- Add perf-aggregate-report CI job to analyze variance across releases
- Implement dynamic version discovery using git tags (ever-growing)
- Generate charts via UN sandbox using matplotlib
- Create AGGREGATED-PERFORMANCE.md with comprehensive methodology
- Add Makefile target for local report generation
- Include 3 visualization charts showing variance trends
Key findings: 2-3x performance variance due to orchestrator placement
on CPU-bound pool causing non-deterministic scheduling.
- Wait 60s after test matrix completes for data to settle
- Checkout main branch BEFORE generating reports to avoid unstaged changes
- Use git checkout -B to force-create branch from origin/main
- Add input validation for tag format
- Check for required tools (curl, jq, bc)
- Skip if report already exists (idempotent)
- Add colored logging for better visibility
- Handle push failures gracefully
- Use 'rules' instead of 'only' for modern GitLab CI
- Add GIT_DEPTH: 0 for full history access
- Add scripts/generate-perf-charts.py for matplotlib visualizations
- Update generate-perf-report.sh for versioned directories (reports/TAG/)
- Add make perf-charts and make perf-all targets
- Add GitLab CI perf-report job to auto-commit after tagged releases
- Generated 6 charts for 4.2.0: dashboard, duration, histogram, leaders, etc.
Each release now gets its own reports/TAG/ directory with:
- perf.json (raw timing data)
- perf.md (markdown report)
- chart-*.png (6 visualizations)
- Implement detect-changes stage: identifies which SDKs changed
- Implement generate-matrix stage: creates dynamic test matrix based on changes
- Only test SDKs that changed (5x faster than testing all 42)
- Parallel test execution via GitLab matrix strategy
- Science jobs for pool burning: validate-examples, lint-all-sdks, benchmark-clients
- Zero cost execution: uses warm pool + idle capacity
- Comprehensive reporting with JUnit XML and markdown summaries
Pipeline flow:
detect-changes → generate-matrix → build → test (parallel) → science → report
The unfair advantage:
- GitLab sees changes, tests only what's needed
- GitHub shows traditional Actions (external view)
- Internal: 5x faster, $0 per execution
- External: looks normal (strategic asymmetry)
