# cc-ci Phase 2b — Test performance: measure, attribute, improve (Autonomous Build Plan) **Status:** QUEUED — starts after Phase 2 (`plan-phase2-recipe-tests.md`) reaches `## DONE`, and runs **before** Phase 3 (`plan-phase3-results-ux.md`). **Transition:** **manual** (operator kicks it off). **Builds on:** Phase 1 (runner, Drone, harness, `MAX_TESTS`) + Phase 2 (the full per-recipe suites — the *real workload* we're optimizing). **Owner agents:** same Builder + Adversary loops + protocol as Phase 1 (`plan.md` §6/§7). Here the Adversary's job is to **independently re-measure** claimed speed-ups and ensure **no test was weakened or isolation broken** to gain them. **This file's path:** `/srv/cc-ci/cc-ci-plan/plan-phase2b-test-performance.md` --- ## 0. Why this phase Runs are slow — a Keycloak test took ~30 minutes (the seed observation). Before we polish results (Phase 3), make the pipeline fast enough to be pleasant and to scale across all recipes. This phase is **empirical**: instrument → measure a baseline → attribute where the time goes → try improvements as controlled experiments → keep what measurably helps → re-measure. **No guessing; numbers decide.** Speed must **never** come from weakening tests, reducing real isolation unsafely, or skipping stages. --- ## 1. Mission Understand *where* recipe-test time goes (per phase, cold vs warm) and *reduce it measurably* on the real Phase-2 workload, with before/after numbers for every change and no loss of correctness. --- ## 2. Definition of Done (Phase 2b exit condition) Terminates only when every item holds **and the Adversary has independently re-verified each within 24h** (logged in `REVIEW.md`): - [ ] **T1 — Instrumentation.** The runner emits **per-phase timings** for every run (image pull, `abra app new`/deploy, service convergence, secret generation, each stage install/upgrade/ backup-restore, each functional test, dependency/SSO setup, teardown) into the run's `results.json` (the same artifact Phase 3 consumes). Timings are visible per run. - [ ] **T2 — Baseline.** A measured baseline across a **representative recipe set** — at least: a light/stateless recipe (custom-html), a single-DB recipe (n8n), a heavy JVM/SSO recipe (keycloak), and an SSO-*dependent* recipe (lasuite-docs). Each measured **cold** (empty image cache) and **warm** (cached), multiple runs to capture variance. Recorded in `docs/perf/baseline.md`. - [ ] **T3 — Attribution.** A written attribution (`docs/perf/attribution.md`) showing the **Pareto breakdown** — which phases dominate, cold vs warm — e.g. "keycloak warm: 8m converge + 4m backup + …". The biggest levers are identified from data, not intuition. - [ ] **T4 — Experiments.** Each improvement idea (§4) tried as a **controlled experiment** (change one variable, hold the rest), with **before/after numbers** in `docs/perf/experiments.md`: what was changed, the measured delta, and keep/discard. Failed experiments are recorded as dead-ends (don't re-try). - [ ] **T5 — Adopted improvements + measured gain.** The beneficial changes are adopted (Nix-declared / harness / Drone config) and the **overall run time is measurably reduced** vs the T2 baseline. Set a concrete target in `DECISIONS.md` from the attribution (e.g. "median warm heavy-recipe run ≤ X min; light recipe ≤ Y min") and hit it, with the single node still safe (RAM/disk/concurrency). - [ ] **T6 — No regression.** Adversary confirms, from a cold start, that after the speed-ups **every Phase-2 test still passes and isolation/teardown still hold** (no shared-state contamination, no weakened/skipped assertions, no leaked apps). A speed-up that compromises correctness is reverted. - [ ] **T7 — Recommendations.** `docs/perf/README.md` summarizes findings, the recommended config (e.g. `MAX_TESTS`, cache settings, warm-infra choices) and per-recipe sizing/timeouts, and what *didn't* help. A new engineer can understand the perf model and re-run the measurements. When T1–T7 hold and are Adversary-verified, write `## DONE` to Phase-2b `STATUS.md`. --- ## 3. Method (the empirical loop) 1. **Instrument first (T1).** You cannot optimize what you don't measure. Add lightweight timing spans around every phase in `run_recipe_ci.py`/harness; emit to `results.json`. Keep overhead negligible. 2. **Baseline (T2).** Run the representative set repeatedly, cold and warm; record medians + spread. Distinguish **cold-cache** (first pull/eval) from **warm-cache** (steady state) — they have very different profiles and call for different fixes. 3. **Attribute (T3).** Rank phases by time. Optimize the **biggest contributors first**; ignore noise. 4. **Experiment (T4).** One change at a time, re-measure on the same recipes, compare to baseline. Keep if the delta is real and correctness holds; otherwise revert and log the dead-end. **Cap retries** (don't thrash on a change that isn't helping). 5. **Adopt + re-measure (T5).** Land the winners declaratively (Nix/harness/Drone), then re-baseline to confirm the cumulative gain. 6. **Guard correctness throughout (T6).** Every speed run is also a correctness run; the Adversary re-verifies independently. --- ## 4. Ideas to try (hypotheses — validate empirically, don't assume) Grouped by where time likely goes. Each is a hypothesis to **measure**, not a guaranteed win. **A. Image pulls (often the cold-cache dominator).** - Stand up a **local Docker registry pull-through cache / mirror** on cc-ci (or `registry-mirrors`) so recipe images aren't re-downloaded across runs. - **Pre-pull/warm** the image set for enrolled recipes (a warm-images step / on enroll), so the first real run isn't paying the cold pull. - Ensure pinned tags (no `:latest` re-pulls); rely on the node's layer cache (don't prune images the active recipes need — reconcile with Phase-1's `autoPrune`). **B. Service convergence / readiness (often the warm-cache dominator).** - Replace any fixed `sleep`s with **tight readiness polling** against real health endpoints (short interval, sensible cap) — over-waiting is pure waste. - Per-recipe **readiness probes** tuned to the app (e.g. keycloak `/realms/master`, DB `pg_isready`) instead of a generic HTTP wait. - Parallelize independent readiness checks within a run. **C. Redundant deploy cycles.** - A run currently deploys multiple times (install; upgrade = old→new; backup = deploy→wipe→restore→ redeploy). **Share one deployment** where safe: run install + functional + backup-restore against a single deploy; only the upgrade stage needs a separate prior-version deploy. Measure the saving vs any isolation cost. - Scope backups to the **minimal data volumes** (restic over only what matters) to cut backup/restore time. **D. Warm / shared dependency infra (biggest lever for SSO recipes — but mind isolation).** - Deploying an SSO provider (keycloak/authentik) *per run* is expensive. Consider a **long-lived warm provider** that recipe tests register a per-run realm/client against, instead of a fresh deploy each run. **Tradeoff:** shared state risks cross-run interference — only adopt if per-run isolation (unique realm/client/users, cleaned up) is provably maintained; the Adversary must verify no contamination. If isolation can't be guaranteed, keep per-run deploys. - Keep traefik/the proxy warm (already persistent in Phase 1). **E. Runner / build caching.** - Persistent **nix store** + warm flake eval on the runner (don't re-evaluate/re-fetch per build). - Cache test-dependency installs (pip/uv wheels, Playwright browser binaries) in a persistent volume or Drone cache, so each build doesn't refetch. **F. Concurrency, sized per recipe.** - Tune `MAX_TESTS`/`DRONE_RUNNER_CAPACITY` empirically: **light recipes can run concurrently** while heavy ones serialize. Consider a per-recipe **weight/size** so the scheduler packs the node without overcommitting RAM/CPU (now 6GB / 2 vCPU). Parallelize independent functional tests within a run. **G. Resources.** - Right-size the VM: RAM (now 6GB), **vCPU** (currently 2 — more cores speed parallel pulls/builds/ JVM), disk I/O. Measure whether CPU or RAM is the bottleneck for heavy recipes before bumping. **H. abra/secret overhead.** - Profile `abra app secret generate` and `abra app new`; avoid regenerating/re-inserting secrets redundantly across stages (reuse the per-run secret store from Phase-1 §4.4-B). (Validate each on the baseline recipes; keep only measured winners. The list is a starting menu, not a mandate.) --- ## 5. Milestones (each ends with an Adversary gate) - **V0 — Instrument + baseline.** Per-phase timing in `results.json`; baseline for the representative set, cold & warm, in `docs/perf/baseline.md`. *Accept:* Adversary reproduces a baseline run and the timings match reality. - **V1 — Attribution.** `docs/perf/attribution.md` ranks the time sinks (cold vs warm) and names the top 2–3 levers. *Accept:* the attribution is supported by the recorded numbers. - **V2 — Quick wins.** Land the cheapest high-impact fixes (image cache/pre-pull, readiness-wait tuning, dedup deploys) with before/after numbers. *Accept:* measured improvement on the baseline, all tests still green. - **V3 — Structural wins.** Evaluate warm/shared infra, runner caching, concurrency sizing, vCPU — adopt the ones that pay off *and* preserve isolation. *Accept:* cumulative improvement vs T2; the Adversary confirms isolation/correctness intact (esp. for any shared-infra change). - **V4 — Lock in + document.** Re-baseline to confirm the gain; record adopted config + dead-ends + recommendations in `docs/perf/`. *Accept:* target from T5 met (or a documented, justified best effort); no regressions; flip Phase-2b `STATUS.md` to `## DONE`. --- ## 6. Guardrails (inherit Phase 1 §9 + Phase 2 §7.1) - **Speed never beats correctness.** No change may weaken/skip a test, reduce a real assertion, or break isolation/teardown to look faster. Every perf experiment is re-run as a correctness run. - **Shared/warm infra is opt-in and isolation-proven.** Only adopt shared dependencies if per-run isolation (unique namespaces, cleanup) is verified by the Adversary; otherwise keep per-run deploys. - **Stay within the node budget.** Concurrency/resource changes must respect RAM/disk/CPU limits (Phase-1 `MAX_TESTS`); don't trade overload for apparent speed. - **Change one variable at a time; cap retries.** Attribute gains to specific changes; record dead-ends in `DECISIONS.md` and stop thrashing. - **Measure honestly.** Report medians + variance, cold vs warm; don't cherry-pick a lucky fast run. --- ## 7. Open decisions (log in DECISIONS.md) - The concrete perf **target** (per-recipe time budgets), derived from the attribution. - Local registry **pull-through cache** vs explicit pre-pull (or both). - Whether to use **warm shared SSO providers** (speed) or keep **per-run providers** (isolation) — decided by the measured saving vs the verified isolation cost. - `MAX_TESTS` and per-recipe **weights**; whether to raise vCPU. - Whether stage **deploy-sharing** (install+functional+backup on one deploy) is safe per recipe.