Running a server

import asyncio
from datap_rs.datapress import DataPress, DataPressConfig, DatasetConfig

async def main() -> None:
    ds = DatasetConfig(
        name="accidents",
        source="data/accidents.parquet",
        format="parquet",
        mode="auto",
        description="US accidents 2016-2023",
    )
    cfg = DataPressConfig(
        backend="datafusion",
        listen="0.0.0.0",
        port=8000,
        workers=8,
    )
    server = DataPress(cfg, datasets=[ds])
    await server.run()              # blocks until SIGINT / SIGTERM

if __name__ == "__main__":
    asyncio.run(main())

DataPress is constructed from a DataPressConfig and a list of DatasetConfig. await server.run() boots the actix runtime, mounts every route, and blocks the calling coroutine until a shutdown signal arrives.

Lifecycle

1. Workers spin up — number controlled by workers (defaults to one per CPU).
2. Each dataset is loaded by the chosen backend (lazy datasets only register their schema).
3. The bind address and full route table are logged.
4. The coroutine blocks on the actix server future.
5. On SIGTERM / SIGINT (e.g. Ctrl+C) the listening socket closes immediately; in-flight requests get up to shutdown_timeout_secs seconds to drain.
6. await server.run() returns. Any cleanup after it (logging, metrics flush, …) runs normally.

Behind a reverse proxy

DataPressConfig(backend="datafusion", port=8000, prefix="/datapress")
# → GET /datapress/api/v1/datasets, GET /datapress/health, ...

prefix must start with / and not end with /. The unprefixed probes — /healthz, /readyz, /version — stay at the bare host root.

In a Jupyter notebook

import asyncio, nest_asyncio
nest_asyncio.apply()                  # allow re-entrant event loops

server_task = asyncio.create_task(DataPress(cfg, [ds]).run())
# ... use DataPressClient against http://127.0.0.1:8000 ...
server_task.cancel()

See Examples.