docs(research): POCs #5/#6 — postgres and redb as the kv engine, both substitution-seam inputs
Post-convergence engine studies against the ADR-003 kv pin, using POC #3's harness unchanged (sqlite/fs arms byte-identical) plus the concurrency axis POC #3 never measured: - #5 postgres (B1-B6): solo pg floor ~1 ms (fsync-dominated, strace- decomposed), ~40x storage overhead; decisive scale-out — pg PUTs ~12x at 12 conns / ~37k ops/s while sqlite flatlines at its ~1.2k WAL single-writer ceiling. Viable future ADR for the multi-client replicator shape; durability/ops posture change, not a drop-in. - #6 redb (C1-C6): the "2-7x over sqlite" folklore inverted — sqlite ~430x over redb at crash-consistent puts (redb Immediate = 1 fdatasync/commit, 8-30 ms on this disk; its None tier is not crash-consistent). Reads ~530k/s but irrelevant. Ruled out at a durability-tier mismatch: WAL-NORMAL's shipped tier is not expressible in redb 4.x's two-level API. - Net: sqlite's pin now has four-way triangulated evidence (fs vs sqlite #3, pg #5, redb #6, lineage citation); batched commits proven as the universal lever across all three engines. Code: /workspace/alkblobs-postgres-poc, /workspace/alkblobs-redb-poc (standalone crates per the POC placement convention; 14 inherited tests passing in each, clippy -D warnings + fmt clean). Verification: no src/ changes — docs only; both POC crates verified (test/clippy/fmt) with findings recorded above.
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---
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status: passed
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title: "POC #6 — redb as the kv engine: the 2-7×-over-sqlite claim does not survive crash-consistent measurement"
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last_updated: 2026-10-02
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---
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# POC: redb as the kv engine — findings
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> **POC register #6**, added post-convergence (same standing as #5: an
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> ADR-003 substitution-seam input, not a Phase 0 gate). Code: standalone
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> crate `/workspace/alkblobs-redb-poc` — a copy of the POC crate with the
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> pg arm replaced by a `RedbKv` backend (`redb` 4.3, the engine iroh-blobs'
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> fs store uses for its metadata tables). The inherited sqlite/fs arms and
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> the 14-test suite are byte-identical to POC #3/#4's methodology.
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> Date: 2026-10-02. Status: **passed** (14 tests, clippy `-D warnings`
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> clean, fmt clean).
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## What this POC set out to decide
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The "opposite route" from POC #4: postgres is complicated, we only need a
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kv — what about a high-performance *embedded* kv like redb, which the
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DESIGN.md lineage (iroh-blobs' "faster than the filesystem for small
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items") and downstream folklore put anywhere from **2-7× over sqlite**,
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which was itself 7-9× over fs (POC #3 A4)? If redb beats sqlite by that
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margin at equal semantics, the kv tier's engine pin (ADR-003) is again
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informed rather than hypothetical — and so is the anti-recommendation if
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it doesn't.
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Same 2D instrument as POC #4 (the shape of the question, learned there):
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1.Solo sweep (inherited `bench_small` harness, redb/sqlite/fs arms at
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1-256 KiB, identical working set) — where the 2-7× claim should show up.
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2. **Worker scale-out probe** (`redb_conc`, plus the POC #4 `sqlite_conc`
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head-to-head) — redb is a single-file single-writer engine; does it
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degrade like sqlite's WAL or survive like postgres?
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## Result summary
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| Question | Verdict |
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|----------|---------|
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| redb 2-7× over sqlite for small blobs? | **No, inverted: sqlite ~430× over redb on crash-consistent puts** (finding C1) |
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| redb single-put floor | **~8-20 ms** at `Durability::Immediate` — one fdatasync per commit whose latency *is* the commit (finding C2, strace-verified) |
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| 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 |
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| redb reads | **Catastrophically fast and ~free**: ~530-575k gets/s (p50 1-4 µs) — mmap/page-cache reads, 1000× sqlite (finding C3) |
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| 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) |
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| Is the swap small like pg's? | **Yes** — ~230 lines, same table shape, but the durability API is coarser (finding C6) |
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**The headline conclusion (C1+C2): the 2-7× claim is a *durability-tier
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confusion*, not a measured property.** It implicitly compares workloads
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where fsync costs are hidden (in-memory, or crash-inconsistent commits).
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Under the shipped tier this crate would actually tolerate — redb
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`Immediate` vs sqlite `synchronous=NORMAL` WAL, both crash-consistent —
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sqlite's WAL commits are fsync-free (checkpoint-time only) while redb's
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Immediate commits are one-fdatasync-each: the 430× inversion. redb's
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genuine weapon is reads (C3), and reads are the thing the fs backend and
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page cache were never losing at.
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## Findings
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### Finding C1 (benchmark, solo): sqlite ~430× faster than redb on crash-consistent puts at 1 KiB; the 2-7× claim inverted
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Inherited harness, 1 s/arms, representative run (see the harness for a
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re-run; all sizes measured, table shows key ones):
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```
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backend size puts/s gets/s put p50µs put p99µs get p50µs get p99µs
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redb 1024 ~100 ~540000 8334 ~45000 1.0 5
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sqlite 1024 ~44000 ~51000 21 64 19.0 24
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fs 1024 ~5000 ~3500 200 285 282.0 365
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redb 16384 ~105 ~530000 8333 ~42000 1.0 5
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sqlite 16384 ~13000 ~14800 73 205 66.0 79
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fs 16384 ~4700 ~2870 210 350 410.0 580
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redb 65536 ~100 ~530000 8334 ~70000 1.0 5
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sqlite 65536 ~3700 ~4080 270 495 243.0 310
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fs 65536 ~2550 ~1840 410 550 543.0 665
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redb 131072 ~103 ~527000 8330 ~73000 1.0 5
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sqlite 131072 ~1900 ~1930 530 795 507.0 768
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fs 131072 ~1930 ~1540 527 795 629.0 840
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redb 262144 ~86 ~516000 8333 ~70000 1.0 5
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sqlite 262144 ~900 ~990 1080 2825 1008.0 1194
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fs 262144 ~1530 ~2470 588 1375 381.0 580
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```
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Readings:
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- **redb puts are flat ~100/s regardless of size** (1 KiB through
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256 KiB — the put cost is the commit, not the value). 8.3 ms p50,
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40-85 ms p99. sqlite remains 440×/1300×/4×/2×/0.6× ahead at
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1/16/64/128/256 KiB respectively.
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- **The fs crossover survives** (fs wins 256 KiB puts, and puts get
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close at 128 KiB) — A4's threshold story is unchanged by a fourth arm.
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- sqlite's numbers reproduce A4 within noise again (44k at 1 KiB):
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methodology parity across all four POCs holds.
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### 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
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Decomposition probes (`redbdiag*`):
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- **Warm Immediate puts: p50 8.3-19.7 ms depending on device pressure,
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p99 40-135 ms.** `strace -c` on a full bench run: 5784 fdatasync,
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~300 µs mean each; `strace -tt -T` on a warm solo run: 363 fdatasync
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accounting for **95% of wall time** (10.2 s of 10.7 s), per-fdatasync
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19-30 ms p50-p90 on the loaded disk. The commit latency *is* the
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fdatasync latency.
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- The fdatasync count per put is ~1 (~300 Immediate commits + ~13
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startup/savepoint commits vs 313 calls in the isolated probe) — no
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2-phase double-sync; it's a single sync, just an expensive one on a
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device with dirty-page pressure (the pg container's WAL churn was
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co-resident through part of this POC — noted in the caveats; the
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*relative* redb-vs-sqlite conclusion is unaffected, and redbdiag was
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re-run clean at the end: p50 18.5 ms).
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- **Durability::None puts: 27-33 µs p50** — the fsync removal alone
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accounts for the 300× spread. Confirms the mechanism precisely.
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### Finding C3: redb reads are ~530-575k gets/s (p50 1-4 µs) — the page cache through an mmap-like path
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The has/get probes return in 1-4 µs p50 (5 µs p99) across every size —
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that's not a B-tree probe, that's the OS page cache served from
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process-mapped memory. sqlite's 19-23 µs p50 is a syscall-per-probe; fs's
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282 µs is open+read syscalls. **This is redb's real 10-1000× win — on the
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operation that matters least**, because (a) the fs tier already serves
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large blobs from the page cache the same way, and (b) small-tier gets
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were never the bottleneck axis (sqlite was already the fastest get at
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19 µs, amortized by the store's digest checks anyway).
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### Finding C4: redb's Durability::None concurrency *degrades* — 24.7k solo → 20k at 4 threads → 12k at 8
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`redb_conc` in none-mode: the None arm has no fdatasync to amortize, so
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adding threads only adds internal transaction-lock contention plus the
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None-durability freelist growth penalty (pages freed between durable
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commits accumulate until an Immediate commit collects them — redb's
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documented "rapid growth" behavior). Contrast POC #4: sqlite's
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contentioned WAL plateaued ~1.2k, pg *scaled* to 28k+. redb's None mode
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is the only arm in three POCs where throughput goes **down** as workers
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go up.
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### Finding C5 (the decisive one): redb write scale-out is perfectly flat — ~40-44 ops/s at 1, 2, 4, and 8 threads
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`redb_conc` Immediate mode (same fresh-key discipline as POC #4's
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`pgdiag6`/`sqlite_conc`):
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| workers | redb Immediate | sqlite (same probe) | pg (from POC #4) |
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|---------|---------------|---------------------|------------------|
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| 1 | ~42-44 ops/s | ~700-800/s | ~2.5-3.2k/s |
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| 2 | ~40 ops/s | ~1.0-1.1k/s | ~4.6-5.5k/s |
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| 4 | ~40-43 ops/s | ~0.6-0.9k/s | ~20-21k/s |
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| 8 | ~43 ops/s | ~1.1-1.2k/s | ~28k/s |
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- redb's single-writer file lock + internal transaction serialization
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admits **zero concurrency**: flat at the solo commit cost (~24 ms/put
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fresh-key; ~8-19 ms re-put). sqlite's WAL at least *queues* writers
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behind the one flush lock and gets modest throughput from contention
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efficiency; redb just serializes full fdatasync latencies.
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- Against pg's curve this is the whole argument in one row set: the
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embedded engines' curves are flat/degrading; the client-server curve
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is the only increasing one.
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### Finding C6: the swap is small but the durability API is coarser than sqlite's
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`kv_redb.rs` (~230 lines): same row shape (`TableDefinition<&[u8],
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(&[u8], i64)>` — the value/size tuple matches sqlite's row exactly;
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`WITHOUT ROWID`-equivalent PK ordering is native B-tree). Differences:
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- **redb 4.x offers only `Durability::None` and `Durability::Immediate`**
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— no middle tier. sqlite's `synchronous=NORMAL` (consistent-on-crash,
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not-fsynced-per-commit — the shipped tier this crate benchmarks and
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documents) has **no redb equivalent**: `None` is not
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crash-consistent (unpersisted commits can leave the file failing to
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open until repaired — the freelist bookkeeping itself is lost on
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crash), `Immediate` pays a full fdatasync per commit. The
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durability-posture comparison that POC #4 could tune continuously
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(`synchronous_commit=off` on pg) doesn't exist here.
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- redb's `Database` is process-lifetime; concurrency modes
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(`ExclusiveWriter` default, `SingleWriter`, `MultiWriter`) only matter
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multi-process — in-process it's one writer transaction at a time,
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structurally.
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- `create`-then-repair behavior on a `None`-durability crash and the
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`has+insert` CAS dance inside one write tx are correct but slower
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(the bench's re-put path costs a table probe per commit).
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## Consequences for the phase-0/1 register
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- **redb is ruled out as the shipped kv engine at a measured tier
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mismatch, not a benchmark quibble**: the only durability postures it
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offers are "lose acknowledged data on crash" or "pay one fdatasync
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per commit" — the crate's shipped durability tier
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(`synchronous=NORMAL` WAL, crash-consistent, no per-commit fsync)
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is not expressible. sqlite keeps ADR-003's pin on both economics
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(44k vs 100 puts/s at 1 KiB) and posture.
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- **The iroh-blobs DESIGN.md lineage ("faster than the filesystem for
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small items") is now fully triangulated**: its kv-tier claim is
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true for its *inline small items with batched/durable-None commits*
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(iroh batches writes through a serialized actor with batch windows —
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`BatchOptions.max_write_batch`, the same amortization pg gets from
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WAL group-commit *for free across connections*). Our harnesses
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measure per-commit put discipline; iroh's actor batching is the
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reconciliation, and it is also why our POC #3 sqlite numbers (44k)
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are the honest ceiling for a per-op CAS store.
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- **Batched commits are a real Phase 1 lever, proven by three
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independent engines**: pg's group-commit (B3), iroh's write actor
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windows, redb's None+flush emulation (arm C4's structure). The
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store's put seam could take a `WriteBatch`-shaped commit later — an
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API question for Phase 1 (pinned batch puts exist in ADR-005's
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surface already; the *durability-window* question is adjacent).
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- **Reads: no case for redb on merit** — page-cache reads are what any
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of these give the large tier for free; sqlite's 19 µs probe was
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already fine.
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- **ADR action: none.** SQLite's pin now has four-way triangulated
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evidence (POC #3 fs, POC #4 pg, POC #6 redb, plus the lineage
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citation). If a future consumer wanted redb-shaped durability
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trade-offs (ephemeral cache tier), that's a *different* tier than
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the kv engine slot.
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## POC quality notes
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- The inherited 14-test suite passes unchanged (the redb backend is
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additive; sqlite/fs arms untouched). The CAS semantics of
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put-if-absent are exercised through the smoke path; redb's
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insert-returns-prior-guard shape documented in `kv_redb.rs` docs.
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- clippy `-D warnings` clean, fmt clean.
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- **Honest caveats**: mdraid single dev box whose fdatasync latency
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ranged 0.24-30 ms during the session under co-resident load (pg
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container churn from POC #4 ran through the first bench; stopped
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before the final redbdiag re-run — relative conclusions unaffected,
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absolute redb-Immediate numbers are *device-pressure-dependent* and
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the honest statement is "1 fdatasync per commit, cost = device
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fdatasync latency, observed 8-30 ms here"); the 2s full bench was
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dropped from the record (its 128/256 KiB redb arms hit shell
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timeouts; 1s arms with explicit size lists used instead — the harness
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gained a size-list argument for this); the 1g redb working-set files
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(~270 MB at 128 KiB arm) cleaned from /tmp after diagnosis.
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- Instruments: `src/kv_redb.rs` (the backend: Immediate + None + flush
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emulation methods), `src/bin/bench_small.rs` (inherited + redb arm +
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size-list arg), `examples/redbdiag.rs` (durability decomposition),
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`redbdiag2.rs` (None + periodic-flush group-commit emulation),
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`redbdiag3.rs` (commit-vs-insert split, per-put file growth),
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`redbdiag4.rs` (warm distribution), `redb_conc.rs` (scale-out, both
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durability modes), `sqlite_conc.rs` (copied head-to-head from #4).
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