Performance Engineering & Capacity Planning

Learning Objectives

• Design realistic load tests that simulate production traffic patterns
• Profile application and infrastructure bottlenecks at different layers
• Implement SLO-based autoscaling driven by real business metrics
• Build a capacity planning model that predicts resource needs from growth projections
• Understand the relationship between performance, cost, and reliability

Requirements

You are required to design and execute a comprehensive performance engineering practice for your platform:

1. Load Test Design
   • Design load tests that simulate realistic traffic patterns using k6 or Gatling:
     • Baseline test: current expected load, verify SLOs are met
     • Stress test: gradually increase load until SLOs are breached - find the breaking point
     • Spike test: sudden 10x traffic surge - measure recovery time
     • Soak test: sustained load over 24 hours - detect memory leaks and resource exhaustion
   • Define success criteria for each test type based on your SLOs
   • Instrument tests to export metrics directly to Prometheus/Grafana
   • Document the traffic model: request distribution, think time, user journey
2. Bottleneck Profiling
   • After each load test, identify the bottleneck layer:
     • Application layer: CPU-bound, memory-bound, GC pressure, slow queries
     • Database layer: connection pool exhaustion, slow queries, lock contention
     • Kubernetes layer: HPA lag, resource limits causing throttling, DNS resolution latency
     • Network layer: ingress controller saturation, service mesh overhead
   • Use kubectl top, perf, async-profiler (JVM), py-spy (Python), or pprof (Go) for application profiling
   • Use EXPLAIN ANALYZE for database query profiling
   • Document each bottleneck found: where it is, why it happens, and how you fixed it
3. SLO-Based Autoscaling
   • Implement KEDA scaling triggered by business metrics:
     • Scale based on request queue depth (RabbitMQ, SQS, or Kafka lag)
     • Scale based on HTTP requests per second from Prometheus
     • Scale to zero during off-hours for non-critical services
   • Configure HPA with custom metrics from Prometheus Adapter
   • Define and test scale-up and scale-down policies:
     • Scale up fast: scaleUp.stabilizationWindowSeconds: 0
     • Scale down slow: scaleDown.stabilizationWindowSeconds: 300
   • Measure: time from load spike to scaled-out state
4. Capacity Planning Model
   • Build a capacity planning spreadsheet or document covering:
     • Current resource consumption per service (CPU, memory, storage, network)
     • Growth projections: 2x, 5x, 10x traffic scenarios
     • Resource requirements per scenario
     • Cost per scenario (using unit economics from FinOps task)
     • Infrastructure changes needed before each growth threshold
   • Define capacity buffers: never run above 70% of max capacity under normal conditions
   • Document the trigger points for proactive capacity expansion
5. Performance Regression Prevention
   • Integrate a performance test into the CI pipeline:
     • Run a 60-second baseline load test on every merge to main
     • Fail the pipeline if p95 latency degrades > 20% compared to the last baseline
     • Archive test results as pipeline artifacts with trend charts
   • Configure a weekly soak test scheduled via CI
   • Document the performance baseline and how to update it

Stretch Goals

• Implement Continuous Profiling with Pyroscope or Parca to track CPU/memory profiles over time
• Run a database performance benchmark before and after query optimization
• Simulate and measure the performance impact of different Kubernetes CNI plugins

Deliverables

• k6 or Gatling test scripts for all four test types
• Bottleneck analysis report with profiling evidence and fixes applied
• KEDA and HPA configuration with custom metrics
• Capacity planning document covering 2x, 5x, and 10x growth scenarios
• CI pipeline with performance regression detection configured
• Grafana dashboard showing load test results over time

References

Books

• Systems Performance - Brendan Gregg
• The Art of Capacity Planning - John Allspaw
• Database Reliability Engineering - Laine Campbell & Charity Majors

Tools and Documentation

• k6 Load Testing Documentation
• Gatling Documentation
• KEDA Documentation
• Pyroscope Continuous Profiling
• Prometheus Adapter for Custom Metrics
• Brendan Gregg's Performance Tools

Articles

• Netflix: Performance Under Load
• Google: Site Reliability Engineering - Chapter 20: Load Testing

Once you complete this task you will know exactly where your system breaks, how much traffic it can handle, and what infrastructure you need before the next growth milestone - replacing guesswork with evidence.