🎯 Master Java memory leak detection through hands-on profiling with the Spring Boot memory leak demo
📚 Course Structure
- Module 1: Foundations - 2 hours (Focus: Memory leak theory and setup; Key learning: Understanding leak patterns; profiling infrastructure)
- Module 2: Profiling - 3 hours (Focus: Hands-on profiling mastery; Key learning: 21 profiling options; flamegraph interpretation)
- Module 3: Analysis - 2 hours (Focus: Systematic analysis; Key learning: Evidence documentation; prioritization frameworks)
- Module 4: Refactoring - 2 hours (Focus: Solution implementation; Key learning: Resource lifecycle patterns; coco=false fix)
- Module 5: Validation - 1 hour (Focus: Before/after comparison; Key learning: Quantitative validation; success measurement)
Total Duration: 8-12 hours (depending on learning path)
📚 Course details
Module 1: Memory Leak Foundations and Detection Setup (2 hours)
Learning Focus: Understanding memory leaks and setting up detection infrastructure
Key Topics:
- What are memory leaks and why they matter?
- Types of memory leaks in Java applications
- Introduction to the Spring Boot memory leak demo
- Setting up profiling infrastructure with system prompts
- Understanding the coco=true/false configuration pattern
Hands-on Activities:
- Explore the CocoController vs NoCocoController implementations
- Set up profiling scripts using @161-java-profiling-detect
- Run initial baseline profiling session
Learning Outcomes:
- Identify different types of memory leak patterns
- Set up automated profiling environment
- Understand the demo application architecture
Module 2: Hands-on Profiling with System Prompts (3 hours)
Learning Focus: Using system prompts to systematically collect profiling data
Key Topics:
- Deep dive into @161-java-profiling-detect system prompt
- Interactive profiling script walkthrough (21 profiling options)
- Memory leak detection strategies (5-minute vs 30-second profiles)
- JMeter load testing integration for realistic scenarios
- Understanding flamegraph interpretation
Hands-on Activities:
- Execute memory allocation profiling (Option 2)
- Run memory leak detection workflow (Option 8)
- Generate JMeter load tests for consistent profiling conditions
- Create comprehensive memory analysis workflow (Option 9)
Learning Outcomes:
- Master the interactive profiling script
- Generate meaningful profiling data under load
- Interpret flamegraph visualizations effectively
Module 3: Analysis and Evidence Collection (2 hours)
Learning Focus: Systematic analysis using @162-java-profiling-analyze
Key Topics:
- Systematic analysis framework for profiling data
- Problem categorization and severity assessment
- Evidence documentation with quantitative metrics
- Cross-correlation analysis techniques
- Impact vs Effort prioritization framework
Hands-on Activities:
- Analyze flamegraphs to identify memory leak patterns
- Create problem analysis documents following templates
- Develop prioritized solution recommendations
- Document evidence with specific file references
Learning Outcomes:
- Systematically analyze profiling results
- Create structured documentation for findings
- Prioritize fixes using Impact/Effort scoring
Module 4: Refactoring and Solution Implementation (2 hours)
Learning Focus: Implementing fixes based on analysis findings
Key Topics:
- Understanding the coco=false refactoring strategy
- Thread pool lifecycle management
- Bounded collections implementation
- Resource cleanup patterns (@PreDestroy)
- Deployment verification procedures
Hands-on Activities:
- Switch from CocoController to NoCocoController
- Verify code changes are properly applied
- Implement monitoring and alerting
- Test application stability after refactoring
Learning Outcomes:
- Apply systematic refactoring strategies
- Implement proper resource management patterns
- Validate refactoring through testing
Module 5: Validation and Comparison (1 hour)
Learning Focus: Using @164-java-profiling-compare to validate improvements
Key Topics:
- Before/after comparison methodology
- Quantitative metrics extraction
- Visual flamegraph comparison techniques
- Success criteria validation
- Documentation of improvements
Hands-on Activities:
- Generate post-refactoring profiling reports
- Perform side-by-side flamegraph comparison
- Create comparison analysis documentation
- Validate performance improvement targets
Learning Outcomes:
- Rigorously validate performance improvements
- Document quantified results
- Establish ongoing monitoring strategies
🛠️ Tools and Technologies
Primary Tools:
- async-profiler v4.1: Advanced profiling with flamegraph generation
- JFR (Java Flight Recorder): Low-overhead continuous profiling
- JMeter: Load testing for realistic profiling scenarios
- Spring Boot Actuator: Application monitoring and health checks
System Prompts Used:
- @161-java-profiling-detect: Setup and data collection
- @162-java-profiling-analyze: Systematic analysis framework
- @164-java-profiling-compare: Before/after validation
- @151-java-performance-jmeter: Load testing integration
Visualization Techniques:
- Flamegraphs: Call stack and allocation visualization
- Heatmaps: Temporal analysis of performance hotspots
- Memory usage charts: GC retention and heap growth patterns
- Thread dumps: Concurrency and threading analysis
🛠️ System Prompts Integration
This course demonstrates practical usage of three key system prompts:
@161-java-profiling-detect
Purpose: Data collection and problem identification
Usage: My Java application has performance issues - help me set up comprehensive profiling process using @161-java-profiling-detect and use the location examples/spring-boot-memory-leak-demo/profiler
@162-java-profiling-analyze
Purpose: Systematic analysis and solution development
Usage: Analyze the results located in examples/spring-boot-memory-leak-demo/profiler and use the cursor rule @162-java-profiling-analyze
@164-java-profiling-compare
Purpose: Before/after validation and improvement measurement
Usage: Review if the problems was solved with last refactoring using the reports located in @/results with the cursor rule @164-java-profiling-compare
🎯 Key Concepts Covered
Memory Leak Patterns
- ✅ Unbounded collection growth
- ✅ Thread pool resource leaks
- ✅ Missing lifecycle management
- ✅ Cache leaks and retention issues
Profiling Techniques
- ✅ async-profiler mastery (21 profiling options)
- ✅ JFR analysis and interpretation
- ✅ Flamegraph visual analysis
- ✅ Load testing integration with JMeter
Enterprise Patterns
- ✅ Bounded collections with graceful degradation
- ✅ Shared resource management
- ✅
@PreDestroylifecycle patterns - ✅ Monitoring and alerting infrastructure
Analysis Methodologies
- ✅ Problem-driven profiling strategies
- ✅ Impact/Effort prioritization frameworks
- ✅ Cross-correlation analysis techniques
- ✅ Evidence-based documentation
🔍 Interactive Elements Throughout the Course
🧠 Knowledge Checks
- "Before we continue, can you explain why GC retention grows with active memory leaks?"
- "What would happen if we didn't implement @PreDestroy in our thread pools?"
- "How do you interpret a flamegraph where the canvas height keeps growing?"
💡 Learning Reinforcement
- "Notice how the NoCocoController eliminates the memory leak - that's the power of proper resource lifecycle management!"
- "This connects to our earlier lesson on bounded collections - remember the MAX_OBJECTS pattern?"
- "The 318% memory retention increase we observed demonstrates why systematic profiling is critical!"
🎯 Practical Challenges
- Implement custom bounded collections with error handling
- Design monitoring strategies for production memory leak detection
- Create custom profiling configurations for specific scenarios
- Develop team knowledge transfer materials
📊 Success Metrics and Validation
Technical Success Criteria
- [ ] Memory leaks successfully detected and resolved
- [ ] Quantified performance improvements documented
- [ ] Systematic profiling workflow mastered
- [ ] Production-ready monitoring strategy developed
Learning Validation Methods
- [ ] Hands-on exercises completed successfully
- [ ] Profiling reports generated and analyzed
- [ ] Documentation created following professional templates
- [ ] Knowledge check questions answered correctly
Real-World Application
- [ ] Techniques applied to actual production applications
- [ ] Team knowledge sharing sessions conducted
- [ ] Performance monitoring integrated into CI/CD pipeline
- [ ] Continuous improvement processes established
🎓 Course Philosophy
Progressive Learning Design
This course follows the @behaviour-progressive-learning pattern:
- Extract core concepts from system prompts
- Structure content into progressive learning modules
- Create interactive exercises with guided solutions
- Generate comprehensive courses with multiple paths
- Provide assessments and validation checkpoints
"The best time to learn performance optimization was yesterday. The second best time is now."
Happy profiling! Go to the foundations 🚀