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.
Metadata
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