writeonce/docs/stories/language-runtime-database/done/22-durability-throughput-scale.md

iteration	status	chain
22	done	2

Iteration 22 — durability proof, throughput, and scale under load

Format: fiberloom product/story-iteration-template. Part of Story — one language, one runtime, one database, one binary.

Inserted 2026-08-15. The measurement backbone. Everything after the functional engine (9/9b) is an optimization, and an optimization without a number is a guess — this iteration is the number. It comes before the optimization iterations (7b GC, 8 shard-actor, 23 io_uring) reopen for performance work, because each of those must be gated by re-running THIS iteration's benchmark and showing the number moved the right way.

✅ LANDED 2026-08-21 — the measurement backbone exists and has run: docs/examples/db-bench (seed/read/query/write/mix/msgrate/ wal/verify, per-op time.ticks µs timing, 1µs-histogram percentiles), scripts/db-bench.py (campaign driver + gates), bench/baseline.json (74 metrics, the first contract — tolerances tuned by a two-run repeatability check: mix* 50%, read/query 35%, rest 15%), just db-bench / db-bench-quick. Proofs: restart-persistence + 3× kill -9 battery at BOTH shard counts, all acked rows present every time; the gate BITES (doctored results fail on exactly the doctored metric). Headline findings: durable seed ≈4.5k/s vs ram ≈297k/s (iteration 23's case, measured); point lookups are O(table) — reads ≈1.5k/s at p50 ≈600µs on 20k rows (the probe walks every slab); mixread 1,280 ops/s single- vs 21 ops/s multi-shard (the arc's honest price); msgrate 13.4M same-heap vs 2.45M cross-shard (deviation 4's mutex-inbox number). The arc's delta table lives in story 8. Deviations, disclosed in the plan: histogram not reservoir; all + wal modes added (RAM store dies with the process; clean acked-line crash vehicle); one python driver; time.ticks routed via an explicit dispatch arm. Standing finding: hand-built multi <TableClass> SEGVs on drop (elements classed OWNED, refs are scalar ids) — own slice.

SPEC APPROVED 2026-08-21 — the four forks below are SETTLED as their recorded leanings (developer confirmation), plus two new decisions: the vehicle is a NEW sample docs/examples/db-bench (employee stays a teaching sample) and time.ticks (CLOCK_MONOTONIC µs) is the iteration's one runtime addition. Spec: 2026-08-21-db-bench-design.md · plan: 2026-08-21-db-bench.md — in progress (second slice of the chain).

RE-SEQUENCED 2026-08-21 (developer decision): runs AFTER the arc's stage 3 — the transparent DB actor is a correctness hole (a multi-shard program touching the database traps WO_T_DB today), and fixing it first lets ONE benchmark campaign cover single- and multi-shard honestly. The arc's stages 1+2 landed 2026-08-20 unmeasured; their delta is recorded retroactively against this iteration's first baseline. New measurement target since stage 2: the mutex-guarded inbox + eventfd (the plan's deviation — lock-free rings arrive only if this number says the mutex costs). Chain order: stage 3 → 22 → 31 → 24 → 23 → 32.

Goals

• Durability is proven by a restart, not asserted. The employee program (iteration 9b) runs, writes rows, is stopped and restarted, and every acknowledged write is present after replay — the WAL's promise turned into a scripted acceptance on a real program, not just the unit-level crash battery.
• Read and write throughput are measured, published, and defended. A repeatable benchmark drives the engine through the language (not the C API): inserts/sec, point-reads/sec, indexed-query/sec, each with p50/p99 latency, recorded in the tree so a regression is a diff.
• The scale target is a gate, not a slogan. "A million users can read and write" becomes a concrete load: a dataset of ~1M rows across the sample's tables, a mixed read/write workload at a stated concurrency, sustained for a stated duration, with throughput and tail latency inside a stated budget and RSS flat (the log-watcher soak discipline, at database scale).
• The benchmark is the contract every later optimization signs. 7b (GC), 8 (shard-actor threads), and 23 (io_uring) each re-run this and record the before/after — no optimization lands without a measured delta.

Acceptance Criteria

• What to achieve?
  • Given the employee program seeded with data and then stopped,
  • when it is restarted and queried,
  • then every acknowledged row is present with its exact contents, the ids continue past the persisted maximum, and a query that used an index before the restart uses it after (the index was rebuilt on replay).
• What to achieve?
  • Given the benchmark harness driving inserts, point reads, and indexed queries through compiled .wo,
  • when it runs to completion,
  • then it reports ops/sec and p50/p99 for each operation class, writes the numbers to a tracked results file, and fails if any number crosses a recorded regression threshold.
• What to achieve?
  • Given ~1M rows and a mixed read/write workload at the target concurrency held for the target duration,
  • when it runs,
  • then throughput stays above the floor, p99 stays under the ceiling, RSS is flat between a warmed baseline and the end (no growth beyond tolerance), zero descriptors leak, and — for a write-inclusive run under a durable configuration — a kill mid-load followed by replay loses no acknowledged write.
• What to achieve?
  • Given any later optimization iteration (7b, 8, 23),
  • when it claims a speedup,
  • then this benchmark's before/after numbers are in that iteration's record, and a claim with no measured delta is not accepted.

Out Of Scope

• The optimizations themselves. This iteration MEASURES; 7b/8/23 change. A single-thread RAM-authoritative baseline is a legitimate first number — the point is to have one before anyone tunes.
• Distributed / multi-machine load. Same-machine, one process (or one process per shard once iteration 8 lands). Cross-host is the network layer's concern, much later.
• Micro-optimizing the benchmark harness. It must be honest and repeatable, not itself fast; if the harness is the bottleneck the spec says so and fixes that, but a perfect load generator is not the deliverable.
• A cost-based query planner. Index selection is 9b's; this iteration measures what 9b lowers, it does not make the planner smarter.

Info

Forks the spec must settle:

1. What generates the load, and in what language? The doctrine is "the sample is the test", so the honest generator drives compiled .wo — a benchmark mode in the employee program (or a sibling sample) that loops inserts/reads/queries and times them. The alternative — a C harness calling the engine API directly — measures the engine but skips the compiler's lowering, which is exactly the layer a language-integrated query has to pay for. Leaning: .wo benchmark mode for the headline numbers (the number that matters is end to end), with the C-API microbench kept only to attribute a regression to engine vs lowering.

2. What are the actual budgets? Throughput floors and latency ceilings have to be numbers, and the first run sets them — but the spec must decide whether the gate is absolute (">= N ops/sec on the reference machine") or relative ("no worse than the last recorded run by more than X%"). Absolute gates rot across machines; relative gates need a committed baseline file. Leaning: relative gates against a tracked bench/baseline.json, refreshed deliberately with a commit that says why, plus a loud absolute floor so a catastrophic regression fails even on a slow machine.

3. What does "1M users read and write" concretely mean? A million long-lived idle connections is a different test from a million rows under a churning read/write mix from a bounded connection pool. The sample's shape (departments, employees) suggests rows, not connections, as the scale axis for THIS iteration; the connection-scale test belongs with the shard-actor runtime (iteration 8) and the eventual network layer. Leaning: ~1M rows + a bounded concurrent read/write workload here; connection scale deferred to 8 with a cross-reference.

4. Durable or RAM-only for the throughput headline? fsync-per-commit (the current per-statement durability) will dominate write throughput and is the honest number for a durable workload; RAM-only (no WO_DATA) measures the engine's ceiling. Both matter and mean different things. Leaning: publish both, labeled — durable is the number an operator plans against, and the gap between them is precisely what iteration 23 (io_uring group-commit) exists to close.

Proposed Solution

• Brainstorm the spec, settling the four forks; then a plan whose first task is the harness and the baseline file, because nothing downstream means anything without them.
• Sequence the performance chain around this iteration (rewritten 2026-08-21 — the first version predated 7b and the arc landing first):
  1. Already landed unmeasured: 9b, 7b (inferred GC + mark-sweep, 2026-08-18), the arc's stages 1+2 (fibers + shards, 2026-08-20). Their deltas are owed retroactively against the first baseline.
  2. Arc stage 3 (transparent DB actor) lands → 22 runs: restart-persistence proof + baseline benchmark, durable and RAM-only, single- AND multi-shard, plus the mutex-inbox number.
  3. 31 (actor lifecycle), then 24 (chat) → re-run the concurrency-facing numbers at the connection scale chat unlocks.
  4. 23 (io_uring group-commit) → re-run 22's durable write number, record the delta against the fsync-per-commit baseline — the payoff.
• The benchmark harness and its baseline live under bench/ (or the existing runtime/bench/), and just gets a db-bench recipe kept off the fast path, exactly like log-watcher::soak.