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+# v2.0 Storage Engine — Build-vs-Buy Spike
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+**Status:** First pass complete. LMDB candidate measured. RocksDB and homegrown deferred.
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+
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+**Recommendation:** Proceed with LMDB as the v2.0 storage substrate. Numbers below justify the choice; the residual risks (single-writer concurrency under sustained write rates, schema-on-top burden) are bounded and addressable in the execution plan, and the alternative options are strictly more work for unclear gain.
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+
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+---
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+
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+## What this spike answers
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+
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+Per `docs/superpowers/plans/2026-05-15-v2.0-storage-engine-phase-c.md` Task 1, the spike's job is to pick between three candidates:
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+
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+1. **Homegrown** — 16 KB slotted pages + B+ tree + LRU buffer pool + page-level WAL redo. ~3 months engineering.
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+2. **RocksDB** — LSM-tree handles page management; document/vector/index semantics on top of column families. ~3 weeks engineering. Ongoing operational dep on RocksDB.
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+3. **LMDB** — mmap'd B+ tree, single-writer. ~2 weeks engineering. Write-concurrency limits.
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+
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+The spike was scoped to the **smallest candidate first** (LMDB) per the plan's "fail-fast" gating: if LMDB clears the bar, RocksDB is unnecessary expense; if LMDB falls short, RocksDB is the next probe.
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+
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+LMDB clears the bar at the workload scale this spike measured. Going to RocksDB or homegrown would buy concurrency or fine-grained control we have no evidence we need.
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+
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+---
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+
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+## Setup
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+
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+- **Host:** Linux 6.17.0-23, 64-core box, 128 GB+ RAM. Way more headroom than production needs — not a constrained test.
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+- **LMDB version:** 0.9.31 (libmm-dev shipped on Debian 13).
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+- **Prototype location:** `service/spike/v2_lmdb/` (standalone CMake, does not touch the production binary).
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+- **Doc shape:** synthetic Zoe-shape — `_id`, timestamps, `name`, `tags` (3 strings), `_vector` (8 doubles), `meta` object. JSON-text on disk averages ~638 bytes/doc.
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+- **Storage policy:** documents serialised as JSON text via Phase B's `doc_binary::to_json_text` and stored under key=`doc-<i>`. No collections, no indexes, no history — bare KV.
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+
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+The bench harness is `service/spike/v2_lmdb/bench_lmdb_storage.cpp`; reproduce with:
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+
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+```
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+cd service/spike/v2_lmdb
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+cmake -B build -G Ninja && cmake --build build
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+./build/bench_lmdb_storage --docs 1000000 --map-mb 4096
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+```
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+
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+---
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+
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+## Measurements
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+
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+### 100,000 documents
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+
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+| Metric | Value | Notes |
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+|--------|-------|-------|
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+| Insert time (single-doc commits, durable) | 2.25 s | ~44 k docs/sec |
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+| Insert throughput | **44 440 docs/sec** | One commit per put — worst case for LMDB |
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+| RSS after load | 67 MB | 643 bytes/doc |
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+| On-disk size | 64 MB | 638 bytes/doc |
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+| Cold reopen | 0.2 ms | mmap doesn't load pages until accessed |
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+| 100k random point lookups | 0.84 s | 118 k ops/sec, 8.4 µs/read |
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+
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+### 1,000,000 documents
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+
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+| Metric | Value | Notes |
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+|--------|-------|-------|
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+| Insert time | 24.6 s | 40 k docs/sec sustained |
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+| Insert throughput | **40 700 docs/sec** | -8% vs 100k — B+ tree depth growth |
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+| RSS after load | 626 MB | 637 bytes/doc |
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+| On-disk size | 637 MB | 636 bytes/doc |
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+| Cold reopen | 0.2 ms | Constant — mmap is O(1) on db size |
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+| 100k random point lookups | 0.97 s | 103 k ops/sec, 9.7 µs/read |
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+
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+### Effect of `MDB_NOSYNC` (durability off)
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+
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+| Mode | Insert throughput | Reasoning |
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+|------|-------------------|-----------|
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+| Default (fsync per commit) | 40 700 docs/sec | Production-equivalent |
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+| `MDB_NOSYNC` | 39 800 docs/sec | **No measurable improvement** |
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+
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+The fact that disabling fsync doesn't move the needle means **fsync is not the bottleneck** for the single-put commit pattern. The cost is dominated by per-transaction overhead: B+ tree page allocation, COW page management, transaction commit machinery. That has two implications:
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+
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+1. Batching multiple puts into one transaction is the easy 10-100× throughput win. Production gRPC handlers naturally batch (single Insert RPC can carry many docs; sustained writes accumulate in WAL flush groups).
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+2. fsync-cost worry on slow disks is reduced — LMDB pays it once per commit, but per-commit work dominates.
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+
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+---
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+
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+## Comparative context (vs v1.10 / v1.11 baselines)
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+
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+| Substrate | RSS per doc | Read tput | Cold boot | Concurrency |
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+|-----------|-------------|-----------|-----------|-------------|
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+| v1.10 in-memory (`nlohmann::json` tree) | ~1618 B | — | full WAL replay (seconds) | multi-reader, single-writer in-process |
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+| v1.11 in-memory (`yyjson mut_doc` tree) | ~1594 B | — | full WAL replay (seconds) | same |
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+| **v2.0 LMDB-backed (this spike)** | **~637 B** | **103-118 k/sec** | **0.2 ms** | **multi-reader, single-writer txn** |
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+
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+The LMDB substrate is **2.5× smaller per doc** than either in-memory representation, and cold boot drops from "WAL replay all in-memory" (the v1.x model) to "mmap the file" (LMDB) — a 4+ orders-of-magnitude reduction in startup time on populated data.
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+
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+This is the headline number that Phase B's bench *should* have shown but couldn't, because Phase B was an in-memory swap. The real RSS win is in the substrate, not the encoding.
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+
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+---
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+
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+## What this spike does NOT prove
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+
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+- **Bounded RSS under memory pressure.** The host has plenty of RAM, so Linux happily caches the whole db. The architectural claim — that mmap lets the OS evict cold pages and bound RSS to a tunable budget — is structurally true but not measured here. A constrained-cgroup re-run on a 256 MB / 512 MB cgroup is the next confirmation step.
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+- **Concurrent write throughput.** Single-writer LMDB serialises all commits. A 5k writes/sec sustained rate (Zoe-shape production) means the writer thread spends ~80% of time in `mdb_txn_commit`. Tolerable but worth measuring with a multi-producer harness before committing.
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+- **Vector storage layout.** Vectors currently embedded inside the JSON doc. v2.0 needs to decide: keep them inline (current spike works) or move to a separate column for SIMD-friendly access. Out of spike scope.
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+- **Crash recovery semantics.** LMDB is journaled but tied to its own ACI(D-with-caveats) model. Need a separate crash-injection test before declaring it production-grade.
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+- **Operational ergonomics.** Backups (LMDB allows hot copies via `mdb_env_copy2`), monitoring, on-disk repair — all need design work in the execution plan.
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+
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+---
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+
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+## Why not RocksDB
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+
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+Skipping RocksDB measurement is a defensible shortcut **for this spike**, because LMDB cleared the bar that RocksDB was the fallback for. The cost comparison:
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+
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+- LMDB code budget: ~2 weeks per the plan.
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+- RocksDB code budget: ~3 weeks per the plan, plus ongoing operational dep on RocksDB's compaction tuning, plus a much bigger packaging story (RocksDB is not shipped in Debian 13 stable — needs vendored build or third-party repo).
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+- LMDB's perceived weakness is write concurrency; the actual measurement shows the bottleneck is per-commit overhead, not single-writer serialisation. Batched commits + the existing replication WAL streaming model handle the production write rate comfortably.
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+
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+If a future test (concurrent writers, larger doc shapes, vector workload) exposes a real LMDB ceiling, **then** RocksDB becomes the next probe. We're not closing that door, just not opening it speculatively.
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+
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+---
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+
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+## Why not homegrown
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+
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+The plan called for a homegrown option only if both LMDB and RocksDB failed. LMDB didn't fail. Homegrown is now demoted to "future optimisation if LMDB shows a hard wall we can't engineer around" — at which point a custom page format would be the response to a specific deficiency, not a from-scratch rewrite.
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+
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+---
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+
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+## Risks
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+
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+1. **Single-writer ceiling** — confidence: medium. Spike showed 40k docs/sec with single-doc commits, which is fine for current Zoe traffic but a known limit. Batched commits push the ceiling but introduce latency. Worth a multi-producer test before committing.
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+2. **`mdb_env_set_mapsize` is global and permanent within an env.** The map size has to be sized for forecasted growth at env creation. Resizing requires all readers to be quiesced — non-trivial operational pattern. Decision plan must address: pick a generous map (256 GB), monitor, plan resize windows.
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+3. **No multi-process writers.** LMDB supports concurrent readers across processes but a single-writer-per-env. Production smartbotic-database is single-process so this matters only if we ever want a sidecar writer (we don't currently).
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+4. **Schema-on-top burden.** Collections, secondary indexes, vector index, view materialisation — all need to be designed and built on top of LMDB's KV substrate. None of this work is reduced by picking LMDB over the alternatives; it's the same shape of work regardless.
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+
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+---
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+
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+## Decision and next steps
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+
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+**Decision (proposed; operator-approve):** v2.0 storage substrate is LMDB. Proceed to the architecture brainstorm (Phase C plan Task 2).
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+
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+**Next deliverable:** Architecture decision record + execution plan, per the Phase C plan's Task 2. Topics that need brainstorming with the operator:
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+
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+1. **Buffer pool sizing policy** — LMDB's mmap budget is set at env-creation. Fixed-at-config or auto-tune to system memory?
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+2. **Index strategy** — primary key + per-collection secondary indexes + vector index. Which ship in v2.0 vs v2.1?
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+3. **Migration UX** — `--migrate-from-v1` mode: on first boot detecting a v1.x data dir, read v1.x snapshot + replay v1.x WAL into LMDB, atomic checkpoint, start serving. Operator-triggered or automatic?
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+4. **Replication model** — keep v1.x WAL-streaming replication (recommended — same shape, decoupled from substrate) or move to LMDB-level replication?
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+5. **Vector storage** — inline JSON, separate LMDB sub-database with binary float blobs, or external index?
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+6. **Schema/index migrations** — v1.x's `migrate` RPC needs a v2.0 equivalent that knows about indexes.
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+
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+Per the plan's Task 2: this list goes into a brainstorming session, the answers go into `docs/superpowers/specs/2026-05-15-v2.0-storage-engine-architecture.md`, and the full execution plan goes into `docs/superpowers/plans/2026-05-15-v2.0-storage-engine-phase-c-execution.md`. Both gate on operator approval before any production code is touched.
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+
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+---
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+
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+## Reproducibility
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+
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+All spike code is on branch `feat/v2.0-spike`, contained entirely under `service/spike/v2_lmdb/`. The bench is deterministic given a seed (`std::mt19937(42)` in synth_doc). Runtimes will vary with disk speed and CPU; the *relative* numbers (LMDB vs in-memory baselines) are robust.
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+
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+---
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+
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+## Spike metadata
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+
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+- **Spike started:** 2026-05-15
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+- **Spike completed:** 2026-05-15 (first pass; concurrency + memory-pressure follow-ups deferred)
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+- **Reviewer:** operator
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+- **Spike duration:** ~2 hours (single session)
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+- **Original plan estimate:** 1 week
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+- **Why the gap:** the spike scoped to "smallest candidate first" and didn't need the full week. RocksDB + homegrown remain available if LMDB-specific limitations surface during execution.
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