Designing Performance Testing and Benchmarking
1. Designing Load Testing Strategy
| Element | Detail |
|---|---|
| Goal | Verify system at expected load |
| Workload model | Mix of operations matching prod |
| Tools | k6, Gatling, JMeter, Locust, Artillery |
| Ramp-up | Gradual to target RPS |
| Hold | Steady-state for 15+ min |
2. Designing Stress Testing
| Aspect | Detail |
|---|---|
| Goal | Find breaking point |
| Beyond capacity | Push past 100% |
| Observe | Where it fails first; recovery |
| Document | Thresholds for capacity planning |
3. Designing Spike Testing
| Aspect | Detail |
|---|---|
| Pattern | Sudden 10–100× burst |
| Validate | Auto-scale lag, queue, rate limit |
| Recovery | System returns to normal post-spike |
| Use case | Black Friday, virality |
4. Designing Soak Testing
| Aspect | Detail |
|---|---|
| Duration | Hours to days |
| Find | Memory leaks, fd leaks, gradual degradation |
| Monitor | Heap, GC, conn count over time |
5. Designing Performance Baselines
| Practice | Detail |
|---|---|
| Baseline per release | Comparable workload |
| Track over time | Detect regressions |
| Per-endpoint | p50/p95/p99 + RPS |
| CI gate | Fail PR on regression > threshold |
6. Designing Capacity Planning
| Step | Detail |
|---|---|
| Forecast load | Trend + business growth |
| Single-instance capacity | From load test |
| Headroom | Run at ~50% to absorb spikes |
| Buffer for failure | N+1 / N+2 nodes |
| Cost projection | Tie to FinOps |
7. Designing Performance Monitoring During Tests
| Signal | Detail |
|---|---|
| App: latency, errors, throughput | Per endpoint |
| JVM / runtime | GC, threads, heap |
| Infra | CPU, mem, disk, net |
| DB | Slow queries, connections, locks |
| Cache hit rate | During load |
8. Designing Scalability Testing
| Aspect | Detail |
|---|---|
| Linear scale check | Double nodes → ~2× throughput? |
| Bottleneck shift | Where does saturation move |
| Sharding validation | Per-shard scale linear |
| DB scale ceiling | Often the limit |
9. Designing Benchmarking Methodology
| Practice | Detail |
|---|---|
| Warm-up | JIT, caches, pools |
| Multiple runs | Report median + variance |
| Isolated env | Avoid noisy neighbors |
| JMH for micro | Java micro-benchmark harness |
| Apples-to-apples | Same data, queries, hardware |
10. Designing Bottleneck Identification
| Tool | Detail |
|---|---|
| Flamegraphs | CPU profiling (async-profiler, Pyroscope) |
| DB query analysis | EXPLAIN, pg_stat_statements |
| USE method | Util / Saturation / Errors per resource |
| Distributed traces | Find slow span |
| Little's Law | L = λ × W to compute capacity |
11. Designing Chaos and Resilience Testing
| Test | Detail |
|---|---|
| Latency injection | Verify timeouts work |
| Pod kill | Replicas + draining |
| DB failover | Reconnection |
| Partial outage | One AZ down |
| Game days | Team-wide drills |
12. Designing Production Load Testing
| Approach | Detail |
|---|---|
| Shadow traffic | Mirror prod to test fleet |
| Dark launches | Run new code without exposing |
| Canary stress | Push extra load to canary |
| Scheduled tests | Off-peak window |
| Safety | Tagged traffic; per-test kill switch |