alkblobs/docs/research/poc-postgres-kv-findings.md

status, title, last_updated

status	title	last_updated
passed	POC #5 — postgres as the kv engine: single-conn penalty measured, concurrency scale-out measured	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, 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.