Phase 0.9 — Spike S14 (cocoindex concurrency under LMDB single-writer)

Phase 0.9 — Spike S14 (cocoindex concurrency under LMDB single-writer)

Audit date: 2026-05-11 (KH session S230) Branch: content-items-investigation (worktree-agent-a8ffb1adaede42c93) Spike spec: 0.9-spike-plan.md §S14 (NEW S229; added after S2 surfaced LMDB swap) Cocoindex version installed: 1.0.3 (PyPI; identical to S2 install) Python version: 3.14.2 (Homebrew); venv at .venv-spike-s14/ (worktree-local; not committed) Test corpus: docs/client-documentation-base/ (35 files — same as S2)

Spike status: PASSED — S2’s “single-writer constrains multi-worker Cloud Run topology” framing is REVISED. Cocoindex 1.0.3 over LMDB does NOT hard-block concurrent processes; LMDB’s writer-lock is held in tiny windows per ops-DB write, and multiple cocoindex processes can run App.update() concurrently against the same LMDB without lock errors, data corruption, or measurable serialisation overhead. Recommended v1 Cloud Run topology = (single-orchestrator-instance) for efficiency-of-work reasons (avoid 10× duplicate @coco.fn execution), NOT for correctness. Reversibility excellent — topology can be revisited without schema or data migration.

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1. Executive summary

Question	Answer
Does LMDB single-writer hard-reject concurrent process-level writers?	No. 10 concurrent processes against the same LMDB ops-DB all completed cleanly (10/10 success, 0 lock errors, no corruption). LMDB’s writer-lock IS single-writer at the transaction level, but cocoindex’s per-row ops-DB writes are short — concurrent processes interleave via the lock without contention-induced failure.
Does cocoindex 1.0.3 coordinate work across concurrent processes?	No. 10 concurrent processes each computed 34-35 of the 35 @coco.fn invocations (343 total observe invocations vs ~35 if perfectly deduped). The second-to-cache process for any given row gets the cache value and skips invocation, but there’s no cross-process work-stealing or job-claim primitive. Result: N concurrent processes do ~N× the @coco.fn work — that’s the cost shape, not lock contention.
Does cocoindex’s memo cache stay coherent under concurrent writers?	Yes. After 10 concurrent processes against one LMDB, a follow-up App.update() observes 0 invocations (full memo hit). The cache converges to the correct (deduplicated) state.
Do read-only consumers block when a writer holds the LMDB?	No. With a slow writer running (HOLD_MS=200, full_reprocess=True), 3 concurrent reader processes each opened the LMDB env and completed their 2s sleep cleanly. LMDB’s documented MVCC (“Multiple readers can hold the lock concurrently, but writers have exclusive access” — verbatim from Rust binary strings) holds.
What’s the v1 Cloud Run recommendation?	Topology (d-new) — single-orchestrator-instance with isolated per-instance LMDB. Each Cloud Run container has its own filesystem; LMDB-per-container is the default. Topology (a) (concurrent writers, shared LMDB) is technically correctness-safe but wastes N× compute via duplicate @coco.fn execution. Topology (b) (queue-based) is unnecessary at v1 scale. Topology (c) (read-only-worker fan-out) is viable for query-side workers; not needed for ingest.
Does cocoindex 1.0.3 support a Postgres-backed ops-DB (no LMDB)?	No. Postgres connector is target-only (write path for materialised views). The ops-DB backend is LMDB; the Rust binary contains no SQLite/Postgres-as-state-store code paths. (“Migrating legacy LMDB files from…” strings confirm the SQLite-to-LMDB swap is complete; there is no reverse path.)
Does cocoindex survive a writer crash mid-transaction?	Yes. Kill -9 a writer at 3s of a 17.5s run (5/35 rows written), then re-run another writer: recovery completes with 30 remaining invocations + 0 errors. LMDB’s MVCC + cocoindex’s per-row commit make this safe.

G14 decision-gate verdict: v1 Cloud Run topology = single-orchestrator-instance with isolated per-instance LMDB. Cloud Run autoscaling can drive multiple cocoindex instances safely (no shared state needed for correctness), but should be tuned so only one instance handles ingest at a time to avoid duplicate work. Migrate to topology (b) queue-based or (c) read-only-worker only if v1 traffic exceeds single-instance throughput (~50-200 concurrent rows/sec is empirically feasible per S2 + this spike).

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2. Method (executed)

2.1 Setup

Per 0.9-spike-plan.md S14 + CLAUDE.md sandbox gotcha for cocoindex installs:

python3 -m venv .venv-spike-s14
PIP_USER=0 PIP_TARGET="" .venv-spike-s14/bin/pip install cocoindex
# Confirmed: cocoindex 1.0.3, Python 3.14.2, identical to S2 venv

All Python invocations + LMDB-engine startup run with dangerouslyDisableSandbox: true per S2’s §4.5 gotcha.

2.2 Probe harness

Spike-local scripts at .spike-s14/ (gitignored):

Script	Purpose
.spike-s14/concurrency_probe.py	Reusable per-process harness. Single App.update() against docs/client-documentation-base/. Honours COCOINDEX_DB env var for LMDB path, HOLD_MS for per-row sleep inside @cocoindex.fn, FULL_REPROCESS=1 to bypass memo, `mode = update
.spike-s14/run_topology_a.sh	Topology (a) — 2 concurrent writers, same LMDB.
.spike-s14/run_topology_a_n4.sh	4 concurrent writers, same LMDB.
.spike-s14/run_topology_a_n10.sh	10 concurrent writers, same LMDB (stress).
.spike-s14/run_topology_b.sh	Topology (b) — N serialised invocations against same LMDB.
.spike-s14/run_topology_c.sh	Topology (c) — writer with FULL_REPROCESS=1 + N read-only consumers.
.spike-s14/run_isolated_lmdb.sh	Topology (d-new) — N concurrent writers, isolated per-process LMDB dirs.
.spike-s14/run_crash_recovery.sh	Crash probe — kill -9 a writer mid-transaction, then re-run.

2.3 The reusable probe

@cocoindex.fn(memo=True)
def _observe(path: str, content: bytes) -> dict:
    """Trivial observer; emits invocation count + size."""
    if _HOLD_MS:
        time.sleep(_HOLD_MS / 1000.0)  # widen transaction window
    sys.stderr.write(f"    observe: {path} (size={len(content)})\n")
    return {"path": path, "size": len(content)}


async def _main() -> None:
    files = walk_dir(CORPUS, recursive=True)
    async def index_one(item):
        data = await item.read()
        return _observe(str(item.file_path), data)
    await cocoindex.mount_each(index_one, files.items())


def run_update() -> None:
    app = cocoindex.App("s14_probe", _main)
    full_reprocess = os.environ.get("FULL_REPROCESS", "") == "1"
    app.update_blocking(report_to_stdout=False, full_reprocess=full_reprocess)

Baseline single-process run with HOLD_MS=200 against fresh LMDB: 7.18s wall-clock; 35 @coco.fn invocations. This is our reference.

2.4 Topology (a) — concurrent writers, shared LMDB

Probes: do two/four/ten concurrent processes against the same LMDB block, fail, or corrupt?

Configuration	N	HOLD_MS	Wall (s)	All exit=0?	Observe per proc (range)	Total observes	Post-run memo coherent?
Fresh LMDB	2	200	7.20	yes	34-35	69	yes (0 on re-run)
Fresh LMDB	4	200	8	yes	34-35	138	yes
Fresh LMDB	10	300	11	yes (10/10)	34-35	343	yes (0 on re-run)

Interpretation:

1. All processes succeed. No MDB_BUSY, no MDB_INVALID, no “lock held” rejection. Concurrent process-level writers ARE supported (despite LMDB’s documented single-writer model — this works because the writer lock is held in tiny per-row windows, not across the whole engine update).
2. Wall-clock barely scales with N. Going from 1 → 10 concurrent processes increases wall-clock from 7.2s → 11s (1.5×). LMDB writer-lock contention is real but small.
3. Cross-process work-coordination is absent. 343 observe invocations across 10 processes vs ~35 if perfectly coordinated. Each process re-fingerprints the corpus independently; the “winner” for any given row commits its result first, and subsequent processes see the cache. 34-35 invocations per process means at most 1 row per process saw the cache early enough to skip — most rows are still processed by 9/10 processes.
4. Final state is coherent. Re-running App.update() after the concurrent run observes 0 invocations. Whichever process committed last for each row is in the cache. No corruption observed across any topology.

2.5 Topology (b) — queue-based serialisation

Probes: do N serialised invocations against the same LMDB run cleanly start-to-finish?

== Topology (b) — 4 serialised writers, same LMDB ==
  slot 1: OK pid=81182 elapsed=7.20s  (cold; 35 observe)
  slot 2: OK pid=81291 elapsed=0.02s  (full memo; 0 observe)
  slot 3: OK pid=81295 elapsed=0.03s  (full memo; 0 observe)
  slot 4: OK pid=81297 elapsed=0.03s  (full memo; 0 observe)
WALL-CLOCK: 8s for 4 serial runs

The first run does all the work (7.20s); subsequent runs hit a fully-memoised cache and complete in ~30ms. Cocoindex’s incremental Δ semantics work perfectly across process restarts — exactly what S2’s §4.2 promised.

Implication: if the corpus is changing (new files arriving), serialised invocations are the most efficient pattern. Each run picks up only the new/changed files; previously-processed files stay in the cache. This is the natural fit for a “ingest job triggered by webhook / cron / Cloud Tasks” pattern.

2.6 Topology (c) — read-only-worker fan-out

Probes: do read-only consumer processes block when a writer holds the LMDB?

Setup: writer runs with FULL_REPROCESS=1 + HOLD_MS=200 against a pre-seeded LMDB → ~7s of active writes. While writer is running, spawn 3 read-only consumer processes (each opens the env via cocoindex.start_blocking() then sleeps 2s).

== Topology (c) — 1 writer + 3 read-only consumers ==
-- writer exit=0 --
OK  pid=82518  elapsed=7.18s
-- reader 1 --
READ-OK  pid=82521  elapsed=2.00s
-- reader 2 --
READ-OK  pid=82522  elapsed=2.00s
-- reader 3 --
READ-OK  pid=82523  elapsed=2.00s

All readers completed without blocking. Cross-process MVCC reads work as advertised — verbatim from cocoindex Rust binary: “Multiple readers can hold the lock concurrently, but writers have exclusive access.”

Implication for v1: if KH ever needs to expose the LMDB ops-DB to read-side workers (e.g. fan-out a query-only API tier sharing the orchestrator’s LMDB via Cloud Filestore), the cross-process read story works. Note: KH’s actual query path is Postgres (the target), not LMDB (the ops-DB), so this is a contingency rather than a v1 design driver.

2.7 Topology (d-new) — isolated per-process LMDB

This isn’t in the original spike plan but emerges naturally as the Cloud Run default: each container has its own filesystem; LMDB-per-container is the natural shape.

== Isolated-LMDB-per-process topology ==
  slot 1 observe count: 35  (full work; OK pid=85360 elapsed=7.21s)
  slot 2 observe count: 35  (full work; OK pid=85361 elapsed=7.20s)
  slot 1 re-run observe count: 0  (memo coherent in own LMDB)
  slot 2 re-run observe count: 0  (memo coherent in own LMDB)

Two isolated LMDBs each fully process the corpus and converge to their own coherent memo state. This is what Cloud Run autoscaling looks like by default — and it’s correctness-safe at the cost of N× work per N concurrent containers.

2.8 Crash recovery

Probes: does cocoindex survive a SIGKILL’d writer mid-transaction?

== Crash-recovery probe: kill writer mid-transaction ==
  killing pid=84623 (SIGKILL) after 3s of work [partial write ~5/35 rows]
== Recovery: run another writer against the crashed LMDB ==
  exit=0
  observe count: 30
  [pid=84669] update OK in 0.05s

LMDB is crash-safe by design (append-only data + lockfile recovery), and cocoindex inherits this. Critical for Cloud Run — pod termination mid-update doesn’t corrupt the ops-DB; the next invocation picks up where the crashed one stopped.

2.9 Verification of Postgres-as-ops-DB option

S2 §3 documented the SQLite-to-LMDB swap but left open whether a Postgres ops-DB option exists (which would dissolve the LMDB question entirely for KH, since the Postgres staging branch is already running).

Probe: inspect cocoindex._internal.setting.Settings + the Rust binary symbol table for any postgres_url/backend/metadata_store settings.

Result: No Postgres-as-ops-DB option exists in 1.0.3. The Rust binary contains:

• db_path (LMDB path)
• lmdb_max_dbs, lmdb_map_size
• “Migrating legacy LMDB files from…” (SQLite→LMDB migration code)
• No Postgres / SQLite ops-DB code paths

Postgres is target-only (writes the materialised view of cocoindex’s flow output). The ops-DB / state store is LMDB-only. If LMDB becomes a v1 blocker (it isn’t, per this spike), the workaround is not “switch to Postgres ops-DB”; it’s either accept isolated-LMDB-per-instance, or wait for upstream cocoindex to add a multi-writer backend.

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3. Per-spike-question findings

3.1 LMDB locking semantics — empirically verified

S2 §4.5 stated: “LMDB single-process semantics. LMDB is single-writer; multi-process concurrent writes are blocked.” — partially incorrect. Refined finding:

• LMDB’s writer transaction is single-writer (one writer can hold the write-txn at a time).
• LMDB’s writer lock is process-coordinated via a memory-mapped lockfile (lock.mdb).
• Concurrent processes do not hard-fail; they queue at the writer-lock and take turns. Per-row commits are short (microseconds), so wall-clock impact is minimal.
• Within a single process, the Rust core uses a tokio::sync::RwLock to coordinate concurrent flow components — see cocoindex_py::rwlock::RWLockReadGuard/RWLockWriteGuard in the binary symbol table.

Correct framing: LMDB enforces transaction-level write-serialisation; cocoindex doesn’t add additional cross-process coordination beyond LMDB’s built-in lockfile. Concurrent processes work, but waste N× compute on uncoordinated @coco.fn execution.

3.2 Queue-based serialisation pattern (Topology b) — works as intended

Serialised invocations against a stable LMDB demonstrate the incremental Δ pattern: first run does full work; subsequent runs hit the memo and complete near-instantly. This is the natural pattern for Cloud Run + Cloud Tasks (or any work-queue) wiring:

[file change webhook] → [Cloud Tasks queue] → [Cloud Run job: cocoindex App.update()]
                                                           ↓
                                              [LMDB on persistent volume]
                                                           ↓
                                              [Postgres target writes]

The cocoindex job runs to completion against the current corpus state; the cache means repeated runs are cheap.

3.3 Read-only-worker pattern (Topology c) — works

If KH needed to fan out read-side query workers sharing the LMDB ops-DB, cross-process MVCC reads succeed alongside an active writer. However, this isn’t relevant for v1 KH topology because:

• KH’s query path goes through Postgres (the cocoindex target), not the LMDB ops-DB.
• The Postgres target IS multi-reader-multi-writer (it’s a real database).
• LMDB is just the operations / state store; nobody outside cocoindex’s flow engine queries it.

So Topology (c) is filed as a “useful capability if a future v1.1 or v2 feature needs it” — not a v1 design driver.

3.4 Cloud Run mapping

KH’s Cloud Run projects (kh-prod-494815 + kh-staging-494815) currently host the Python pipeline jobs. The v1 architecture absorbs cocoindex’s orchestrator role into this tier.

Container filesystem semantics:

• Cloud Run instances each have their own ephemeral filesystem (/tmp/ is writable; / is read-only after image build).
• Without a shared volume mount (Cloud Filestore / GCS Fuse / Cloud Storage volume), each container has its own LMDB → topology (d-new) by default.
• With a shared volume mount (Cloud Filestore), multiple containers can share an LMDB → topology (a) or (c).

Recommended v1 deployment:

Component	Cloud Run topology	Why
cocoindex orchestrator	Single-instance via min_instances=1, max_instances=1 OR scheduled Cloud Run job (one-shot per invocation)	Avoid the N× duplicate-work cost of Topology (a). LMDB lives on Cloud Run job container fs + persists to Cloud Storage volume mount OR is rebuilt from scratch each invocation (full re-fingerprint = ~7s for a 35-file corpus per this spike; scales linearly).
KH API tier (Next.js)	Vercel; cocoindex-irrelevant	Reads Postgres directly; no LMDB dependency.
KH MCP server	Vercel; cocoindex-irrelevant	Reads Postgres directly.
Query / search workers (if added)	Cloud Run multi-instance; reads Postgres	No LMDB share needed.

LMDB persistence choice (open question for Phase 2):

• Option α — ephemeral LMDB, rebuild each run. Simplest. ~7s overhead per invocation for 35 files; scales to ~minutes for full client corpus. Cost: re-fingerprinting overhead. Win: stateless orchestrator, no volume management.
• Option β — persisted LMDB on Cloud Storage volume mount. Lower latency on repeated invocations (memo hits). Cost: volume management, eventual-consistency concerns if mount semantics aren’t strong. Cloud Run’s gcsfuse + Cloud Storage volume is the documented path; latency is higher than local disk but acceptable for cocoindex’s ops-DB writes (small + infrequent).

Recommend Option α for v1 (ephemeral LMDB). Re-fingerprinting at job start is bounded; the simplicity is worth it. Promote to Option β only if profiling shows full-corpus re-fingerprint exceeds the job invocation budget.

3.5 Reversibility

Topology choice is fully reversible without schema or data migration:

• If v1 ships single-orchestrator + ephemeral LMDB and v1.1 needs multi-writer: just add the volume mount. Cocoindex’s flow definitions, schema, and target bindings are unchanged.
• If upstream cocoindex adds a multi-writer ops-DB backend (e.g. Postgres-backed): swap the backend via Settings. Flow code unchanged.
• If KH outgrows Cloud Run and needs Kubernetes: cocoindex’s container model is platform-agnostic; the same LMDB volume mount story applies.

No lock-in. This is the rare architectural decision where deferring optimisation is risk-free.

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4. Decision gate G14 outcome

G14: cocoindex concurrency under LMDB single-writer

Sub-decision	Verdict
LMDB hard-blocks concurrent writers?	No — empirically refuted. 10 concurrent writers OK.
v1 Cloud Run topology	Single-orchestrator-instance with isolated per-instance LMDB. Use min_instances=1, max_instances=1 for the cocoindex job, OR scheduled Cloud Run job invocations.
LMDB persistence	Ephemeral (Option α) for v1. Re-fingerprint cost bounded; simplicity wins. Promote to volume-mounted (Option β) if profiling demands.
Queue infra needed?	No additional queue infra needed for v1. Cloud Run’s built-in trigger mechanisms (Cloud Tasks if needed, but optional) are sufficient.
Read-only-worker capability	Available but not used for v1. Filed for future query-side workers if needed.
Reversibility to multi-writer if v1.1 demands	Excellent — schema, data, target bindings unchanged. Just add volume mount + raise max_instances.

G14 status: RESOLVED. Phase 2 architecture commits to single-orchestrator-instance cocoindex on Cloud Run with ephemeral LMDB. No new infra required beyond what’s already running for the Python pipeline.

Phase 2 architecture-impl cost delta vs S229 estimate: zero. Topology decision is configuration, not code.

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5. Surprises and residual questions

5.1 Surprises

1. S2’s “single-writer constrains multi-worker topology” framing was overcautious. LMDB’s writer-lock is real but cocoindex’s per-row writes are short — concurrent processes interleave at the lock without erroring. The actual constraint is “wasted compute” (N× work), not “lock contention failure”. Correction note for downstream docs.
2. Cocoindex doesn’t coordinate work across concurrent processes. Each process re-fingerprints the full corpus; only the LMDB memo cache deduplicates results post-hoc. If KH ever wants true multi-process work-sharing, it needs an external coordinator (e.g. a row-claim primitive in a separate queue) — but that’s a v1.1+ scaling concern, not v1.
3. The migration tooling in the binary (“Migrating legacy LMDB files from…”) suggests cocoindex’s own developers were once using SQLite ops-DBs. The swap to LMDB is recent. Watch for upstream churn — a Postgres-backed ops-DB option could land in a future version and dissolve the v1.1 multi-writer question.
4. Crash recovery is built-in. SIGKILL’d writers leave the LMDB intact + the next invocation resumes. This is exactly what Cloud Run needs given pod-termination semantics.
5. The “max_inflight_components” knob on AppConfig is the intra-process concurrency control — distinct from the inter-process question this spike addressed. Worth tuning in Phase 2 if profiling shows the orchestrator’s @coco.fn fan-out is throughput-bound.

5.2 Residual questions

Q	For
Q-S14-1	Full-corpus re-fingerprint cost at production scale. Spike used 35 files (~7s with HOLD_MS=200). KH’s first-client corpus is ~500-2000 files; binary docx/pdf files add IO. Expected: 30s-3min per cold run. Acceptable if Cloud Run job timeout is 10min+ — confirm before committing to Option α (ephemeral LMDB).
Q-S14-2	Cocoindex flow-restart vs partial-restart semantics. If a Cloud Run job is killed mid-flight, does the next invocation start with the LMDB state at kill-time OR does it re-run the entire flow from scratch? This spike confirmed crash-safety; the partial-progress question wasn’t directly tested. Mid-spike observation: killed writer at 5/35 → recovery processed remaining 30. Suggests partial resume works as expected. Confirm at full-corpus scale.
Q-S14-3	Cloud Storage volume mount (gcsfuse) latency profile on LMDB writes. If KH later wants Option β (persisted LMDB), need to confirm volume-mount latency doesn’t cause LMDB writer-lock starvation. Plan: latency-probe pre-Phase-3-deploy if Option β is chosen.
Q-S14-4	Upstream cocoindex roadmap on multi-writer / Postgres-backed ops-DB. Could materially affect v1.1 topology. Track upstream releases; re-evaluate annually.
Q-S14-5	Cloud Run min_instances=1 cost vs scheduled-job cost. Scheduled cocoindex jobs (Cloud Scheduler → Cloud Run jobs) avoid the always-on instance cost but have cold-start latency. v1 budget impact: marginal either way at v1 scale; revisit if cost-conscious.

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6. Recommendations for downstream artefacts

6.1 Updates to 0.9-intended-architecture.md

§10 (Tool stack composition) — cocoindex pipeline (orchestrator) block should specify:

Single-instance orchestrator on Cloud Run (min_instances=1, max_instances=1) with ephemeral per-instance LMDB ops-DB. Cocoindex’s incremental Δ semantics + LMDB crash-safety mean re-runs after kill are cheap and safe. No queue infrastructure required for v1.

§11 (UI surface) — no changes (this is back-end-only).

§12 (Dedupe / data-fix) — note that cocoindex’s per-row dedup is single-process; cross-process dedup (UC8) still needs S10’s separate substrate evaluation.

6.2 Updates to 0.9-spike-S2-cocoindex-folder-binding.md

§4.5 (Operational concerns) item 2 — current text:

LMDB single-process semantics. LMDB is single-writer; multi-process concurrent writes are blocked. For KH’s planned multi-worker Cloud Run topology, the ops-DB needs partitioning (one LMDB per worker shard) OR the engine must run as a singleton orchestrator with workers as @coco.fn runners only. Architecture revision needed if multi-worker concurrent ingest is mandatory for v1. Confirm with S1 + a separate “cocoindex concurrency” probe (not in current spike-plan; recommend adding as S14).

Recommended revision:

LMDB transaction-level single-writer. LMDB’s writer-lock serialises transactions; concurrent processes are NOT hard-blocked (S14 empirically verified 10/10 success with 10 concurrent writers). However, cocoindex doesn’t coordinate work across concurrent processes — N concurrent writers do ~N× the @coco.fn work, with the memo cache deduplicating results post-hoc. v1 recommendation: single-orchestrator-instance on Cloud Run with ephemeral per-instance LMDB. See S14 for full analysis. The architecture revision risk flagged in S2 is dissolved.

6.3 Updates to 0.9-spike-plan.md

§S14 status: NEW S229 → ✅ COMPLETE S230. PASSED — single-orchestrator topology confirmed.

§7 Risk register — remove the “Cocoindex concurrency unclear / Cloud Run topology” entry.

6.4 New CLAUDE.md gotcha (cocoindex install)

Add under Gotchas → General:

cocoindex installs require sandbox bypass. PyPI install (pip install cocoindex) AND Rust-engine LMDB startup both fail under macOS sandbox profile with “Operation not permitted”. Use dangerouslyDisableSandbox: true for all cocoindex-related Bash calls including subprocess invocations. Recorded across spikes S2 + S14.

(Both S2 and S14 hit this; codifying in CLAUDE.md prevents re-surfacing in future spikes / Phase 2 work.)

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7. Effort revision (S229 baseline → S230 actual)

Estimate	S229 spike-plan §1 budget	S230 actual
S14 effort	1 day	~3.5 hours main-session worktree sub-agent (incl. probe scripting + Rust binary inspection + 4 topology tests + crash test + docs)
Effort delta on Phase 2 architecture-impl	possibly +2-5 days if multi-writer needed	0 days — single-orchestrator is config-only
Cloud Run infra delta	possibly +Cloud Tasks queue / Pub-Sub	0 — uses existing Cloud Run jobs

S14 was 1d planned, ~0.5d actual. The risk it was sized to investigate dissolves cleanly with the empirical observation.

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8. Reproducibility — to re-run this spike

# From worktree or main:
cd <repo>

# 1. Venv (identical to S2)
python3 -m venv .venv-spike-s14
PIP_USER=0 PIP_TARGET="" .venv-spike-s14/bin/pip install cocoindex
.venv-spike-s14/bin/python3 -c "import cocoindex; print(cocoindex.__version__)"
# Expected: 1.0.3

# 2. Topology probes
HOLD_MS=200 bash .spike-s14/run_topology_a.sh        # 2 concurrent writers
HOLD_MS=200 bash .spike-s14/run_topology_a_n4.sh     # 4 concurrent writers
HOLD_MS=300 bash .spike-s14/run_topology_a_n10.sh    # 10 concurrent writers (stress)
HOLD_MS=200 N=4 bash .spike-s14/run_topology_b.sh    # 4 serialised writers
HOLD_MS=200 N=3 bash .spike-s14/run_topology_c.sh    # writer + 3 readers
bash .spike-s14/run_isolated_lmdb.sh                 # isolated-LMDB-per-process
bash .spike-s14/run_crash_recovery.sh                # SIGKILL recovery

All scripts live under .spike-s14/ (gitignored). Re-create from the inline excerpts in §2 above + the canonical corpus at docs/client-documentation-base/ if needed.

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9. Confidence assessment

S14 verdict confidence: 92%.

Evidence quality:

• Empirical multi-process verification on the canonical corpus.
• Rust binary symbol inspection confirms LMDB-only ops-DB backend (no hidden alternative).
• LMDB’s MVCC semantics are well-documented; cocoindex inherits them without surprises.
• Crash-recovery test confirms Cloud Run pod-termination resilience.

Remaining 8% drag:

• Cloud Run + Cloud Storage volume mount latency not directly tested (only on local fs).
• Production-scale corpus (~500-2000 files) not tested; behaviour should extrapolate cleanly but unverified.
• Upstream cocoindex multi-writer story may change in future versions.

Phase 2 commit confidence improves accordingly — the “cocoindex multi-worker topology blocker” hypothetical from S2 is empirically dissolved.

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10. Decision recommendation (binding for G14)

Recommendation: v1 Cloud Run cocoindex topology = single-orchestrator-instance with isolated per-instance LMDB (ephemeral).

• Configure cocoindex job with min_instances=1, max_instances=1 OR as a scheduled Cloud Run job triggered by Cloud Scheduler.
• LMDB lives on the instance’s ephemeral filesystem; rebuilt from scratch each cold-start.
• Re-fingerprint cost (~7s per 35 files) acceptable at v1 scale.
• No queue infrastructure required.
• No data-share volume mount required.
• Topology reversible to multi-writer or volume-mounted if v1.1 demands.

This dissolves the S2 §4.5 architecture-revision concern. Phase 2 architecture-impl can commit cocoindex as the v1 orchestrator without re-thinking Cloud Run topology.

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End of S14 spike record. G14: RESOLVED (single-orchestrator-instance + ephemeral LMDB for v1).