chalk-remote-call-python

A Python runtime interface for Chalk's RemoteCallService. This package provides a gRPC server backed by a Rust implementation (tonic + PyO3) that lets you define a handler(event, context) function to process incoming Arrow-serialized requests.

The server receives Arrow IPC record batches over a bidirectional gRPC stream, transforms them into a Python dict, invokes your handler, and streams the results back as Arrow IPC.

Requirements

Python >= 3.10
Rust toolchain (for building the native extension)
pyarrow >= 14.0.0

Usage

1. Write a handler

Create a Python module with a handler function:

# my_handler.py
import pyarrow as pa
import pyarrow.compute as pc


def on_startup():
    """Optional -- runs once before the server starts accepting requests."""
    print("Loading model weights...")


def handler(event: dict[str, pa.Array], context: dict) -> pa.Array:
    """Called for each incoming request.

    Args:
        event: dict mapping column names to pyarrow.Array values.
        context: dict with request metadata

    Returns:
        A pyarrow.Array, pyarrow.RecordBatch, pyarrow.Table, list, dict, or scalar.
        The framework auto-converts the result to Arrow IPC for the response.
    """
    return pc.multiply(event["x"], event["y"])


def on_shutdown():
    """Optional -- runs once after the server stops accepting requests."""
    print("Releasing resources...")

2. Start the server

chalk-remote-call --handler my_handler.handler

Or via python -m:

python -m chalk_remote_call --handler my_handler.handler

CLI options

Flag	Env var	Default	Description
`--handler`		(required)	Dotted path to handler function
`--port`	`CHALK_REMOTE_CALL_PORT`	`6666`	Port to listen on
`--host`	`CHALK_REMOTE_CALL_HOST`	`[::]`	Host to bind to
`--workers`	`CHALK_REMOTE_CALL_WORKERS`	`10`	Tokio runtime worker threads
`--on-startup`			Dotted path to a startup function
`--on-shutdown`			Dotted path to a shutdown function
`--log-level`		`INFO`	`DEBUG`, `INFO`, `WARNING`, or `ERROR`

The CLI writes Python logs to stderr as one JSON object per line, with separate timestamp, severity, logger, and message fields. Exceptions include an exception.stacktrace field. Calls to serve() from Python retain the embedding application's logging configuration.

Missing optional StatsD endpoints or a metrics-bus topic do not emit warnings. Failures initializing configured metrics sinks still warn. Routine customer StatsD configuration messages are emitted at DEBUG by the shared metrics library; set RUST_LOG=info,chalk_metrics::metrics::config=debug to include them.

Environment variables

Variable	Description
`CHALK_INPUT_ARGS`	Comma-separated list of column names (e.g. `x,y,z`). Renames incoming RecordBatch columns by index. If unset, the original column names are used. Reserved columns (below) are held out of this mapping and keep their own name.
`CHALK_FNQ_QUEUE_PROTOCOL`	Self-consumer work transport: `list_v1` (default/backward compatible) or `stream_v1`. Deployment tooling sets this from immutable scaling-group revision metadata.

Reserved columns

A caller may attach framework side-channel data to a request as an extra column. Reserved columns are matched by name, are optional on any given request, and are excluded from the positional CHALK_INPUT_ARGS mapping — so CHALK_INPUT_ARGS always lists exactly the handler's declared arguments, whether or not the caller sent one.

Column	Meaning
`__chalk_row_metadata__`	Per-row binary blob supplied by the caller (Chalk's engine sets it from `catalog_call(..., row_metadata => ...)`). Handlers read it via `chalkcompute.get_row_metadata()`.

3. Docker image

FROM python:3.11-slim

WORKDIR /app

RUN pip install chalk-remote-call-python

# Copy your handler code
COPY my_handler.py /app

EXPOSE 6666

ENTRYPOINT ["chalk-remote-call"]
CMD ["--handler", "handler.handler"]

Build and run:

docker build -t my-chalk-handler .
docker run -p 6666:6666 -e CHALK_INPUT_ARGS="x,y" my-chalk-handler

Programmatic usage

You can also start the server from Python:

from chalk_remote_call import serve

def handler(event, context):
    return list(event.values())[0]

serve(handler=handler, port=8080)

Local Testing

Use grpcurl to verify the server is running:

# Check health
grpcurl -plaintext localhost:6666 grpc.health.v1.Health/Check

# List services via reflection
grpcurl -plaintext localhost:6666 list

Architecture

The server uses a Rust backend via PyO3:

tonic — gRPC server with health checking and reflection
prost — protobuf message handling
arrow-rs — Arrow IPC validation
PyO3 — FFI bridge to call Python handler functions

The Rust server runs on a tokio async runtime. When a request arrives, Arrow IPC bytes are passed to Python via PyO3 (spawn_blocking + GIL acquisition), where the handler is invoked. Results are passed back as IPC bytes.

Development

Setup

cd chalk-remote-call-python
uv venv --python 3.11
uv pip install -e ".[dev]"

Running tests

pytest tests/ -v

Rust code

Rust source lives in chalk-remote-call-rs/:

To check the Rust code independently:

PYO3_PYTHON=python3 cargo check --manifest-path chalk-remote-call-rs/chalk-remote-call-server/Cargo.toml

Remote exception transport

Handler failures retain their original exception type, message, traceback, exception chains, and exception groups. Runtime dispatch frames are excluded from the user traceback; full diagnostics are retained in server logs.

All call modes carry a JSON payload with chalk_remote_error: 1 and an exception object containing the ChalkException fields kind, message, stacktrace, and internal_stacktrace. Direct calls carry the payload as the unescaped gRPC status message; queued calls carry it in the error message. The local async service additionally populates ChalkError.exception. This permits existing queue transports to forward structured errors without a protobuf migration. Clients must decode the versioned payload, not parse gRPC debug text or classify user errors by their message. Unknown versions remain opaque.

Payloads are bounded for gRPC header limits. Truncation is marked explicitly, with complete tracebacks kept in server logs. Exception objects and locals are never pickled or sent. Chalk SDK-generated handlers identify their dispatch frames, and notebook source maps identify the deployed snapshot's lines.

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chalk-remote-call-python 1.9.2

chalk-remote-call-python

Requirements

Usage

1. Write a handler

2. Start the server

CLI options

Environment variables

Reserved columns

3. Docker image

Programmatic usage

Local Testing

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Algorithm	Hash digest
SHA256	`f66af4b8d43d288aa41d0979fcf0e7bd4cc2aed1ff1f4b75741575a5f9e34966`
MD5	`63040b7df6b8eda7862c1439a5b10431`
BLAKE2b-256	`33b0f6e5cd7984db6b97dcf4e4dfaac111df1bdc71f6336665ebf12add34e529`

Algorithm	Hash digest
SHA256	`13cbb52ff6e603a193c5a88c9093eb0699093039300bedb1e6a61fe601352142`
MD5	`600138f84b58b61dea30feafcf03a219`
BLAKE2b-256	`d0c705701cf07c3976419515a512954f249802f84e35c51cb31fd8a1020d18d6`

Algorithm	Hash digest
SHA256	`0f929f705b798f3b7d213d38172f0482fe8639184009286f6e7e9504cc2d391a`
MD5	`aebc44e09507924eefb91e8337ce3219`
BLAKE2b-256	`ace6032d8c26efbe730b51dd3ddae9b740ab7db3f7222c7f405fad507fac11ee`

Algorithm	Hash digest
SHA256	`a2785aed5b694acf495f1be4f3676f0fa58798b9324acb0b8b6004e07da3bdd9`
MD5	`170e5fc1da6c3dafef29c3a52841a1d6`
BLAKE2b-256	`5423f7a2cc2180032a32f080eee6fdd76d1e036e8f98ee2fc5d9c45edc8441e4`

Algorithm	Hash digest
SHA256	`d96ac40af60e52f45716d0ab8bee1e462368d70b0e014691dbbe2a8ed7f7a855`
MD5	`cd14bd243345fb50503822607201b629`
BLAKE2b-256	`818c8ff04b74a78e4d369251c34827e64ef2c303050ccf2e9172a49f9f4f5470`