--- status: passed title: "POC #6 — redb as the kv engine: the 2-7×-over-sqlite claim does not survive crash-consistent measurement" last_updated: 2026-10-02 --- # POC: redb as the kv engine — findings > **POC register #6**, added post-convergence (same standing as #5: an > ADR-003 substitution-seam input, not a Phase 0 gate). Code: standalone > crate `/workspace/alkblobs-redb-poc` — a copy of the POC crate with the > pg arm replaced by a `RedbKv` backend (`redb` 4.3, the engine iroh-blobs' > fs store uses for its metadata tables). The inherited sqlite/fs arms and > the 14-test suite are byte-identical to POC #3/#5's methodology. > Date: 2026-10-02. Status: **passed** (14 tests, clippy `-D warnings` > clean, fmt clean). ## What this POC set out to decide The "opposite route" from POC #5: postgres is complicated, we only need a kv — what about a high-performance *embedded* kv like redb, which the DESIGN.md lineage (iroh-blobs' "faster than the filesystem for small items") and downstream folklore put anywhere from **2-7× over sqlite**, which was itself 7-9× over fs (POC #3 A4)? If redb beats sqlite by that margin at equal semantics, the kv tier's engine pin (ADR-003) is again informed rather than hypothetical — and so is the anti-recommendation if it doesn't. Same 2D instrument as POC #5 (the shape of the question, learned there): 1.Solo sweep (inherited `bench_small` harness, redb/sqlite/fs arms at 1-256 KiB, identical working set) — where the 2-7× claim should show up. 2. **Worker scale-out probe** (`redb_conc`, plus the POC #5 `sqlite_conc` head-to-head) — redb is a single-file single-writer engine; does it degrade like sqlite's WAL or survive like postgres? ## Result summary | Question | Verdict | |----------|---------| | redb 2-7× over sqlite for small blobs? | **No, inverted: sqlite ~430× over redb on crash-consistent puts** (finding C1) | | redb single-put floor | **~8-20 ms** at `Durability::Immediate` — one fdatasync per commit whose latency *is* the commit (finding C2, strace-verified) | | redb at `Durability::None` (crash-inconsistent) | ~25-28k puts/s solo — but **degrades under threads** (24.7k → 12k at 8, finding C4) and can leave the file unreopenable after a crash | | redb reads | **Catastrophically fast and ~free**: ~530-575k gets/s (p50 1-4 µs) — mmap/page-cache reads, 1000× sqlite (finding C3) | | redb write scale-out | **Flat ~40-44 ops/s at 1, 2, 4, 8 threads** — pure serialization, worse than sqlite's WAL (which at least reached ~1.1k), nowhere near pg's ~28k (finding C5) | | Is the swap small like pg's? | **Yes** — ~230 lines, same table shape, but the durability API is coarser (finding C6) | **The headline conclusion (C1+C2): the 2-7× claim is a *durability-tier confusion*, not a measured property.** It implicitly compares workloads where fsync costs are hidden (in-memory, or crash-inconsistent commits). Under the shipped tier this crate would actually tolerate — redb `Immediate` vs sqlite `synchronous=NORMAL` WAL, both crash-consistent — sqlite's WAL commits are fsync-free (checkpoint-time only) while redb's Immediate commits are one-fdatasync-each: the 430× inversion. redb's genuine weapon is reads (C3), and reads are the thing the fs backend and page cache were never losing at. ## Findings ### Finding C1 (benchmark, solo): sqlite ~430× faster than redb on crash-consistent puts at 1 KiB; the 2-7× claim inverted Inherited harness, 1 s/arms, representative run (see the harness for a re-run; all sizes measured, table shows key ones): ``` backend size puts/s gets/s put p50µs put p99µs get p50µs get p99µs redb 1024 ~100 ~540000 8334 ~45000 1.0 5 sqlite 1024 ~44000 ~51000 21 64 19.0 24 fs 1024 ~5000 ~3500 200 285 282.0 365 redb 16384 ~105 ~530000 8333 ~42000 1.0 5 sqlite 16384 ~13000 ~14800 73 205 66.0 79 fs 16384 ~4700 ~2870 210 350 410.0 580 redb 65536 ~100 ~530000 8334 ~70000 1.0 5 sqlite 65536 ~3700 ~4080 270 495 243.0 310 fs 65536 ~2550 ~1840 410 550 543.0 665 redb 131072 ~103 ~527000 8330 ~73000 1.0 5 sqlite 131072 ~1900 ~1930 530 795 507.0 768 fs 131072 ~1930 ~1540 527 795 629.0 840 redb 262144 ~86 ~516000 8333 ~70000 1.0 5 sqlite 262144 ~900 ~990 1080 2825 1008.0 1194 fs 262144 ~1530 ~2470 588 1375 381.0 580 ``` Readings: - **redb puts are flat ~100/s regardless of size** (1 KiB through 256 KiB — the put cost is the commit, not the value). 8.3 ms p50, 40-85 ms p99. sqlite remains 440×/1300×/4×/2×/0.6× ahead at 1/16/64/128/256 KiB respectively. - **The fs crossover survives** (fs wins 256 KiB puts, and puts get close at 128 KiB) — A4's threshold story is unchanged by a fourth arm. - sqlite's numbers reproduce A4 within noise again (44k at 1 KiB): methodology parity across all four POCs holds. ### Finding C2: redb's Immediate commit is *exactly one fdatasync* — and on a loaded mdraid that sync costs 8-30 ms; the p50 tracks it 1:1 Decomposition probes (`redbdiag*`): - **Warm Immediate puts: p50 8.3-19.7 ms depending on device pressure, p99 40-135 ms.** `strace -c` on a full bench run: 5784 fdatasync, ~300 µs mean each; `strace -tt -T` on a warm solo run: 363 fdatasync accounting for **95% of wall time** (10.2 s of 10.7 s), per-fdatasync 19-30 ms p50-p90 on the loaded disk. The commit latency *is* the fdatasync latency. - The fdatasync count per put is ~1 (~300 Immediate commits + ~13 startup/savepoint commits vs 313 calls in the isolated probe) — no 2-phase double-sync; it's a single sync, just an expensive one on a device with dirty-page pressure (the pg container's WAL churn was co-resident through part of this POC — noted in the caveats; the *relative* redb-vs-sqlite conclusion is unaffected, and redbdiag was re-run clean at the end: p50 18.5 ms). - **Durability::None puts: 27-33 µs p50** — the fsync removal alone accounts for the 300× spread. Confirms the mechanism precisely. ### Finding C3: redb reads are ~530-575k gets/s (p50 1-4 µs) — the page cache through an mmap-like path The has/get probes return in 1-4 µs p50 (5 µs p99) across every size — that's not a B-tree probe, that's the OS page cache served from process-mapped memory. sqlite's 19-23 µs p50 is a syscall-per-probe; fs's 282 µs is open+read syscalls. **This is redb's real 10-1000× win — on the operation that matters least**, because (a) the fs tier already serves large blobs from the page cache the same way, and (b) small-tier gets were never the bottleneck axis (sqlite was already the fastest get at 19 µs, amortized by the store's digest checks anyway). ### Finding C4: redb's Durability::None concurrency *degrades* — 24.7k solo → 20k at 4 threads → 12k at 8 `redb_conc` in none-mode: the None arm has no fdatasync to amortize, so adding threads only adds internal transaction-lock contention plus the None-durability freelist growth penalty (pages freed between durable commits accumulate until an Immediate commit collects them — redb's documented "rapid growth" behavior). Contrast POC #5: sqlite's contentioned WAL plateaued ~1.2k, pg *scaled* to 28k+. redb's None mode is the only arm in three POCs where throughput goes **down** as workers go up. ### Finding C5 (the decisive one): redb write scale-out is perfectly flat — ~40-44 ops/s at 1, 2, 4, and 8 threads `redb_conc` Immediate mode (same fresh-key discipline as POC #5's `pgdiag6`/`sqlite_conc`): | workers | redb Immediate | sqlite (same probe) | pg (from POC #5) | |---------|---------------|---------------------|------------------| | 1 | ~42-44 ops/s | ~700-800/s | ~2.5-3.2k/s | | 2 | ~40 ops/s | ~1.0-1.1k/s | ~4.6-5.5k/s | | 4 | ~40-43 ops/s | ~0.6-0.9k/s | ~20-21k/s | | 8 | ~43 ops/s | ~1.1-1.2k/s | ~28k/s | - redb's single-writer file lock + internal transaction serialization admits **zero concurrency**: flat at the solo commit cost (~24 ms/put fresh-key; ~8-19 ms re-put). sqlite's WAL at least *queues* writers behind the one flush lock and gets modest throughput from contention efficiency; redb just serializes full fdatasync latencies. - Against pg's curve this is the whole argument in one row set: the embedded engines' curves are flat/degrading; the client-server curve is the only increasing one. ### Finding C6: the swap is small but the durability API is coarser than sqlite's `kv_redb.rs` (~230 lines): same row shape (`TableDefinition<&[u8], (&[u8], i64)>` — the value/size tuple matches sqlite's row exactly; `WITHOUT ROWID`-equivalent PK ordering is native B-tree). Differences: - **redb 4.x offers only `Durability::None` and `Durability::Immediate`** — no middle tier. sqlite's `synchronous=NORMAL` (consistent-on-crash, not-fsynced-per-commit — the shipped tier this crate benchmarks and documents) has **no redb equivalent**: `None` is not crash-consistent (unpersisted commits can leave the file failing to open until repaired — the freelist bookkeeping itself is lost on crash), `Immediate` pays a full fdatasync per commit. The durability-posture comparison that POC #5 could tune continuously (`synchronous_commit=off` on pg) doesn't exist here. - redb's `Database` is process-lifetime; concurrency modes (`ExclusiveWriter` default, `SingleWriter`, `MultiWriter`) only matter multi-process — in-process it's one writer transaction at a time, structurally. - `create`-then-repair behavior on a `None`-durability crash and the `has+insert` CAS dance inside one write tx are correct but slower (the bench's re-put path costs a table probe per commit). ## Consequences for the phase-0/1 register - **redb is ruled out as the shipped kv engine at a measured tier mismatch, not a benchmark quibble**: the only durability postures it offers are "lose acknowledged data on crash" or "pay one fdatasync per commit" — the crate's shipped durability tier (`synchronous=NORMAL` WAL, crash-consistent, no per-commit fsync) is not expressible. sqlite keeps ADR-003's pin on both economics (44k vs 100 puts/s at 1 KiB) and posture. - **The iroh-blobs DESIGN.md lineage ("faster than the filesystem for small items") is now fully triangulated**: its kv-tier claim is true for its *inline small items with batched/durable-None commits* (iroh batches writes through a serialized actor with batch windows — `BatchOptions.max_write_batch`, the same amortization pg gets from WAL group-commit *for free across connections*). Our harnesses measure per-commit put discipline; iroh's actor batching is the reconciliation, and it is also why our POC #3 sqlite numbers (44k) are the honest ceiling for a per-op CAS store. - **Batched commits are a real Phase 1 lever, proven by three independent engines**: pg's group-commit (B3), iroh's write actor windows, redb's None+flush emulation (arm C4's structure). The store's put seam could take a `WriteBatch`-shaped commit later — an API question for Phase 1 (pinned batch puts exist in ADR-005's surface already; the *durability-window* question is adjacent). - **Reads: no case for redb on merit** — page-cache reads are what any of these give the large tier for free; sqlite's 19 µs probe was already fine. - **Anti-recommendation adopted (ADR-007, 2026-10-02): redb stays ruled out** — recorded in the decision so the question does not reopen implicitly. SQLite's engine pin now has four-way triangulated evidence (POC #3 fs, POC #5 pg, POC #6 redb, plus the lineage citation), and ADR-007 ships the kv tier as sqlite + postgres. If a future consumer wanted redb-shaped durability trade-offs (ephemeral cache tier), that's a *different* tier than the kv engine slot. ## POC quality notes - The inherited 14-test suite passes unchanged (the redb backend is additive; sqlite/fs arms untouched). The CAS semantics of put-if-absent are exercised through the smoke path; redb's insert-returns-prior-guard shape documented in `kv_redb.rs` docs. - clippy `-D warnings` clean, fmt clean. - **Honest caveats**: mdraid single dev box whose fdatasync latency ranged 0.24-30 ms during the session under co-resident load (pg container churn from POC #5 ran through the first bench; stopped before the final redbdiag re-run — relative conclusions unaffected, absolute redb-Immediate numbers are *device-pressure-dependent* and the honest statement is "1 fdatasync per commit, cost = device fdatasync latency, observed 8-30 ms here"); the 2s full bench was dropped from the record (its 128/256 KiB redb arms hit shell timeouts; 1s arms with explicit size lists used instead — the harness gained a size-list argument for this); the 1g redb working-set files (~270 MB at 128 KiB arm) cleaned from /tmp after diagnosis. - Instruments: `src/kv_redb.rs` (the backend: Immediate + None + flush emulation methods), `src/bin/bench_small.rs` (inherited + redb arm + size-list arg), `examples/redbdiag.rs` (durability decomposition), `redbdiag2.rs` (None + periodic-flush group-commit emulation), `redbdiag3.rs` (commit-vs-insert split, per-put file growth), `redbdiag4.rs` (warm distribution), `redb_conc.rs` (scale-out, both durability modes), `sqlite_conc.rs` (copied head-to-head from #5).