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 1.99.0 (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, which the log collector uses for the traceback. CLI logging is configured before importing runtime dependencies. Import failures, invalid environment configuration, and exceptions escaping startup or the server emit a single ERROR record with the full traceback and exit with status 1. Calls to serve() from Python retain the embedding application's logging configuration.

On Linux, server shutdown drains buffered function-call metrics before the runtime exits, including when the server returns an error. Draining waits up to 10 seconds; a timeout or publisher failure is logged as a warning. Routine drain messages are logged at DEBUG; set RUST_LOG=info,_native::metrics=debug to include them.

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_CONSUMER_ENDPOINT Opt into DP API FnqConsumerService consumption (HTTP or HTTPS gRPC endpoint). Unset preserves direct Redis consumption. API errors never fall back to Redis.
CHALK_FUNCTION_QUEUE_TOKEN_FILE Controller-mounted working JWT, reopened per RPC to observe rotation. Defaults to /var/run/secrets/chalk-function-queue/identity-token. The shim does not exchange credentials.
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)

Invocation context

Each Python bridge invocation runs in a fresh contextvars.Context. Handler changes to context variables stay within that invocation, including across awaits and generator iteration, and are discarded on return or error. Incoming request metadata still supplies the tracing parent. A coalesced batch has one invocation context for the whole batch.

Workers retain their Python thread state for compatibility with native extensions. This context boundary does not reset process globals or threading.local() storage, whose lifetime follows the worker thread.

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

The Linux metrics shutdown regression tests use an in-memory bus publisher:

PYO3_PYTHON=python3 cargo test --manifest-path chalk-remote-call-rs/chalk-remote-call-server/Cargo.toml --no-default-features --lib metrics::tests

--no-default-features links Python into the test executable; wheel builds retain the default extension-module feature.

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, along with callers above the handler dispatch boundary. Handler frames and their downstream calls remain visible (except for Chalk SDK and marked generated dispatch frames); full diagnostics are retained in server logs and internal_stacktrace.

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.

Fnq integration test

On Linux, build the native extension and the paired chalk-private test harness:

# chalk-private, inside the Nix dev shell
bazel build //rust-crates/chalk/dataplane-api-server:fnq_test_server

# chalk-remote-call-python
uv pip install -e ".[dev]" redis PyJWT cryptography
FNQ_TEST_SERVER=/path/to/chalk-private/bazel-bin/rust-crates/chalk/dataplane-api-server/fnq_test_server \
REDIS_URL=redis://127.0.0.1:6379 .venv/bin/pytest tests/test_fnq_api_e2e.py -v

Use an isolated Redis instance. The tests create temporary signing keys and start the production JWT layer and Fnq service, then exercise real native-shim execution, lease renewal, result writes, token rotation, and environment isolation. These tests skip unless FNQ_TEST_SERVER and REDIS_URL are set. Deploy the service before setting CHALK_FNQ_CONSUMER_ENDPOINT on a workload.

Project links

Key dates

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  • Released:
Newer release available (1.10.13)

Owner

PyPI data
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Credits

Author: Chalk AI, Inc.

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Data verified by PyPI on Oct 25, 2026
Data provided by the project maintainers and verified by PyPI at the time of upload.

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Requires

Python <3.16, >=3.10

Provides Extra

dev tracing

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Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.10.13)

Owner

PyPI data
Data sourced directly from PyPI's database.

Credits

Author: Chalk AI, Inc.

Provenance

Data verified by PyPI on Oct 25, 2026
Data provided by the project maintainers and verified by PyPI at the time of upload.

Full provenance

All 43 files in this release have provenance metadata.

View provenance details

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Python <3.16, >=3.10

Provides Extra

dev tracing

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Release files for chalk-remote-call-python 1.10.10

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chalk_remote_call_python-1.10.10-cp313-cp313-musllinux_1_2_x86_64.whl 10.2 MB With provenance CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details for chalk_remote_call_python-1.10.10-cp313-cp313-musllinux_1_2_x86_64.whl
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chalk_remote_call_python-1.10.10-cp313-cp313-manylinux_2_28_x86_64.whl 9.1 MB With provenance CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64 Details for chalk_remote_call_python-1.10.10-cp313-cp313-manylinux_2_28_x86_64.whl
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chalk_remote_call_python-1.10.10-cp312-cp312-manylinux_2_28_x86_64.whl 9.1 MB With provenance CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64 Details for chalk_remote_call_python-1.10.10-cp312-cp312-manylinux_2_28_x86_64.whl
chalk_remote_call_python-1.10.10-cp312-cp312-manylinux_2_28_aarch64.whl 10.3 MB With provenance CPython 3.12 CPython 3.12 Linux glibc 2.28+ ARM64 Details for chalk_remote_call_python-1.10.10-cp312-cp312-manylinux_2_28_aarch64.whl
chalk_remote_call_python-1.10.10-cp312-cp312-macosx_11_0_arm64.whl 2.7 MB With provenance CPython 3.12 CPython 3.12 macOS 11.0+ ARM64 Details for chalk_remote_call_python-1.10.10-cp312-cp312-macosx_11_0_arm64.whl
chalk_remote_call_python-1.10.10-cp312-cp312-macosx_10_13_x86_64.whl 2.8 MB With provenance CPython 3.12 CPython 3.12 macOS 10.13+ x86-64 Details for chalk_remote_call_python-1.10.10-cp312-cp312-macosx_10_13_x86_64.whl
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chalk_remote_call_python-1.10.10-cp311-cp311-musllinux_1_2_x86_64.whl 10.2 MB With provenance CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details for chalk_remote_call_python-1.10.10-cp311-cp311-musllinux_1_2_x86_64.whl
chalk_remote_call_python-1.10.10-cp311-cp311-musllinux_1_2_aarch64.whl 10.4 MB With provenance CPython 3.11 CPython 3.11 Linux musl 1.2+ ARM64 Details for chalk_remote_call_python-1.10.10-cp311-cp311-musllinux_1_2_aarch64.whl
chalk_remote_call_python-1.10.10-cp311-cp311-manylinux_2_28_x86_64.whl 9.1 MB With provenance CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64 Details for chalk_remote_call_python-1.10.10-cp311-cp311-manylinux_2_28_x86_64.whl
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chalk_remote_call_python-1.10.10-cp311-cp311-macosx_10_13_x86_64.whl 2.8 MB With provenance CPython 3.11 CPython 3.11 macOS 10.13+ x86-64 Details for chalk_remote_call_python-1.10.10-cp311-cp311-macosx_10_13_x86_64.whl
chalk_remote_call_python-1.10.10-cp310-cp310-win_amd64.whl 2.6 MB With provenance CPython 3.10 CPython 3.10 Windows x86-64 Details for chalk_remote_call_python-1.10.10-cp310-cp310-win_amd64.whl
chalk_remote_call_python-1.10.10-cp310-cp310-musllinux_1_2_x86_64.whl 10.2 MB With provenance CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-64 Details for chalk_remote_call_python-1.10.10-cp310-cp310-musllinux_1_2_x86_64.whl
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1.10.13

43 release files Full provenance

1.10.12

43 release files Full provenance

1.10.11

43 release files Full provenance

This release

1.10.10 This release

43 release files Full provenance

1.10.9

43 release files Full provenance

1.10.8

43 release files Full provenance

1.10.7

43 release files Full provenance

1.10.6

43 release files Full provenance

1.10.5

43 release files Full provenance

1.10.4

36 release files Full provenance

1.10.3

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1.10.2

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1.10.1

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1.10.0

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1.9.2

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1.9.1

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1.9.0

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1.8.7

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1.8.6

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1.8.5

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1.8.3

26 release files Full provenance

1.8.2

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1.8.1

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1.8.0

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1.7.2

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1.7.1

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1.7.0

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1.6.3

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1.6.2

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1.6.1

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1.6.0

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1.5.1

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1.5.0

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1.4.0

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1.3.0

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1.2.0

21 release files Full provenance

1.1.2

21 release files Full provenance

0.0.0

21 release files Full provenance

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