Northern-Thailand-Ping-River-Monitor/docs/RUST_REWRITE.md

A Rust rewrite: assessment

Status: evaluation, nothing decided. Spike: spikes/rust-forecast.

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.