# Scale Benchmarks: 1M-Item Baselines

## Hardware

macOS Darwin 23.6.0 (Apple Silicon / x86-64 — see run date below).

**Run command:**
```bash
cargo bench --manifest-path tidal/Cargo.toml --bench scale
```

**Date:** 2026-02-23

## Dataset

| Parameter | Value |
|-----------|-------|
| Items | 1,000,000 |
| Creators | 10,000 (100 items/creator) |
| Categories | 20 |
| Embedding dim | 128 (not 1536 — reduced for bench RAM) |
| Signal coverage | 10% view, 5% like |
| Bench tool | Criterion (sample_size=10, 30s measurement, Flat mode) — **closed-loop, single-threaded; reports the mean, not the tail** |

## Measurement contract (read before trusting any number below)

> The `time:` figures Criterion reports are **single-threaded mean per-op cost
> under a closed loop** — the `[lower mean upper]` triple is a *confidence
> interval on the mean*, **not** a latency distribution. They are
> **regression tripwires**, not tail-SLO evidence.
>
> A `p99`/`p999`/tail SLO can only be honored by an **open-loop,
> coordinated-omission-corrected** measurement under real concurrency (the
> `tidal-stress` ramp). A closed-loop mean cannot observe the tail it hides:
> when the system stalls, a closed-loop harness simply *stops sending*, so the
> queue that would inflate p99 never forms. Reporting a mean under a `p99` label
> understates the tail by 10–100× under load.
>
> Therefore every row below is labelled **isolated per-op cost (mean)**. Tail
> SLOs are validated separately — see `tidal-stress` (open-loop) for the
> authoritative p99/p999 under the production workload.

## Acceptance Criteria (isolated per-op cost — regression tripwires, NOT tail SLOs)

| Benchmark | Tail SLO (validated open-loop) | Isolated per-op cost (mean, closed-loop) | Tripwire |
|-----------|--------------------------------|------------------------------------------|----------|
| RETRIEVE | < 50ms p99 | **152 µs** (for_you) | ✅ well under |
| SEARCH | < 100ms p99 | **28.9 ms** (text_only) | ✅ well under |
| Signal write | < 100µs p99 | **82 ns** (rotating 1K) | ✅ well under |

The mean per-op costs sit far under the tail targets — a necessary but **not
sufficient** condition for the p99 SLO. "Well under" means the *mean* clears the
target with headroom; the p99 itself is signed off only by the open-loop
`tidal-stress` run, never by this table.

## Benchmark Results

### RETRIEVE (1M items)

```
retrieve_1m/for_you          time:   [151.88 µs 152.13 µs 152.40 µs]
retrieve_1m/trending         time:   [127.96 µs 128.25 µs 128.52 µs]
retrieve_1m/new_filtered     time:   [  7.5636 µs   7.5855 µs   7.6058 µs]
```

**All RETRIEVE queries < 200µs.** The 50ms target is beaten by 3 orders of magnitude.

- `for_you`: signal-scored ranking over full 1M-item universe — 152µs
- `trending`: windowed view count ranking — 128µs
- `new_filtered`: category filter at ~5% selectivity — 7.6µs (bitmap pre-filter eliminates 95% of candidates)

### SEARCH (1M items)

```
search_1m/text_only          time:   [28.844 ms 28.934 ms 29.021 ms]
search_1m/text_filtered      time:   [ 1.8972 ms  1.9104 ms  1.9220 ms]
```

**Both SEARCH queries < 30ms.** The 100ms target is beaten by 3-50×.

- `text_only`: BM25 over 1M documents — 28.9ms (most expensive path; dominated by Tantivy posting list traversal)
- `text_filtered`: BM25 with category filter reduces candidate set — 1.9ms

### Signal Write (1M-item DB, rotating 1K entities)

```
signal_write_1m/write_rotating_1k_entities    time:   [82.033 ns 82.286 ns 82.535 ns]
```

**82 ns per write.** The 100µs target is beaten by 1,200×. DashMap hot-path write amortises to sub-100ns across 1K rotating entity IDs.

## Setup Notes

The `LazyLock<TidalDb>` pattern ensures the 1M-item database is built exactly once per bench run. Build time ~30s on the reference hardware above. The text syncer waits 3s after ingestion.

## Database Build Time

Approximately **30 seconds** on reference hardware (observed from `[scale bench] Database ready` log line).

## Analysis

tidalDB's **isolated per-op mean cost** sits well within all three
acceptance-criteria targets at 1M items. The dominant cost is SEARCH text_only at ~29ms — driven by Tantivy posting list traversal across 1M documents. The LogMergePolicy tuning (< 20 segments at steady state) keeps this below the 100ms target with headroom. **The p99 tail SLOs themselves are signed off by the open-loop `tidal-stress` ramp, not by these closed-loop means** (see the measurement contract above).

Signal writes at 82ns confirm the DashMap hot-path is not a bottleneck at this scale. The 5M-entry LRU trimming threshold (DEFAULT_MAX_SIGNAL_ENTRIES) provides ample headroom for the 100K-item signal coverage in this benchmark (~200K entries = ~218MB).