# A Rust rewrite: assessment

Status: evaluation, nothing decided. Spike: [spikes/rust-forecast](../spikes/rust-forecast/README.md).

## Short version

- **Feasible.** The risky part, serving the scikit-learn forecast from Rust with
  no train/serve skew, works: the spike reproduces the pandas feature matrix
  and the HistGradientBoosting regression output bit for bit (classifier heads
  to the last ulp).
- **Training stays Python.** There is no Rust HGB that trains like
  scikit-learn, and the evaluation harness (`scripts/evaluate_variants.py`) is
  where model work happens. A "Rust rewrite" means a Rust service (collectors,
  API, forecast serving, ntfy) plus a Python offline job (monthly retrain and
  evaluation) that exports models for Rust.
- **Performance is not a reason.** The forecast takes 158 ms per station in Python
  and 0.25 ms in Rust, but it runs once an hour. Load tests showed the site
  is bandwidth-bound once caches are hit.
- **Real reasons, if any:** one process instead of leader-elected uvicorn workers
  (the root of per-worker state bugs like `/scraping/status`), a fraction of the
  memory (324 MB today), typed parsing and SQL, one deployable binary, and the
  same stack as proplan (axum + sqlx).
- **Recommendation:** no big-bang rewrite. If Rust is wanted, start with the
  collector + notifier daemon (phase 1 below), which stands alone and can be
  abandoned without loss. The cheap Python alternative is at the end.

## What exists (Python, `src/`)

| Area | LOC | Fate in Rust |
| --- | ---: | --- |
| Collectors: RID hourly, HII/ThaiWater, RID dams, Open-Meteo | 2,500 | port; typed serde payloads |
| Web API (20 routes), schemas, health, metrics | 2,300 | port with identical JSON (golden-file tests) |
| DB adapters: Postgres, SQLite, MySQL, InfluxDB, VictoriaMetrics | 1,300 | Postgres only via sqlx, existing 13 tables, no data migration |
| ntfy notifications; Matrix alerting (CLI path) | 1,050 | port ntfy; Matrix only if still used |
| ML serving: predict, features, rain/dam/HII fill | ~1,300 | port; parity-tested (spike) |
| ML training + evaluation | ~1,700 | **stays Python** |
| CLI (`main.py`) | 650 | small clap CLI (`fill-gaps`, test cycle) |
| `static/dashboard.html` (164 KB) | | unchanged, served as a static file |
| tests | 3,900 | Rust unit tests + parity fixtures + API golden files |

## Target shape

One binary, one process (tokio):

- tasks: RID / HII / dams / rain collectors on their schedules; forecast
  precompute when new readings land; ntfy notifier. Shared state is an
  `Arc<RwLock<..>>`, so there's no leader election, no status hand-off file and
  no executor sizing.
- axum + tower-http: same routes and JSON, compression (gzip/brotli also covers
  the 257 KB river GeoJSON), static dashboard, CORS; `utoipa` if `/docs` stays.
- sqlx against the existing schema. Python and Rust can run against the same
  database during migration.
- models: `scripts/retrain.sh` also exports each bundle to JSON (spike format,
  ~4.9 MB per station vs 6.2 MB joblib). Rust loads them at start and reloads on
  change. A bundle whose `feature_names` Rust cannot build is refused and the
  station falls back to the heuristic, as Python does today.

## The permanent cost: the Python/Rust model contract

Every feature added in `features.py` must be ported to Rust in the same change,
or the new bundle is refused. That has to be enforced by CI: Python writes a parity
fixture per station (as `export_fixture.py` does), and a Rust test asserts
bit-identical features and regression output. The spike found two traps that
silently flip tree branches (HGB thresholds often equal real data values):
serde_json's default float parsing (1 ulp off; enable `float_roundtrip`) and
pandas' Kahan-compensated rolling sums/means (replicated in `rolling_kahan()`).
This slows model iteration, which is the most active part of the repo.

## Benefits and costs

| For | Against |
| --- | --- |
| Single process: no leader election, no per-worker state | Two languages forever; parity CI is mandatory |
| Memory ~324 MB -> est. 60-80 MB (mostly tree arrays; less with u32/u16 nodes) | Weeks of work with little user-visible change |
| Typed RID/HII parsing, compile-checked SQL | Re-introduces bugs into recently hardened code (tz, alerts, security) |
| One static binary; no venv on the server for the API | Server still needs Python + sklearn for the monthly retrain |
| Same stack as proplan | Feature work = Python + Rust change every time |

## Plan, if pursued (each phase ships on its own)

0. **Spike: done.** P.1 features + HGB inference, bit-identical.
1. **Collector + notifier daemon** (~1.5k LOC): RID (the fixed semantics: store
   late readings, retry while behind, re-fetch yesterday's holes), HII, dams,
   Open-Meteo, ntfy, `notification_state`. The Python API runs with collection
   off and becomes a stateless reader. Shadow one week first: compare stored
   hours against RID upstream.
2. **Forecast crate** (~2k LOC): all 15 stations, dam/HII/rain inputs, heuristic
   fallback, probabilities and stages, `forecast_history`; parity CI for every
   station on a flood window and a dry window.
3. **Read API** (~2k LOC): routes recorded from the Python API as golden files,
   then a Caddy switch. Python keeps only training/evaluation.
4. **Cleanup:** drop the multi-backend adapters, demo databases and the
   Python web stack.

Open questions before phase 1: is Matrix alerting still used? Must `/docs`
(OpenAPI UI) stay? Is SQLite dev mode needed? Where does the monthly retrain
run if the server stops having a Python environment for the API?

## Cheaper alternative (stay Python)

Most of the operational gain comes from taking collection out of the web
workers: run the collector as its own systemd unit (one process) and the
uvicorn workers as read-only API. That's about a day of work. It removes leader
election and per-worker state, but doesn't bring the memory, typing or
single-binary benefits.