Logging and acquisition¶

For continuous data capture, watlowlib provides a recorder that samples a controller (or a WatlowManager of controllers) at a fixed rate, plus first-party sinks that persist the resulting Sample stream. The two halves connect through pipe(). See Design §6.

Recorder¶

record(...) is an async context manager. It schedules ticks on absolute targets (drift-free) and yields a stream of Sample batches.

import anyio
from watlowlib import WatlowManager, record

async def main() -> None:
    async with WatlowManager() as mgr:
        await mgr.add("oven_top", "/dev/ttyUSB0", address=1)
        await mgr.add("oven_bot", "/dev/ttyUSB0", address=2)
        async with record(
            mgr,
            parameters=["process_value", "setpoint"],
            rate_hz=2.0,
            duration=60.0,
        ) as stream:
            async for batch in stream:
                for sample in batch:
                    print(sample.device, sample.parameter, sample.value)

anyio.run(main)

record(...) accepts:

• A Controller for a single device.
• A WatlowManager for multi-device acquisition.
• A custom PollSource — the protocol the recorder drives for adapters and tests.

Required: parameters (list of canonical names or aliases) and rate_hz. Optional: duration (seconds; None = unbounded), overflow, buffer_size, and auto_reconnect. See Design §6 for the full scheduling model.

Watlow polls a small group of parameters per device per tick, so a recorder tick produces N×M samples — one per (device, parameter) pair that succeeded. The shape is intentionally long-format so a SQLite file can carry mixed-vendor recordings (Watlow rows union with alicatlib rows under the same schema).

Sample¶

Sample wraps a single parameter read with timing provenance.

AcquisitionSummary¶

AcquisitionSummary is the shared summary shape used by the recorder log event and returned by pipe().

Sinks¶

A sink persists Samples. The protocol is small:

class SampleSink(Protocol):
    async def open(self) -> None: ...
    async def write_many(self, samples: Sequence[Sample]) -> None: ...
    async def close(self) -> None: ...
    # plus async context-manager dunder methods

First-party sinks all conform:

Optional-extra sinks lazy-import their backend; importing watlowlib.sinks.parquet without pyarrow installed raises WatlowSinkDependencyError with the install hint, not an opaque ImportError.

pipe(stream, sink)¶

pipe(...) is the v1 acquisition glue. It reads sample batches from the recorder, buffers up to batch_size (or flush_interval seconds, whichever comes first), and calls sink.write_many to flush. On stream exhaustion it drains any remaining buffer and returns the AcquisitionSummary.

async with (
    record(mgr, parameters=["process_value"], rate_hz=2.0, duration=60.0) as stream,
    SqliteSink("run.db") as sink,
):
    summary = await pipe(stream, sink, batch_size=64, flush_interval=1.0)
print(f"{summary.samples_emitted} samples, {summary.samples_late} late")

Row schema¶

sample_to_row(sample) is the canonical flatten. Schema is locked on the first batch so mixed success / error rows don't reshape the file.

The Sample.raw payload is intentionally not in the row — bytes don't fit cleanly into CSV / JSONL / SQLite affinities. Callers that need raw consume Sample directly via InMemorySink.

The schema is intentionally compatible with alicatlib's sink schema where the columns make sense across both libraries — see examples/mixed_watlow_alicat_sqlite.py.

Backpressure¶

The recorder has a bounded internal queue. If a slow sink can't keep up, the recorder drops batches per the configured OverflowPolicy:

• OverflowPolicy.BLOCK — await the slow consumer (default). Silent drops are surprising in a data-acquisition setting, so the recorder blocks the producer rather than discarding samples. Effective sample rate drops to the consumer's drain rate; samples_late accrues once the consumer catches up.
• OverflowPolicy.DROP_NEWEST — drop the batch about to be enqueued. Counted on samples_late.
• OverflowPolicy.DROP_OLDEST — evict the oldest queued batch and enqueue the newest. Counted on samples_late.

Drops are counted on the recorder summary's samples_late and emitted as structured log events through _logging.get_logger("streaming").

SQLite specifics¶

SqliteSink defaults to journal mode WAL, synchronous=NORMAL, and one BEGIN IMMEDIATE … COMMIT per write_many call. Schema is locked on the first batch — later samples carrying an unknown column are dropped with a one-shot WARN log rather than reshaping the file.

SELECT device, parameter, AVG(value) FROM samples
GROUP BY device, parameter
ORDER BY device, parameter;

The same query works on a file that mixes Watlow and Alicat rows.

See also¶

• Streaming — solo Controller and manager recordings.
• Controllers — Controller and Reading shape.
• Sinks API — full reference.
• Testing — FakeTransport for sink unit tests.
• Design §6 — scheduling, overflow, schema rationale.