Course Objective & Scope
An application that passes every functional test can still fail during traffic peaks when connection pools saturate, slow queries pile up or memory leaks surface. This course teaches how to measure system capacity limits and pinpoint bottlenecks before release using Apache JMeter.
It covers performance metrics (p95/p99 latency, throughput, error rate, Apdex), designing load, stress, spike and soak tests, JMeter Thread Groups, Samplers, CSV Data Sets, dynamic correlation with JSON/Regex Extractors, Groovy/JSR223 scripting, non-GUI CLI and distributed (master-worker) load generation, live dashboards with InfluxDB/Grafana, and correlating findings with APM and infrastructure metrics.
Who Should Attend?
- QA, test automation and performance engineers running load and stress tests
- Backend developers validating service scalability and SLA/SLO targets
- DevOps engineers, SREs and architects responsible for capacity planning
- Prerequisite: familiarity with HTTP, REST APIs and basic web architecture
Key Takeaways
- Model realistic production workloads including concurrency, target RPS, ramp-up and think times
- Build dynamic JMeter test plans with token/session correlation and parameterised test data
- Generate high load reliably in non-GUI CLI mode and across distributed master-worker clusters
- Interpret p90/p95/p99 latency percentiles, throughput plateaus and error curves accurately
- Correlate response-time degradation with CPU, memory, GC, thread-pool and database bottlenecks
Curriculum
Performance Engineering Fundamentals and Workload Modelling
- Goals of Load, Stress, Spike, Soak (Endurance) and Scalability tests
- Little's Law, concurrent virtual users (VUs) vs requests per second (RPS) and SLA/SLO targets
- Why averages lie: reading medians, p90, p95, p99 percentiles and latency distributions
Apache JMeter Architecture and Scenario Building
- Test Plans, Thread Groups (Open Model, Arrivals, Concurrency), Samplers, Logic Controllers and Timers
- Simulating realistic client behaviour with HTTP Header, Cookie and Cache Managers
- Data parameterisation with CSV Data Set Config and Response/Duration Assertions
Dynamic Correlation and Advanced JMeter Scripting
- Capturing dynamic tokens and IDs with JSON, Boundary and Regular Expression Extractors
- Chaining OAuth2/JWT authentication flows and multi-step business transactions
- High-throughput custom logic using JSR223 + Groovy instead of legacy BeanShell
High-Volume Load Generation, Live Monitoring and Distributed Runs
- JMeter sizing rules: why GUI mode is only for authoring, CLI execution and HTML Dashboards
- Distributed Master-Worker architecture and scaling load generators on Docker/Kubernetes
- Streaming real-time metrics to InfluxDB / Prometheus and Grafana via Backend Listeners
Bottleneck Analysis, Reporting and CI/CD Performance Gates
- Recognising signatures of network, thread-pool, GC, external-API and database bottlenecks
- Automated performance regression thresholds in CI/CD pipelines
- Hands-on lab: running a load test, diagnosing bottlenecks and writing an executive performance report
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