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`

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

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chalk-remote-call-python 1.9.0

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

Architecture

Development

Setup

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BLAKE2b-256	`bad2089c0a41db3574b5dd24edee9c00a499173ba4859a6df03c2131c14f2537`

Algorithm	Hash digest
SHA256	`fe53fece6dba237fd0bf27f17ed8f8086bce1a8c39a2a267f696c5ae8fc872ce`
MD5	`be63557ca813ff89dc7ce695358fe125`
BLAKE2b-256	`af03a760f364e4842b742c4f32330e71335eaaea1dc65ea16695bf8768477a33`

Algorithm	Hash digest
SHA256	`f3c2f55826590fa89896e59ac6803a2a1108af5f090d930835e531fd12b605cf`
MD5	`88a2d250b61d82d6d42b466eb21f7a0b`
BLAKE2b-256	`428c2fe02ce3f2297ae385e7928a798bb3f0b073206922805f045b33867d7e75`