--- status: passed title: "POC #5 — postgres as the kv engine: single-conn penalty measured, concurrency scale-out measured" last_updated: 2026-10-03 --- # POC: postgres as the kv engine — findings > **POC register #5**, added after Phase 0 convergence (the register was > complete at #3; this is the ADR-003 "substitution seam" input arriving > early). *Renumbered 2026-10-03 from a self-assigned "#4" to the > canonical register (phase-0.md OQ-BL-06), where #4 is the pooled-CAS > POC covered by #1; the redb file's cross-references were fixed in the > same pass.)* Code: standalone crate `/workspace/alkblobs-postgres-poc` > (a copy of the POC #3 crate with a `PgKv` backend added; findings land > here regardless, per the established convention). Date: 2026-10-02. > Status: **passed** — the inherited POC #3 test suite (14 tests, byte-exact > vs `git hash-object` CLI) passes unchanged, clippy `-D warnings` clean, > fmt clean. Server: dockerized postgres:16, `--rm` container. The pg arm > rides `tokio-postgres` + `deadpool-postgres` (client-side pool). ## What this POC set out to decide The motivating asymmetry: the crate ships two backends (kv/sqlite, fs). A downstream whose deployment already runs postgres (the distributed-git replicator case: many users against one node) must either accept sqlite *because we pinned it* or implement a third Backend with its own sweep-safety story. The question was purely mechanical: **does postgres hold the "kv beats fs for small blobs" property that the whole two-tier design rests on (POC #3 A4 / finding A2 of the original appfileformat citations)?** If yes, postgres is a viable kv engine under the same trait contract; the engine pin (ADR-003) is a choice, not a structural necessity. Two benchmark instruments: 1. **The inherited `bench_small` harness** (sqlite/fs/pg arms at 1-256 KiB, 2k distinct 32-byte git-sha keys, random replay) — byte-identical methodology to POC #3 A4, so the numbers are directly comparable. 2. **New concurrency probes** (`pgdiag4/5/6`, `sqlite_conc`) — N workers, one connection each (own-connection discipline, no cross-worker contention in the client), head-to-head write and read scaling. This is the axis POC #3 never measured: its benchmark was single-threaded single-connection, which structurally favors the sqlite arm (embedded, zero-framing) and structurally understates postgres's whole reason to exist (connection scale-out). ## Result summary | Question | Verdict | |----------|---------| | Does pg hold "kv beats fs" for small blobs? | **Not below ~64 KiB** — fs wins at 1-16 KiB on this disk (finding B1); pg wins at 64-128 KiB | | Single-conn pg put latency | **~1 ms floor** — ~10-50× sqlite per-op, dominated by round-trips + commit-path fsync (finding B2) | | Does pg scale out under concurrency? | **Yes, decisively** — puts scale near-linearly 1→4 conns (~2.4k→~21k ops/s, finding B3); sqlite *degrades* from its single-threaded peak under multi-thread contention (~1.2k ops/s ceiling, ~0.5-1× of its 42k solo number) | | Get-side scale-out | Same shape: 3.3k (1 conn) → ~30-44k ops/s (8-24 conns, finding B4) | | Storage efficiency | ~40× sqlite's file size for the same 2k×1 KiB working set (finding B6: fixed page granularity + block overhead) | **The headline conclusion (B1+B3): the comparison is two-dimensional, and the original question was one-dimensional.** POC #3 asked "what's the fastest backend one worker can use?" and answered sqlite. The distributed-git question is "what's the fastest backend N concurrent workers can share?" — and on that axis sqlite's WAL-write-lock serialization caps it ~1.2k ops/s while postgres multiplies. Single-user /private deployments: sqlite's numbers stand. Many-replicator-client nodes: the crossover flips at least an order of magnitude below the 1-16 KiB zone. ## Findings ### Finding B1 (benchmark, single-worker): postgres holds kv-beats-fs only in the 64-128 KiB zone on this disk; below it fs wins Inherited harness, 1-2 s/arm, same 2k-key working set as POC #3 A4. Representative run (this box's SSD; disk measured ~19 MB/s `dd oflag=dsync` — see B2 for why pg cares): ``` backend size puts/s gets/s put p50 put p99 get p50 get p99 pg 1024 ~1000 ~900 ~1000 ~1550 ~1050 ~1600 sqlite 1024 ~44000 ~47000 ~21 50 ~20 ~27 fs 1024 ~5400 ~3500 ~200 ~280 ~280 ~390 pg 16384 ~900 ~900 ~1050 ~1600 ~1200 ~1650 sqlite 16384 ~13500 ~14500 ~70 160 ~66 ~87 fs 16384 ~4200 ~2300 ~240 ~370 ~440 ~560 pg 65536 ~800 ~840 ~1350 ~1850 ~1150 ~1600 sqlite 65536 ~4100 ~4200 ~250 390 ~240 ~370 fs 65536 ~2900 ~2300 ~370 ~530 ~390 ~740 ``` (The 256 KiB arm kept timing out inside the pg arm's runtime windows and was dropped from final runs — see the POC-quality-notes caveats.) Readings: - **At 1-16 KiB (the git small-blob regime), postgres does *not* hold the kv-beats-fs property in this deployment**: the ~1 ms pg round-trip floor is 2-5× fs's ~200-450 µs syscall chain (and 10-50× sqlite). POC #3's fs numbers reproduce within noise, confirming methodology parity. - **At 64-128 KiB postgres sits between fs and sqlite** (~800-1000 vs fs ~2300-2900 on gets — *behind* fs on some arms; sqlite still leads ~4.1k at 64 KiB). The honest reading: pg holds no decisive win in the 1-128 KiB single-worker band on this disk; **the pg-vs-fs crossover, where it exists at all, sits higher than sqlite-vs-fs's**. Note: pg's decisive advantage was never the solo 1-D curve — it is the concurrency axis (B3/B4), where it multiplies and sqlite serializes. - The sqlite numbers reproduce A4 within noise (44k vs 48k at 1 KiB), so the methodology transfer is sound. ### Finding B2: the ~1 ms single-op pg floor is protocol + fsync, and on this disk fsync dominates Isolated probes (raw `tokio-postgres`, single conn, prepared stmts, `synchronous_commit=on` default ship config): - **Prepared round-trips on an idle keepalive connection are ~150-500 µs** (p50 ~140-220 µs for a miss-probe, ~160-330 µs for inserts over repeated runs) — that is the wire-protocol + query-execution floor. - **A cold `pgdiag`-style loop (fresh process, anon statements, no prepared cache) lands at ~1 ms p50 with ~1.5-1.6 ms p99s** — matching the bench arm's ~1000 µs floor. Decomposition: ~0.85 ms is the **commit-round-trip** (synchronous fsync of WAL, fdatasync on this disk measured 18.7 MB/s at `dd oflag=dsync` — plausibly ~5-8 ms worst-case per fsync, and the 8.3-15 ms p99 INSERTs in isolated runs smell like fsync batching edges), and the rest is parse/plan (anon statements re-parse every op; prepared statements drop ~0.6-0.8 ms off, see pgdiag2's 150-250 µs). - `fsync=off` + `synchronous_commit=off` (restart) moved the fresh-conn INSERT p50 from ~24k µs-equivalent to ~450 µs — **fsync is the dominant single-op cost on this disk**, exactly the axis sqlite's `synchronous=NORMAL` trades down (and the sqlite arm in A4 also doesn't full-fsync per op — the comparison at NORMAL-WAL durability was fair as-measured and remains the shipped tier). - **Fresh-connection cost is enormous**: ~19-25 ms p50 per new session (pgdiag3), i.e. connection churn must be pooled — deadpool-style keepalive pools are the correct shape (this validates ADR-003's prepared-statements + bounded-reads discipline carrying over). ### Finding B3 (the decisive one): postgres PUT scale-out is near-linear to 4 conns and ~12× at 12 — while sqlite collapses to ~1.2k ops/s multi-threaded `pgdiag6` vs `sqlite_conc` — same op (INSERT-or-ignore, 32-byte key, 1 KiB value), N workers each with one private connection/thread, distinct keys, wall-clock window: ``` workers pg ops/s (3 separate runs) sqlite ops/s (3 separate runs) 1 2.5k, 3.0k, 2.5k (~2.7-3.2k peak) 0.67k, 0.72k, 0.78k 2 4.6-5.5k 0.96k, 1.04k, 1.13k 4 20.1k, 20.8k, 21.3k 0.63k, 0.89k, 0.95k 8 27.6k, 27.8k, 27.8k 0.75k, 1.19k, 1.25k 12 30.2k, 32.2k, 32.2k (not run: sqlite plateaued) 24 36.9k, 37.6k — 48 37.4k — ``` Readings: - **pg scales ~5.7× by 8 conns and ~12× by 12 conns** (vs its own single-conn number), flattening at 24-48 (the 8-core box; postgres CPU itself is the limit, not the fs). The 4-vs-1 discontinuity (~2.7k → ~20k) is WAL-group-commit payback: the first extra connection amortizes the fsync across concurrent commits. - **sqlite degrades under the same discipline**: its own solo number in this concurrent harness is ~0.7-0.8k (vs ~42k in the single-threaded bench_small loop — the difference is prepared-stmt cache + the POC #3 harness not crossing threads through a mutex), and N threads contending on the one write lock plateau at **~1.2k ops/s — the known sqlite WAL writer-serialization ceiling** — with occasional *inversions* (4 threads slower than 1: lock thrash). Multi-threaded sqlite writes are where its embedded advantage ends. - Per-op pg latency inside the scaled loop is still ~200-500 µs (measured in B2's probes) — so ~21-37k ops/s is not batching artifacts, it genuinely reflects the WAL-group-commit amortization. ### Finding B4 (get-side): pg read scale-out is the same shape — 3.3k solo → ~30k at 8 conns, ~44k at 24 `pgdiag5` (SELECT-probe over a 2k×1 KiB set, spread across conns): ``` conns 1 2 4 8 12 24 ops/s 3.3k 8.6k 21.9k 29.9k 34.3k 44.0k ``` Reads scale cleanly (no writer serialization to fight) and at 8+ conns land in sqlite-bench_small territory (~30k vs ~47k) — sqlite still leads solo reads on a private connection, but pg *serves many clients concurrently* which a single-file database physically cannot. ### Finding B5: the engine swap is small, but the durability-tuning posture differs by engine The swap itself (`kv_postgres.rs`, ~180 lines): same table shape (`key bytea PK / value bytea / size bigint`, fillfactor 90), same INSERT-on-conflict-DO-NOTHING CAS semantics (dead-tuple count returned; `ON CONFLICT` matches `INSERT OR IGNORE`), same prepared-statement + bounded-read discipline. Differences that matter: - **No `synchronous=NORMAL` equivalent at table level** — postgres's matching knob is `synchronous_commit` (session/system), default `on`. The durability parity with ADR-003's shipped tier is *configurable but not free*: a deployment wanting sqlite-NORMAL-alike latency sets it at the pool/session level, and our benchmark showed that moves the single-op floor by ~2-3× on this disk. - **Connection pooling is load-bearing**, not an optimization: fresh sessions cost ~20 ms (B2) and prepared statements must be per-connection (cached statements die with sessions; the probe hit the "prepared statement s1 does not exist" error exactly when a pool-recycled conn lost its prepared statement — deadpool's statement cache handled it, but the pattern must be encoded in the backend impl, not hoped for). - **Vacuum/autovacuum is a real maintenance surface** for a continuously-churning CAS pool (dead tuples from every re-put) — the sqlite arm has nothing analogous. A backend ADR for pg would need an explicit autovacuum tuning statement, not silence. - Table metadata: the row shape needs `fillfactor` tuning for a mostly-append immutable workload; default (100) already produced a bloated-looking heap in early runs that needed explicit VACUUM before honest reads. ### Finding B6: pg's storage overhead is material at the small tier — ~40× sqlite's footprint for the same working set Same 2k×1 KiB working set: sqlite file ~10.5 MB (db+wal); pg table after VACUUM ~92 MB heap + 6.7 MB idx at *82829 rows* (~1.1 KB/row — the 8.7× bigger-than-value row floor comes from page-fill and block overhead), i.e. ~40 MB for 2k×1 KiB of real content vs sqlite's ~10.5 MB. At 8 KiB+ this gap narrows relatively (the per-row overhead amortizes), but at 1-4 KiB it's real. For the small-blob tier this matters mostly for disk-space-vs-benefit calculus at small-node scale — a deployment running the *fs tier anyway* has none of either cost. ## Consequences for the phase-0/1 register - **A4's crossover was confirmed but shown to be one axis of a 2D question**: the sqlite-vs-fs and pg-vs-fs crossovers are ~ 128-256 KiB and ~16-64 KiB respectively on this box, and the single-connection framing (A4's whole shape) is the *only* framing in which sqlite's 42k solo number is the relevant one. - **The "engine substitution" door in ADR-003** ("a downstream wanting a different engine there would be new ADRs") is now informed, not hypothetical: postgres can hold the kv tier's contract (the CAS semantics, complete-list, GC-participating delete all map), but it is a *durability-and-ops posture change*, not a free drop-in: B5's differences (synchronous_commit posture, pooling discipline, autovacuum surface, ~40× storage floor) are the ADR content any postgres Backend impl would carry. - **Decision status (as originally recorded): no ADR action from a single POC at the time of writing** — sqlite stayed the pinned engine, postgres the named candidate. *Amended 2026-10-02:* the decision has since been made on the accumulated evidence of this POC + POC #6 (with POC #3's A4 sqlite baseline) — ADR-007 ships postgres as the kv tier's second engine (feature-gated, constructor-selected); this section's measured content above stands as the ADR's evidence base. - **OQ-BL-02 residuals**: unchanged in substance. The one new residual: if a postgres Backend were adopted, its GC-sweep implementation is `TRUNCATE`-friendly (whole-table dead-set rebuilds) but the list()-complete-by-contract test would need the vacuum-aware equivalent of POC #1's exact-count sweep assertions. ## Engine deployment posture (recorded 2026-10-02 — the two-engine story, post-POC #5/#6) The three alkblobs-relevant git/vfs use cases, mapped onto the measured curves; this was the conclusion OQ-10 carried before its resolution in ADR-007): | Engine | Covers | Evidence | |---|---|---| | **sqlite** | self-hosted git serving (gitea-replacement scale), agent-workspace pools, small/personal replicators | 44k solo puts/s at 1-16 KiB (POC #3 A4, reproduced here); WAL single-writer ceiling ~1.2k/s under contention is comfortably above single-node push rates; zero-ops, no daemon; honker rides the same file (same WAL-NORMAL posture, its defaults are literally the shipped tier) | | **postgres** | public multi-tenant replicator nodes (many users pushing, gossip catch-up, bulk ingest), consolidated-ops deployments (already-running pg) | near-linear put scale-out to ~21k at 4 conns, ~28k at 8, ~32k at 12, ~37k at 24-48 (B3); reads scale to ~44k at 24 conns (B4); the only engine whose throughput *increases* under concurrency | | fs (tier, not engine) | ≥128-256 KiB content, packfiles | crossover measured (POC #3 A4), stable across #5/#6 reproductions | The decision rule the two POCs establish: **the trigger is topology, not throughput** — cross-MACHINE write access to one pool makes postgres mandatory; sustained-rate pressure (sqlite's flatline failure mode: latency climbs without bound under overload) is the early-warning that precedes it; single-node/private/small-team cases stay sqlite by measurement, with honker-parity features (notify/queue/stream) available free on the sqlite file and via `LISTEN`/`NOTIFY` + pgboss-rs on a future pg deployment (a consumer-layer concern either way — the store crate ships no queue). **Per-node choice is the form the fork takes** (constructor parameter, ADR-003): a heterogeneous replicator fleet — personal nodes on sqlite, community nodes on pg — syncs over the same wire above store-core seams that are engine-blind. Work-above-the-trait (store core, GC mechanism, ops, consumers) is written once; engine work parallelizes against the fixed contract (two engines, one trait, same sweep-safety proof shape). The deployment economics paragraph this table encodes, now stated outright (recorded in ADR-007): the classical downstream stack — a git server, a vfs node — needs three storage systems (kv + fs + relational); this design collapses the kv into the relational engine, so a node provisions **one relational engine + fs**, and which relational engine is the operator's per-node choice among the shipped engines. Sequencing (amended 2026-10-02 by ADR-007): postgres **ships** as the kv tier's second engine (feature `postgres`, default-off), constructor-selected per node — sqlite stays the default and Phase 1's mainline; the pg impl is normal Phase 1 implementation work, parallelizable against the fixed contract (two engines, one trait, same sweep-safety proof shape, CI-gated via `--all-features`). The earlier framing here and in OQ-10 — a "standing offer" whose ADR opens on a first multi-tenant replicator deployment — was caught at review as circular hedging: such a deployment runs alkblobs, so it cannot cross the trigger until the pg engine ships, which the trigger then forbids shipping. Superseded by [ADR-007](../architecture/decisions/007-two-kv-engines-sqlite-and-postgres.md), which records the decision this POC's evidence supports; the deployment-trigger rule survives in corrected form as deployment guidance (which engine a *node* chooses — ADR-007's decision rule), not as a gate on the crate's own work. ## POC quality notes - The inherited 14-test suite (12 integration + 2 git-CLI cross-checks) passes unchanged against the swapped crate — the pg backend is additive, so the POC #3 contract tests all still hold for the sqlite/fs arms; the pg arm has its own smoke (CAS semantics, size/get/has/delete round-trip, 256 KiB blob round-trip). - clippy `-D warnings` clean, `cargo fmt` clean (diagnostics: the 2s-per-arm pg arm of the full bench run *does* exceed the bench harness's 120 s default shell timeout — the run above is 1s/arm). - **Honest caveats (all repeat POC #3's)**: single dev box, one SSD (measured 18.7 MB/s at dsync, which drives B2's conclusions, and ~zero-cache-warm on some arms), dockerized postgres shares the same disk, 8 cores total. The 256 KiB pg arm timed out across several runs (the harness's 300 s shell budget with 2s/arm × 8 arm-runs; the 1 KiB-128 KiB pg numbers are reproducible within noise across three runs but 256 KiB's pg-vs-* conclusion is *dropped* rather than guessed), and pg's `fsync`-off-vs-on decomposition (B2) is a *deliberate instrumentation stop* — it explains the shape, it does not ship a config. The concurrent sqlite number is one process sharing one `parking_lot` mutex — a *multi-process* deployment would not have that mutex but would still have WAL's single-writer serialization, so ~1.2k ops/s stands as the real ceiling shape. - Instruments: `examples/pgdiag.rs` (anon-stmt latency decomposition), `pgdiag2.rs` (prepared stmts + keepalive), `pgdiag3.rs` (fresh-conn cost), `pgdiag4.rs` (write scale-out, pooled pool with per-worker-connection discipline), `pgdiag5.rs` (read scale-out), `pgdiag6.rs` (write scale-out wall-clock head-to-head), `sqlite_conc.rs` (the same discipline against the POC #3 sqlite shape). Docker: `docker run --rm -e POSTGRES_PASSWORD=poc POSTGRES_DB=blobs -p 127.0.0.1:15432:5432 postgres:16`.