cc-ci-orchestrator/cc-ci-plan/plan-phase2b-test-performance.md

# 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.