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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Driver- and worker-side setup for distributing DataFusion expressions.
When a :class:`Expr` is shipped to a worker process (e.g. through
:func:`multiprocessing.Pool` or a Ray actor), the worker reconstructs the
expression against a :class:`SessionContext`. If the expression references
UDFs imported via the FFI capsule protocol — or any UDF the worker would
otherwise resolve from its registered functions rather than from inside
the shipped expression — install a configured :class:`SessionContext`
once per worker:
.. code-block:: python
from datafusion import SessionContext
from datafusion.ipc import set_worker_ctx
def init_worker():
ctx = SessionContext()
ctx.register_udaf(my_ffi_aggregate)
set_worker_ctx(ctx)
Built-in functions and Python UDFs (scalar, aggregate, window) travel
inside the shipped expression itself and do not need pre-registration
on the worker.
.. note:: Serialization model
Expressions containing Python UDFs (scalar, aggregate, window) are
serialized using :mod:`cloudpickle`. The callable itself travels
**by value** (bytecode and closure cells inlined), but any names the
callable resolves via ``import`` are captured **by reference** and
must be importable on the receiving worker.
The serialized payload is stamped with the sender's Python
``(major, minor)`` version. Loading on a different minor version
raises :class:`ValueError` with an actionable message — cloudpickle
payloads are not portable across Python minor versions. See
:meth:`datafusion.Expr.to_bytes` for examples of what travels by
value vs. by reference.
On the driver side, call :func:`set_sender_ctx` to control how
:func:`pickle.dumps` encodes expressions — for example, to apply
:meth:`SessionContext.with_python_udf_inlining` to every pickled
expression on this thread:
>>> import pickle
>>> from datafusion import SessionContext, col, lit
>>> from datafusion.ipc import clear_sender_ctx, set_sender_ctx
>>> driver_ctx = SessionContext().with_python_udf_inlining(enabled=False)
>>> set_sender_ctx(driver_ctx)
>>> try:
... blob = pickle.dumps(col("a") + lit(1))
... finally:
... clear_sender_ctx()
>>> isinstance(blob, bytes)
True
Without a sender context the default codec is used (Python UDF
inlining on). The sender context only affects pickle / ``to_bytes``
encoding; explicit ``expr.to_bytes(ctx)`` calls still use the supplied
``ctx``.
The thread-local sender context holds a strong reference to the
installed :class:`SessionContext` until :func:`clear_sender_ctx` is
called or the thread exits. Long-running driver threads that install a sender
context once and never clear it will retain that session for the
lifetime of the thread; pair :func:`set_sender_ctx` with
:func:`clear_sender_ctx` (e.g. in a ``try``/``finally``) when the
sender context is only needed for a bounded scope.
"""
from __future__ import annotations
import threading
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from datafusion.context import SessionContext
__all__ = [
"clear_sender_ctx",
"clear_worker_ctx",
"get_sender_ctx",
"get_worker_ctx",
"set_sender_ctx",
"set_worker_ctx",
]
_local = threading.local()
def set_worker_ctx(ctx: SessionContext) -> None:
"""Install this worker's :class:`SessionContext` for shipped expressions.
Call once per worker — typically from a ``multiprocessing.Pool``
initializer or a Ray actor ``__init__``. Idempotent: overwrites any
previous value. Stored in a thread-local slot, so each thread within a
worker may install its own context independently.
Examples:
>>> from datafusion import SessionContext
>>> from datafusion.ipc import set_worker_ctx, get_worker_ctx, clear_worker_ctx
>>> set_worker_ctx(SessionContext())
>>> get_worker_ctx() is not None
True
>>> clear_worker_ctx()
"""
_local.ctx = ctx
def clear_worker_ctx() -> None:
"""Remove this worker's installed :class:`SessionContext`.
After clearing, expressions reconstructed in this worker fall back to
the global :class:`SessionContext` — adequate for built-ins and Python
UDFs (scalar, aggregate, window), but anything imported via the FFI
capsule protocol must be registered on the global context to resolve.
Examples:
>>> from datafusion import SessionContext
>>> from datafusion.ipc import set_worker_ctx, clear_worker_ctx, get_worker_ctx
>>> set_worker_ctx(SessionContext())
>>> clear_worker_ctx()
>>> get_worker_ctx() is None
True
"""
if hasattr(_local, "ctx"):
del _local.ctx
def get_worker_ctx() -> SessionContext | None:
"""Return this worker's installed :class:`SessionContext`, or ``None``.
Examples:
>>> from datafusion.ipc import get_worker_ctx, clear_worker_ctx
>>> clear_worker_ctx()
>>> get_worker_ctx() is None
True
"""
return getattr(_local, "ctx", None)
def set_sender_ctx(ctx: SessionContext) -> None:
"""Install this driver's :class:`SessionContext` for outbound pickles.
Controls how :func:`pickle.dumps` encodes :class:`Expr` instances on
this thread. The most useful application is propagating a session
configured with
:meth:`SessionContext.with_python_udf_inlining` so the toggle takes
effect through pickle (which otherwise calls
:meth:`Expr.to_bytes` with no context and uses the default codec).
Idempotent: overwrites any previous value. Stored in a thread-local
slot, so worker threads on the driver may install their own contexts.
Does not affect :meth:`Expr.to_bytes` calls that pass an explicit
``ctx`` — those continue to use the supplied context.
Examples:
>>> from datafusion import SessionContext
>>> from datafusion.ipc import (
... clear_sender_ctx,
... get_sender_ctx,
... set_sender_ctx,
... )
>>> driver = SessionContext().with_python_udf_inlining(enabled=False)
>>> set_sender_ctx(driver)
>>> get_sender_ctx() is driver
True
>>> clear_sender_ctx()
"""
_local.sender_ctx = ctx
def clear_sender_ctx() -> None:
"""Remove this driver's installed sender :class:`SessionContext`.
After clearing, pickled expressions fall back to the default codec
(Python UDF inlining on).
Examples:
>>> from datafusion import SessionContext
>>> from datafusion.ipc import (
... set_sender_ctx, clear_sender_ctx, get_sender_ctx,
... )
>>> set_sender_ctx(SessionContext())
>>> clear_sender_ctx()
>>> get_sender_ctx() is None
True
"""
if hasattr(_local, "sender_ctx"):
del _local.sender_ctx
def get_sender_ctx() -> SessionContext | None:
"""Return this driver's installed sender :class:`SessionContext`, or ``None``.
Examples:
>>> from datafusion.ipc import get_sender_ctx, clear_sender_ctx
>>> clear_sender_ctx()
>>> get_sender_ctx() is None
True
"""
return getattr(_local, "sender_ctx", None)
def _resolve_ctx(
explicit_ctx: SessionContext | None = None,
) -> SessionContext:
"""Resolve a context for Expr reconstruction.
Priority: explicit argument > worker context > global context.
Falling back to the global :class:`SessionContext` (instead of a
freshly constructed one) preserves any registrations the user has
installed on it. It carries no extension codecs, though, so a worker
that must decode payloads written by one has to install a context
carrying them via :func:`set_worker_ctx`.
Examples:
>>> from datafusion import SessionContext
>>> from datafusion.ipc import _resolve_ctx, clear_worker_ctx
>>> clear_worker_ctx()
>>> isinstance(_resolve_ctx(), SessionContext)
True
>>> ctx = SessionContext()
>>> _resolve_ctx(ctx) is ctx
True
"""
if explicit_ctx is not None:
return explicit_ctx
worker = get_worker_ctx()
if worker is not None:
return worker
# Lazy import: `datafusion/__init__.py` imports `datafusion.ipc`
# before `datafusion.context`, so a module-top import would force
# `datafusion.context` to load mid-init of `datafusion.ipc`. The
# cycle is benign today (context.py only pulls expr.py at module
# scope, neither pulls ipc.py back), but a single new import in
# context.py's transitive deps could turn it into a real cycle.
# Deferring keeps `datafusion.ipc` import-order-independent.
from datafusion.context import SessionContext # noqa: PLC0415
return SessionContext.global_ctx()