fluvilog 0.5.1

Near-real-time readings from Hamburg's water quality network (WGMN) via the public Wassergüte-Auskunft.

fluvilog

Near-real-time readings from Hamburg's water-quality network (WGMN), via the public Service Portal service.

By default it runs as a continuous service that polls and stores readings into SQLite. A one-shot mode prints the latest values as a table instead, and an optional HTTP API serves the stored data.

Requirements

• Python 3.13
• uv (recommended to make use of provided configuration and dependency lockfile)

Getting started

uv sync --extra api     # api extra is optional, needed only for `serve-api` mode

Without uv: python -m venv .venv && .venv/bin/pip install -e . (add '.[api]' for the HTTP API).

Once installed:

uv run fluvilog list    # list stations (no egress here)
uv run fluvilog once    # one-shot fetch and print
uv run fluvilog         # Continuously fetch and store (all parameters, all stations)
uv run fluvilog backfill --from 2025-01-01   # fetch and store a historical range

Bare fluvilog (no subcommand) runs collect. A single fetch cycle takes ~30–50 s — this is due to the way the data is provided, not an issue with your network or the code.

Configuration arguments are provided to select specific stations, metrics, configure fetch intervals and more. fluvilog --help to your rescue.

Configuration

Every relevant flag has a FLUVILOG_* environment-variable equivalent. A flag overrides the environment, which overrides the built-in default.

Variable	Flag	Default
FLUVILOG_DB	--db	fluvilog.db
FLUVILOG_INTERVAL	--interval	600 (seconds; accepts s/m/h suffix)
FLUVILOG_MAX_CATCHUP	--max-catchup	7 (days back-filled per poll on resume)
FLUVILOG_STATION	--station	all stations (comma-separated codes or names)
FLUVILOG_PARAMETER	--parameter	all parameters (comma-separated names or 0-based indices)
FLUVILOG_API_HOST	--host	127.0.0.1
FLUVILOG_API_PORT	--port	8000
FLUVILOG_CORS_ORIGIN	--cors-origin	none (comma-separated origins)
FLUVILOG_LOG_LEVEL	--log-level	INFO (DEBUG/INFO/WARNING/ERROR/CRITICAL)

Gaps after downtime

collect resumes from the latest stored reading, so an outage (crash, restart, deploy) shorter than --max-catchup days back-fills automatically on the next poll — writes are idempotent, so the re-fetched overlap is harmless. A single query can only cover ~10 days at full 10-minute resolution, which is why the per-poll catch-up is capped.

For longer gaps (or to seed history), run a one-shot backfill over an explicit range — it splits the range into source-sized windows and stores each idempotently, so it is safe to re-run and resumes cleanly after an interruption:

uv run fluvilog backfill --from 2025-01-01 --to 2025-03-31 --db water.db

--to defaults to today. Data goes back to each station's start. For windows before a selected station began recording, backfill omits that station from the request (and warns), so it never polls for data that cannot exist yet.

HTTP API (optional)

Install the [api] extra, then:

uv run fluvilog serve-api --db water.db --cors-origin http://localhost:5173

The API opens the database read-only per request, so it can run concurrently with collect (the single writer) under SQLite WAL. Endpoints:

• GET /api/health — service liveness probe ({"status": "ok"})
• GET /api/ready — readiness probe; 200 when the database is reachable, 503 otherwise ({"service": "ok", "db": "ok"})
• GET /api/stations — station catalogue with WGS84 coordinates
• GET /api/readings/latest?station=&parameter= — latest reading per series
• GET /api/readings?from=&to=&station=&parameter= — readings in a window (≤30 days)

Data source

The Wassergüte-Auskunft limits each request to ≤5 stations and ≤5 parameters (otherwise it silently truncates), so fetches are batched around that. Readings arrive at a 10-minute cadence and are stored idempotently, keyed on (station, parameter, timestamp), so the poll interval is decoupled from the source cadence.

Docker

Released images are published to GHCR:

docker run -v "$PWD/data:/data" ghcr.io/dmnq-f/fluvilog   # collect into /data/fluvilog.db

Or build locally:

docker build -t fluvilog .
docker run -v "$PWD/data:/data" fluvilog

To run collect and the HTTP API together against a shared database, see examples/compose.yaml:

docker compose -f examples/compose.yaml up -d   # collect (writer) + serve-api on http://localhost:8000