Approach summary (SDET perspective)
Introduce performance tests as first-class jobs in the automation ecosystem but keep them logically separated from fast functional runs. Use the functional framework for test definitions where helpful, but run load/latency workloads on dedicated runners and infra to avoid noisy neighbors.
Architectural choices
- Separate runners (recommended): functional runner for unit/integration tests; dedicated performance runner(s) (k6/Gatling/JMeter) on isolated nodes or k8s pods. Pros: stable baselines, controllable resource allocation, easier scaling. Cons: additional infra.
- Same runner (only for lightweight latency/smoke): reuse framework harness to validate endpoints quickly; not suitable for high-concurrency load.
Resource isolation
- Run performance jobs on dedicated VMs/k8s namespaces with CPU/memory limits; use separate networks or subnets to emulate real topology.
- Use traffic shaping (tc/chaos/latency proxies) to measure client-side latency.
- Tag and pin runners to avoid co-located CI workloads; use spot instances for non-critical runs.
Data collection & aggregation
- Emit metrics from clients and target services to Prometheus via Pushgateway or exporters.
- Capture request/response times, error rates, throughput, system metrics (CPU, memory, GC, thread pools), and network stats.
- Store raw traces/logs in a time-series DB (Prometheus) and long-term storage (Elasticsearch/S3). Visualize in Grafana; keep run metadata in a test results DB for correlation.
Thresholding & failure semantics
- Define tiers: smoke latency gates (fast-fail), load thresholds (soft gates), and SLA breaches (hard fail).
- Use statistical thresholds (p95/p99) and error-rate limits. Example: fail build if p95 latency > 500 ms AND error rate > 1% for 2 consecutive runs.
- Implement anomaly detection and rollback rules for canaries; allow “warning” status that notifies but doesn’t block for flaky conditions.
CI gating strategies
- PR pipeline: unit + functional + lightweight performance smoke (single-user latency).
- Merge/master: nightly full load/stress runs; results posted to dashboards and issues created on regressions.
- Release/canary: run targeted performance suites against canary environment with strict pass/fail; gate production promotion on critical SLA metrics.
- Use progressive gating: require trending stability over N runs before blocking.
Cost & infra considerations
- Use auto-scaling k8s clusters and spot instances for large scale; schedule heavy runs off-hours.
- Right-size scenarios: synthetic population vs full-traffic replay; sample lower concurrency for CI and reserve full scale for nightly/regression windows.
- Reuse containers/images to reduce startup cost; archive artifacts to S3 to avoid repeated data transfer.
Example stack
- Test harness: pytest / Java test runner for definitions
- Load: k6 (scripts in repo) or Gatling
- Metrics: Prometheus + Grafana, ELK for logs
- Orchestration: GitHub Actions/Jenkins triggers, k8s for runners
Why this works: separates concerns, ensures repeatable baselines, provides fast feedback in PRs while enabling realistic, scalable stress testing with robust observability and controlled cost.
