WEEK 1 – Performance Testing & Engineering Foundations

• Introduction to Performance Testing vs Performance Engineering
• Performance Engineering roadmap
• Performance Engineering lifecycle & process
• Roles & responsibilities of a Performance Engineer
• Skills required & career path in Performance Engineering

• A startup runs only load tests before release and faces outages in production.
  ➜ Identify what Performance Engineering activities are missing.

• Understanding Performance SLAs (simple & complex systems)
• Common performance issues (CPU, memory, IO, network)
• Common mistakes in performance test execution & monitoring
• Production traffic analysis (intro)

• The application meets the SLA in testing but fails in production during festivals.
  ➜ Identify reasons & missing SLA considerations.

• How to create test data for performance testing
• Production traffic analysis (deep dive)
• Workload fundamentals
• Hands-on: basic workload & test data preparation

• Load test passes with dummy data but fails with real data.
  ➜ Identify data-related performance risks.

• How to analyze performance test results
• User-level vs operational-level vs system-level analysis
• Key KPIs & metrics correlation
• Hands-on: result analysis walkthrough

• Response time increases after 200 users, despite the CPU being low.
  ➜ Find possible bottlenecks.

• Performance test environment creation
• Architecture design phase in Performance Engineering
• Hardware sizing basics
• Traditional vs Cloud performance testing

• The performance test environment is smaller than production.
  ➜ Identify risks & mitigation strategy.

WEEK 2 – Performance Engineering, Capacity & Monitoring

• Workload modelling from scratch
• Workload modelling vs capacity planning
• When to use which approach
• Hands-on: workload modeling exercise

• ECommerce application expects 5× traffic in 3 months.
  ➜ Build workload growth model.

• Network performance & optimization
• Load balancing concepts & best practices
• Building efficient capacity models
• Scaling strategies for large systems

• App scales well till 8 cores, but not beyond.
  ➜ Identify scalability limits.

• In-depth system performance analysis
• Identifying bottlenecks across tiers
• Monitoring strategies & best practices

• CPU spikes, but throughput drops. Reasons for low CPU with an increase in load
  ➜ Analyse what’s really happening.

• Windows performance issues
• PerfMon counters – CPU, memory, disk, network
• Configuring PerfMon counters
• Real-time monitoring & Event Viewer
• Hands-on: Windows server monitoring

• Windows server shows high CPU but low application throughput.
  ➜ Analyze PerfMon counters.

• Linux performance issues
• Linux monitoring utilities:
• vmstat
• iostat
• top
• free
• netstat
• CPU, memory, and network issue detection
• Hands-on: Linux server monitoring

• Linux CPU is idle but response time is high.
  ➜ Identify hidden bottleneck.

WEEK 3 – Java Performance Engineering (Basic → Advanced)

• JVM types & importance
• JVM architecture internals
• Heap vs Stack memory
• Eden, Survivor, Old Gen, PermGen/Metaspace

• App crashes only after running for 6 hours.
  ➜ Identify possible memory issue.

• Memory leaks & OutOfMemory errors
• Defining right heap size for load testing
• JVM CPU, memory, disk & network usage
• Threads & thread lifecycle
• Thread pooling & JDBC connection pooling

• Requests hang, but CPU is low.
  ➜ Identify thread-related causes.

• JVM performance monitoring concepts
• Open-source tools: jmap, JConsole, JVisualVM, Visual GC
• Commercial tools: YourKit, JProfiler, JProbe, JMC
• Hands-on: JVM monitoring demo

• Response time spikes every 5 minutes.
  ➜ Identify GC patterns.

• Heap dumps & analysis basics
• Thread dumps & analysis basics
• Collecting heap & thread dumps
• GC overview & phases
• GC logs collection & analysis

• Sudden OOM in production.
  ➜ Identify whether a leak or a configuration issue.

• Garbage collectors: Serial, Parallel, CMS, G1, Shenandoah, ZGC, Epsilon
• GC tuning strategies
• Memory leak vs OOM errors
• Thread pools, locks & thread tuning
• Hands-on: Java app deployment + sample load test

• App performs worse after increasing the heap size.
  ➜ Explain why.

WEEK 4 – .NET, Database, Frontend & Real-World Issues

• Real-time .NET performance problems
• .NET performance metrics & measurement
• .NET memory management
• Profiling: sampling vs event-based
• Tools: DotTrace, ANTS, CLR Profiler, SQL Profiler

• .NET app slows down over time.
  ➜ Identify GC & memory causes.

• SOH vs LOH
• Workstation GC vs Server GC
• GC pause time analysis
• IIS application pool tuning
• ASP.NET optimization techniques
• Hands-on: .NET application profiling

• IIS recycles frequently under load.
  ➜ Identify root cause.

• High CPU due to code spinning
• CPU scalability & blocked threads
• High response time issues
• Memory & network latency issues
• Client-side vs server-side bottlenecks

• The performance issue appears only at night.
  ➜ Identify environmental factors.

• Java & .NET tuning best practices
• Test environment best practices
• Application performance monitoring best practices
• Practical exercises with best-practice implementation

• Monitoring was added after the outage.
  ➜ Propose a proactive monitoring strategy.

• Oracle & SQL Server monitoring
• SGA, PGA, Buffer Cache
• AWR, ASH, ADDM reports
• Query optimization techniques
• MySQL performance issues & tuning

• DB CPU is high but app CPU is low.
  ➜ Identify DB-side issues.

• Practical AWR, ASH & ADDM analysis
• Identifying DB bottlenecks
• Long-running SQL optimization
• Understanding ADDM recommendations

• Query runs fast alone, but is slow in production.
  ➜ Identify concurrency issues.

• Importance of front-end performance
• Browser rendering & critical metrics
• Chrome DevTools, GTMetrix, WebPageTest, PageSpeed, YSlow
• Front-end optimization techniques
• JavaScript performance analysis
• Final recap + real-world performance engineering mindset
• Course Summary with Lab Assignments
• Interview Preparation Tips
• Next Steps & Career Guidance

• Full-stack performance issue (Frontend + App + DB + OS).
  ➜ Identify bottleneck, propose tuning & monitoring strategy.