabbitMQ for scalable, asynchronous task processing
Production-ready deployment - With containerization, Kubernetes support, and comprehensive security
Who Should Take This Course?
Fresh Computer Science & Engineering Grads - Gain a tangible, real-world project and foundational understanding of distributed systems
Software Engineers/Developers - Elevate your Spring Boot skills, master advanced scheduling patterns, and understand architectural trade-offs
Software Designers & Architects - Learn to design resilient and scalable task execution systems with informed architectural decisions
SRE & DevOps Engineers - Understand the inner workings of distributed schedulers to better monitor, deploy, and troubleshoot production systems
Data Engineers - Learn how to orchestrate data pipelines and manage complex ETL jobs effectively
Product and Engineering Managers - Gain deeper technical understanding to better estimate, plan, and manage projects involving asynchronous operations
What Makes This Course Different?
System Design First - Each lesson unravels a specific system design challenge related to task scheduling, followed by practical implementation in Spring Boot
Real-world Constraints and Trade-offs - We delve into difficult engineering decisions including CAP theorem implications, consistency models, resource contention, and fault tolerance
Hands-on, Progressive Building - Build a substantial, functional system piece by piece, seeing how each component integrates and contributes to overall robustness
Deep Dive into Distributed Concepts - Explore advanced topics like leader election, distributed locks, idempotency, backpressure, and circuit breakers in scheduling context
Focus on Observability and Reliability - Emphasize not just making it work, but making it observable, maintainable, and resilient in production environments
Daily Coding Constraints - Each lesson includes specific coding challenges that reinforce learning and build practical skills
Prerequisites
Intermediate Java Proficiency - Solid understanding of core Java concepts, OOP, and concurrent programming
Basic Spring Boot Knowledge - Familiarity with Spring Boot fundamentals, dependency injection, and REST controllers
Basic Database Understanding - SQL knowledge for RDBMS and understanding of NoSQL concepts
Familiarity with RESTful APIs - How to consume and expose them effectively
Basic Git Knowledge - For version control and collaborative development
Development Environment - Java 17+, Maven/Gradle, IDE such as IntelliJ IDEA, VS Code, or Eclipse
Course Structure
60 daily lessons, each designed as a digestible unit building upon previous knowledge. Each lesson includes:
Conceptual deep dive - Explaining the why and how of specific system design principles
Practical coding examples - Implementing concepts using Java Spring Boot with hands-on exercises
Think Like an Architect section - Discussing trade-offs, potential pitfalls, and alternative approaches
Production Ready tip - Practical advice for deploying and maintaining features in real-world scenarios
Daily Coding Constraint - Specific coding challenge or task to complete, reinforcing the day's learning
Complete Curriculum
Module 1: Foundations of Task Scheduling & Spring Boot Basics (Days 1-10)
Day 1: Introduction to Task Scheduling & Course Overview Concept: What is task scheduling? Why is it critical in modern applications? Overview of the system we'll build. Coding Constraint: Set up a basic Spring Boot project and create a simple "Hello, Scheduler!" application.
Day 2: Spring Boot's @Scheduled Annotation - The Starting Point Concept: Introduction to Spring's built-in scheduling capabilities including fixedRate, fixedDelay, and cron expressions. Coding Constraint: Implement three different scheduled tasks using fixedRate, fixedDelay, and cron expressions.
Day 3: Understanding ThreadPoolTaskScheduler and TaskExecutor Concept: How Spring manages threads for scheduled tasks and customizing thread pools for performance optimization. Coding Constraint: Configure a custom ThreadPoolTaskScheduler bean and observe its effect on task execution.
Day 4: Limitations of @Scheduled in a Distributed Environment Concept: The single-instance problem and why @Scheduled is insufficient for horizontally scaled applications. Coding Constraint: Simulate running two instances of your app with @Scheduled and observe duplicate executions.
Day 5: Introducing the Need for a Distributed Scheduler Concept: The challenges of distributed task execution including race conditions, single points of failure, and task visibility. Coding Constraint: Outline the high-level architecture of our distributed scheduler with components and interactions.
Day 6: Designing the Task Definition Model Concept: What information does a task need? Name, type, schedule, payload, and status considerations. Coding Constraint: Create a TaskDefinition POJO and a basic Spring Data JPA entity for it.
Day 7: Task Persistence - Choosing Your Database (RDBMS Focus) Concept: Why persist task definitions? Advantages of RDBMS for structured data and transactional consistency. Coding Constraint: Set up H2 or PostgreSQL with Spring Data JPA for TaskDefinition storage.
Day 8: Implementing a Basic Task Management API (REST) Concept: Exposing endpoints for creating, retrieving, updating, and deleting task definitions. Coding Constraint: Build a TaskDefinitionController with GET and POST endpoints.
Day 9: Dynamic Task Creation and Scheduling Concept: How to dynamically schedule tasks based on persisted definitions, not just static @Scheduled annotations. Coding Constraint: Implement a service that reads TaskDefinition from DB and schedules it using a TaskScheduler.
Day 10: Understanding Runnable and Callable for Task Execution Concept: The fundamental interfaces for encapsulating tasks in Java and their execution patterns. Coding Constraint: Refactor your dynamic scheduler to accept Runnable tasks based on your TaskDefinition.
Module 2: Achieving Distribution & Concurrency Control (Days 11-20)
Day 11: The Distributed Lock Problem Concept: Why we need distributed locks to prevent multiple instances from executing the same task simultaneously. Coding Constraint: Brainstorm different approaches to distributed locking including database, Zookeeper, and Redis solutions.
Day 12: Database-Backed Distributed Locks (Pessimistic Locking) Concept: Using database mechanisms like SELECT FOR UPDATE and unique constraints for simple distributed locks. Coding Constraint: Implement a basic distributed lock using a dedicated database table and JPA.
Day 13: Database-Backed Distributed Locks (Optimistic Locking) Concept: Using version columns for optimistic concurrency control in distributed environments. Coding Constraint: Implement optimistic locking for task execution status updates.
Day 14: Introduction to Redis for Distributed Locks Concept: Redis as a faster, more scalable option for distributed locks using SET NX/EX commands. Coding Constraint: Integrate Spring Data Redis and implement a simple Redis-based distributed lock.
Day 15: Redlock Algorithm - A Deeper Dive into Distributed Consensus Concept: Understanding the complexities of highly available distributed locks and Redlock principles. Coding Constraint: Discuss the challenges and trade-offs of implementing Redlock. Conceptual analysis of implementation approaches.
Day 16: Leader Election - The Core of a Single-Active Scheduler Concept: How one instance becomes the "leader" responsible for scheduling, while others remain on standby. Coding Constraint: Outline a simple leader election mechanism using a shared database table.
Day 17: Implementing Database-Backed Leader Election Concept: A practical implementation of leader election using a "lease" or "heartbeat" mechanism in the database. Coding Constraint: Implement the database-backed leader election logic with proper lease management.
Day 18: Handling Leader Failover and Re-election Concept: What happens when the leader crashes? Strategies for detecting failure and electing a new leader. Coding Constraint: Simulate leader failure and observe how your system handles re-election processes.
Day 19: Spring Cloud Commons - Distributed Coordination Abstractions Concept: Introduction to Spring Cloud's built-in abstractions for distributed systems coordination. Coding Constraint: Research Spring Cloud's Distributed Lock and Leader Election capabilities for future reference.
Day 20: Idempotency in Task Execution Concept: Ensuring that executing a task multiple times has the same effect as executing it once. Critical for retries and failures. Coding Constraint: Design a strategy for making task executions idempotent using unique execution IDs.
Module 3: Robustness, Reliability, and Error Handling (Days 21-30)
Day 21: Task Execution Tracking and History Concept: Why we need to log task executions including status, start/end times, and outcomes. Coding Constraint: Create a TaskExecution entity and persist execution details for each scheduled task.
Day 22: Designing for Task Statuses (Pending, Running, Succeeded, Failed) Concept: A state machine approach to task execution with proper status transitions. Coding Constraint: Implement an enum for TaskStatus and update the TaskExecution entity accordingly.
Day 23: Basic Error Handling for Tasks Concept: Catching exceptions within task execution and marking tasks as FAILED appropriately. Coding Constraint: Add a try-catch block around task execution logic to update status on failure.
Day 24: Automatic Retries with Exponential Backoff Concept: When and how to retry failed tasks and why exponential backoff is crucial for system stability. Coding Constraint: Implement a basic retry mechanism within the same process with limited retries.
Day 25: Advanced Retries with Spring Retry Concept: Leveraging Spring Retry for declarative and configurable retry policies. Coding Constraint: Integrate Spring Retry into your task execution logic with custom retry configurations.
Day 26: The Dead Letter Queue (DLQ) Pattern Concept: Where do tasks go after exhausting all retries? The importance of DLQs for analysis and manual intervention. Coding Constraint: Design a mechanism using a dedicated table or topic for "dead letter" tasks.
Day 27: Circuit Breaker Pattern with Resilience4j Concept: Protecting your scheduler from cascading failures caused by flaky external services. Coding Constraint: Integrate Resilience4j to wrap a simulated external call within a task execution.
Day 28: Handling Long-Running Tasks - Asynchronous Execution Concept: How to prevent blocking the scheduler for tasks that take a long time to complete. Coding Constraint: Refactor tasks to run in a separate TaskExecutor using @Async annotation.
Day 29: Timeout Mechanisms for Tasks Concept: Preventing tasks from running indefinitely and exhausting system resources. Coding Constraint: Implement a timeout mechanism for tasks using Future or ScheduledFuture.
Day 30: Graceful Shutdown of the Scheduler Concept: Ensuring tasks complete or are safely re-scheduled when the application shuts down. Coding Constraint: Implement a Lifecycle or DisposableBean to manage task shutdown gracefully.
Module 4: Asynchronous Messaging & Scalability (Days 31-40)
Day 31: Introduction to Message Queues (Kafka/RabbitMQ) Concept: Why message queues are essential for scalable, decoupled distributed systems. Coding Constraint: Set up a local Kafka or RabbitMQ instance using Docker.
Day 32: Sending Task Execution Requests to a Message Queue Concept: Decoupling task scheduling from task execution by using a message queue. Coding Constraint: Modify your scheduler to publish task execution requests to a message queue.
Day 33: Consuming Task Execution Requests from a Message Queue Concept: Building a separate "worker" service that consumes and executes tasks. Coding Constraint: Create a Spring Boot consumer application that listens to the message queue and executes tasks.
Day 34: Acknowledging Messages and Handling Failures in Consumers Concept: Ensuring message processing guarantees including at-least-once, at-most-once, and exactly-once delivery. Coding Constraint: Implement proper message acknowledgment and error handling in your consumer.
Day 35: Scaling Task Consumers Horizontally Concept: How message queues enable easy scaling of task processing by adding more consumers. Coding Constraint: Run multiple instances of your consumer and observe task distribution patterns.
Day 36: Task Prioritization with Message Queues Concept: Strategies for giving higher priority tasks preferential treatment using separate queues or custom logic. Coding Constraint: Discuss how to implement task prioritization using your chosen message queue.
Day 37: Handling Bursts and Backpressure Concept: Strategies for preventing system overload during peak load including flow control and consumer throttling. Coding Constraint: Implement a simple backpressure mechanism in your consumer with rate limiting.
Day 38: Batching Task Executions Concept: When and how to process tasks in batches for improved efficiency. Coding Constraint: Implement a batch processing mechanism for a specific task type.
Day 39: Event-Driven Task Scheduling Concept: Triggering tasks based on external events, not just time-based schedules. Coding Constraint: Design an event-driven task trigger such as S3 events or messages from another service.
Day 40: Microservices and the Scheduler as a Service Concept: How our scheduler can serve multiple microservices, acting as shared infrastructure. Coding Constraint: Discuss the API considerations for other services to interact with your scheduler.
Module 5: Observability, Management & Advanced Patterns (Days 41-50)
Day 41: Comprehensive Logging for Schedulers Concept: What to log, how to log effectively using structured logging, and logging best practices. Coding Constraint: Integrate SLF4J/Logback and implement structured logging for task events.
Day 42: Monitoring Task Health with Micrometer/Actuator Concept: Exposing metrics for task executions including success/failure rates, latencies, and active tasks. Coding Constraint: Use Spring Boot Actuator and Micrometer to expose custom task-related metrics.
Day 43: Visualizing Metrics with Prometheus and Grafana Concept: Setting up a monitoring stack to visualize the health of your scheduler. Coding Constraint: Spin up Prometheus and Grafana via Docker and connect to your application.
Day 44: Alerting on Task Failures Concept: How to configure alerts for critical task failures or performance degradation. Coding Constraint: Discuss how to set up alerts in Grafana or a separate alerting system.
Day 45: Centralized Log Management (ELK/Splunk) Concept: Aggregating logs from multiple instances for easy debugging and analysis. Coding Constraint: Discuss integrating with an ELK stack or similar for centralized logging.
Day 46: RESTful API for Task Management and Querying Concept: Building a comprehensive API for managing tasks, viewing status, and triggering ad-hoc executions. Coding Constraint: Enhance your API to allow querying task definitions and execution history.
Day 47: Web UI for Task Management (Basic Integration) Concept: A simple UI using Thymeleaf or basic React/Vue to interact with the scheduler API. Coding Constraint: Sketch out a basic UI or integrate a simple frontend for task management.
Day 48: Security Considerations for Task Schedulers Concept: Protecting your task definitions and execution endpoints with authentication and authorization. Coding Constraint: Implement basic Spring Security for your API endpoints.
Day 49: Testing Strategies for Distributed Schedulers Concept: Unit, integration, and end-to-end testing approaches for complex distributed systems. Coding Constraint: Write integration tests for your distributed lock and leader election components.
Day 50: Chaos Engineering for Schedulers (Conceptual) Concept: Proactively injecting failures to test system resilience and identify weaknesses. Coding Constraint: Brainstorm scenarios for chaos testing your scheduler including killing leader and network partitions.
Module 6: Advanced Topics & Production Readiness (Days 51-60)
Day 51: Dynamic Scaling of Scheduler Instances Concept: How to scale your scheduler instances up and down based on load automatically. Coding Constraint: Discuss auto-scaling strategies in cloud environments like AWS EC2/ECS and GCP GKE.
Day 52: Kubernetes and Containerization for Schedulers Concept: Deploying and managing your scheduler in a Kubernetes environment effectively. Coding Constraint: Create a Dockerfile for your Spring Boot application with proper optimization.
Day 53: Helm Charts for Kubernetes Deployment Concept: Packaging and deploying your application to Kubernetes using Helm for easier management. Coding Constraint: Outline the structure of a Helm chart for your application deployment.
Day 54: Handling Time Zones and Daylight Saving in Schedules Concept: The complexities of cron expressions and time zones, and how to manage them properly. Coding Constraint: Implement a mechanism to store and interpret time zones for tasks correctly.
Day 55: Cron Expression Generators and Validators Concept: Tools and libraries to help users create valid cron expressions and validate them. Coding Constraint: Integrate a cron expression parser/validator library into your application.
Day 56: Multi-Tenant Task Scheduling (Conceptual) Concept: Designing a scheduler that can serve multiple isolated clients or tenants securely. Coding Constraint: Discuss approaches for multi-tenancy including separate databases and tenant IDs.
Day 57: Serverless Functions for Ad-hoc Task Execution (e.g., AWS Lambda) Concept: Leveraging serverless platforms for executing short-lived or event-driven tasks. Coding Constraint: Design how your scheduler could trigger a Lambda function for specific task types.
Day 58: Performance Tuning and Optimization Concept: Identifying and resolving performance bottlenecks in your scheduler system. Coding Constraint: Profile your application using JVisualVM and identify areas for optimization.
Day 59: Future Trends in Task Scheduling Concept: Emerging technologies and patterns including Workflow Engines like Camunda and Temporal.io. Coding Constraint: Explore one emerging task scheduling or workflow technology and compare approaches.
Day 60: Course Wrap-up - Your Production-Ready Distributed Scheduler Concept: Recap of all concepts, holistic view of the system you've built, and next steps for continuous improvement. Coding Constraint: Review your entire codebase, identify any remaining improvements, and reflect on the comprehensive system you've created.
Key Topics Coverage Summary
Spring Boot Fundamentals - @Scheduled annotation, ThreadPoolTaskScheduler, TaskExecutor, and custom scheduler configuration
System Design Patterns - Leader election, distributed locks, sharding, event-driven architecture, and microservices coordination
Database Integration - JPA persistence, optimistic and pessimistic locking, connection pool optimization, and schema design
Message Queue Integration - Kafka and RabbitMQ for scalable task distribution, consumer scaling, and message acknowledgment patterns
Error Handling and Resilience - Retry mechanisms with exponential backoff, circuit breakers, dead letter queues, and graceful degradation
Observability - Comprehensive logging, metrics with Micrometer, monitoring with Prometheus/Grafana, and distributed tracing
Security and Compliance - Authentication, authorization, secure API design, and multi-tenancy considerations
Performance and Scalability - Thread pool optimization, horizontal scaling, auto-scaling, and performance profiling
Production Deployment - Containerization with Docker, Kubernetes deployment, Helm charts, and cloud platform integration
Testing and Quality - Unit testing, integration testing, chaos engineering, and production readiness validation
This comprehensive curriculum provides hands-on experience building enterprise-grade distributed task schedulers, progressing from basic Spring Boot concepts to advanced production deployment patterns used by leading technology companies.