From cc1bff75a288818f24a1121db88dcaa4bb0b9045 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 15:10:35 -0400 Subject: [PATCH 01/57] feat: pickle support for Expr via PythonLogicalCodec inline encoding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Builds on the codec consistency work in feat/proto-codecs. Python scalar UDFs are cloudpickled inline into the proto `fun_definition` field by PythonLogicalCodec / PythonPhysicalCodec, so a pickled Expr that references a Python `udf()` reconstructs on the receiver with no pre-registration. UDAFs, UDWFs, and FFI-imported UDFs still resolve through the receiver's session. Rust: * `PythonFunctionScalarUDF` regains the `func()` / `input_fields()` / `return_field()` / `volatility()` / `from_parts()` accessors the codec needs. * `crates/core/src/codec.rs` adds shared `try_encode_python_scalar_udf` / `try_decode_python_scalar_udf` helpers built on cloudpickle + pyarrow IPC for the input schema. Both `PythonLogicalCodec.try_encode_udf` and `PythonPhysicalCodec.try_encode_udf` consult the helper first and fall back to `inner` for non-Python UDFs (and the receiver's function registry on decode if the prefix does not match). Python: * `datafusion.ipc` module: thread-local `set_worker_ctx` / `clear_worker_ctx` / `get_worker_ctx` for installing a receiver `SessionContext` on a worker process. `_resolve_ctx` returns explicit > worker > fresh. * `Expr.__reduce__` returns `(Expr._reconstruct, (self.to_bytes(),))`. `_reconstruct` calls `Expr.from_bytes(buf, ctx=None)` which consults the worker context. * `Expr.from_bytes` signature switches to `(buf, ctx=None)` (was `(ctx, buf)`); no callers in main, only PR1 tests which are updated. * `datafusion.ipc` exported from the top-level package. Dependencies: * `cloudpickle>=2.0` added as a runtime dep. Lazy-imported on the encode / decode hot paths — users who never pickle a plan or expression pay only the install footprint, not import-time cost. * ruff `S301` added to the test-suite + examples ignore lists (legitimate `pickle.loads` use). Tests: * `test_pickle_expr.py` — 11 cases covering built-in expr pickle, scalar UDF self-contained blobs, closure-capturing UDFs, worker ctx lifecycle, thread-local isolation. * `test_pickle_multiprocessing.py` + `_pickle_multiprocessing_helpers.py` — parametrized over `fork`/`forkserver`/`spawn` start methods. 9 cases. Auto-skip when the sandbox blocks semaphore creation; CI runs the full matrix. * `test_expr.py` — existing `from_bytes` tests updated to new signature. 1088 root tests pass (up from 1077), 13 skipped (up from 4, the new mp cases skip locally under sandboxed semaphores). Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 149 +- crates/core/src/udf.rs | 38 +- pyproject.toml | 8 + python/datafusion/__init__.py | 3 +- python/datafusion/expr.py | 48 +- python/datafusion/ipc.py | 98 + .../tests/_pickle_multiprocessing_helpers.py | 89 + python/tests/test_expr.py | 4 +- python/tests/test_pickle_expr.py | 164 ++ python/tests/test_pickle_multiprocessing.py | 125 ++ uv.lock | 1704 +++++++++-------- 11 files changed, 1565 insertions(+), 865 deletions(-) create mode 100644 python/datafusion/ipc.py create mode 100644 python/tests/_pickle_multiprocessing_helpers.py create mode 100644 python/tests/test_pickle_expr.py create mode 100644 python/tests/test_pickle_multiprocessing.py diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 088532df2..e6c237ef6 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -77,22 +77,30 @@ use std::sync::Arc; -use arrow::datatypes::SchemaRef; +use arrow::datatypes::{Field, Schema, SchemaRef}; +use arrow::pyarrow::ToPyArrow; +use datafusion::arrow::pyarrow::FromPyArrow; use datafusion::common::{Result, TableReference}; use datafusion::datasource::TableProvider; use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; -use datafusion::logical_expr::{AggregateUDF, Extension, LogicalPlan, ScalarUDF, WindowUDF}; +use datafusion::logical_expr::{ + AggregateUDF, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, WindowUDF, +}; use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; use datafusion_proto::logical_plan::{DefaultLogicalExtensionCodec, LogicalExtensionCodec}; use datafusion_proto::physical_plan::{DefaultPhysicalExtensionCodec, PhysicalExtensionCodec}; +use pyo3::BoundObject; +use pyo3::prelude::*; +use pyo3::types::{PyBytes, PyTuple}; + +use crate::udf::PythonFunctionScalarUDF; /// Wire-format prefix that tags a `fun_definition` payload as an /// inlined Python scalar UDF (cloudpickled tuple of name, callable, /// input schema, return field, volatility). Defined once here so /// the encoder and decoder cannot drift. -#[allow(dead_code)] pub(crate) const PY_SCALAR_UDF_MAGIC: &[u8] = b"DFPYUDF1"; /// `LogicalExtensionCodec` parked on every `SessionContext`. Holds @@ -177,10 +185,16 @@ impl LogicalExtensionCodec for PythonLogicalCodec { } fn try_encode_udf(&self, node: &ScalarUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_scalar_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udf(node, buf) } fn try_decode_udf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udf) = try_decode_python_scalar_udf(buf)? { + return Ok(udf); + } self.inner.try_decode_udf(name, buf) } @@ -249,10 +263,16 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { } fn try_encode_udf(&self, node: &ScalarUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_scalar_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udf(node, buf) } fn try_decode_udf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udf) = try_decode_python_scalar_udf(buf)? { + return Ok(udf); + } self.inner.try_decode_udf(name, buf) } @@ -284,3 +304,126 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { self.inner.try_decode_udwf(name, buf) } } + +// ============================================================================= +// Shared Python scalar UDF encode / decode helpers +// +// Both `PythonLogicalCodec` and `PythonPhysicalCodec` consult these on +// every `try_encode_udf` / `try_decode_udf` call. Same wire format on +// both layers — a Python `ScalarUDF` referenced inside a `LogicalPlan` +// or an `ExecutionPlan` round-trips identically. +// ============================================================================= + +/// Encode a Python scalar UDF inline if `node` is one. Returns +/// `Ok(true)` when the payload (`DFPYUDF1` prefix + cloudpickled +/// tuple) was written and the caller should skip its inner codec. +/// Returns `Ok(false)` for any non-Python UDF, signalling the caller +/// to delegate to its `inner`. +pub(crate) fn try_encode_python_scalar_udf(node: &ScalarUDF, buf: &mut Vec) -> Result { + let Some(py_udf) = node + .inner() + .as_any() + .downcast_ref::() + else { + return Ok(false); + }; + + Python::attach(|py| -> Result { + let bytes = encode_python_scalar_udf(py, py_udf) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + buf.extend_from_slice(PY_SCALAR_UDF_MAGIC); + buf.extend_from_slice(&bytes); + Ok(true) + }) +} + +/// Decode an inline Python scalar UDF payload. Returns `Ok(None)` +/// when `buf` does not carry the `DFPYUDF1` prefix, signalling the +/// caller to delegate to its `inner` codec (and eventually the +/// `FunctionRegistry`). +pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result>> { + if buf.is_empty() || !buf.starts_with(PY_SCALAR_UDF_MAGIC) { + return Ok(None); + } + let payload = &buf[PY_SCALAR_UDF_MAGIC.len()..]; + + Python::attach(|py| -> Result>> { + let udf = decode_python_scalar_udf(py, payload) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + Ok(Some(Arc::new(ScalarUDF::new_from_impl(udf)))) + }) +} + +/// Build the cloudpickle payload for a `PythonFunctionScalarUDF`. +/// +/// Layout: `cloudpickle.dumps((name, func, input_schema_bytes, +/// return_field, volatility_str))`. Input fields ride along as an +/// IPC-encoded pyarrow Schema so they round-trip without extra +/// plumbing. +fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> PyResult> { + let cloudpickle = py.import("cloudpickle")?; + + let input_schema = Schema::new(udf.input_fields().to_vec()); + let pa_schema_obj = input_schema.to_pyarrow(py)?; + let pa_schema = pa_schema_obj.into_bound(); + let schema_bytes: Vec = pa_schema + .call_method0("serialize")? + .call_method0("to_pybytes")? + .extract()?; + + let return_field_obj = udf.return_field().as_ref().to_pyarrow(py)?; + let volatility = format!("{:?}", udf.volatility()).to_lowercase(); + + let payload = PyTuple::new( + py, + [ + udf.name().into_pyobject(py)?.into_any(), + udf.func().bind(py).clone().into_any(), + PyBytes::new(py, &schema_bytes).into_any(), + return_field_obj.into_bound(), + volatility.into_pyobject(py)?.into_any(), + ], + )?; + + let blob = cloudpickle.call_method1("dumps", (payload,))?; + blob.extract::>() +} + +/// Inverse of [`encode_python_scalar_udf`]. +fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult { + let cloudpickle = py.import("cloudpickle")?; + let pyarrow = py.import("pyarrow")?; + + let tuple = cloudpickle + .call_method1("loads", (PyBytes::new(py, payload),))? + .cast_into::()?; + + let name: String = tuple.get_item(0)?.extract()?; + let func: Py = tuple.get_item(1)?.unbind(); + let schema_bytes: Vec = tuple.get_item(2)?.extract()?; + let return_field_py = tuple.get_item(3)?; + let volatility_str: String = tuple.get_item(4)?.extract()?; + + let buffer = pyarrow.call_method1("py_buffer", (PyBytes::new(py, &schema_bytes),))?; + let pa_schema = pyarrow + .getattr("ipc")? + .call_method1("read_schema", (buffer,))?; + + let schema = Schema::from_pyarrow_bound(&pa_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let input_fields: Vec = schema.fields().iter().map(|f| f.as_ref().clone()).collect(); + + let return_field = Field::from_pyarrow_bound(&return_field_py) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let volatility = datafusion_python_util::parse_volatility(&volatility_str) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + Ok(PythonFunctionScalarUDF::from_parts( + name, + func, + input_fields, + return_field, + volatility, + )) +} diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index c0a39cb47..468888415 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -43,11 +43,13 @@ use crate::expr::PyExpr; /// This struct holds the Python written function that is a /// ScalarUDF. #[derive(Debug)] -struct PythonFunctionScalarUDF { +pub(crate) struct PythonFunctionScalarUDF { name: String, func: Py, - signature: Signature, + input_fields: Vec, return_field: FieldRef, + signature: Signature, + volatility: Volatility, } impl PythonFunctionScalarUDF { @@ -63,10 +65,40 @@ impl PythonFunctionScalarUDF { Self { name, func, - signature, + input_fields, return_field: Arc::new(return_field), + signature, + volatility, } } + + /// Stored Python callable. Consumed by the codec to cloudpickle + /// the function body across process boundaries. + pub(crate) fn func(&self) -> &Py { + &self.func + } + + pub(crate) fn input_fields(&self) -> &[Field] { + &self.input_fields + } + + pub(crate) fn return_field(&self) -> &FieldRef { + &self.return_field + } + + pub(crate) fn volatility(&self) -> Volatility { + self.volatility + } + + pub(crate) fn from_parts( + name: String, + func: Py, + input_fields: Vec, + return_field: Field, + volatility: Volatility, + ) -> Self { + Self::new(name, func, input_fields, return_field, volatility) + } } impl Eq for PythonFunctionScalarUDF {} diff --git a/pyproject.toml b/pyproject.toml index 951f7adc3..7df7e4760 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,12 @@ classifiers = [ "Programming Language :: Rust", ] dependencies = [ + # cloudpickle is invoked by the Rust-side PythonLogicalCodec / + # PythonPhysicalCodec via pyo3 to serialize Python scalar UDF + # callables into the proto wire format. Lazy-imported on the encode + # / decode hot paths, so users who never serialize a plan or + # expression incur no runtime cost beyond the install footprint. + "cloudpickle>=2.0", "pyarrow>=16.0.0;python_version<'3.14'", "pyarrow>=22.0.0;python_version>='3.14'", "typing-extensions;python_version<'3.13'", @@ -120,6 +126,7 @@ extend-allowed-calls = ["datafusion.lit", "lit"] "PT011", "RUF015", "S101", + "S301", "S608", "SLF", ] @@ -133,6 +140,7 @@ extend-allowed-calls = ["datafusion.lit", "lit"] "PLR2004", "RUF015", "S101", + "S301", "T201", "W505", ] diff --git a/python/datafusion/__init__.py b/python/datafusion/__init__.py index f08b464bb..dfdeef07e 100644 --- a/python/datafusion/__init__.py +++ b/python/datafusion/__init__.py @@ -65,7 +65,7 @@ import importlib_metadata # type: ignore[import] # Public submodules -from . import functions, object_store, substrait, unparser +from . import functions, ipc, object_store, substrait, unparser # The following imports are okay to remain as opaque to the user. from ._internal import Config @@ -142,6 +142,7 @@ "configure_formatter", "expr", "functions", + "ipc", "lit", "literal", "object_store", diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index e0135e3ed..26e1f7e85 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -436,21 +436,51 @@ def variant_name(self) -> str: def to_bytes(self, ctx: SessionContext | None = None) -> bytes: """Serialize this expression to protobuf bytes. - When ``ctx`` is supplied, encoding routes through the session's - installed :class:`LogicalExtensionCodec`. Without ``ctx`` a - default codec is used. + Python scalar UDFs are cloudpickled inline by + :class:`PythonLogicalCodec`, so the returned blob is + self-contained for scalar UDFs. Aggregate / window / FFI UDFs + are stored by name only; the receiver must have them + registered. + + When ``ctx`` is supplied, encoding also routes through the + session's installed codec stack. """ ctx_arg = ctx.ctx if ctx is not None else None - return self.expr.to_bytes(ctx_arg) + return bytes(self.expr.to_bytes(ctx_arg)) - @staticmethod - def from_bytes(ctx: SessionContext, data: bytes) -> Expr: + @classmethod + def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: """Decode an expression from serialized protobuf bytes. - ``ctx`` provides the function registry for resolving UDF - references and the logical codec for in-band Python payloads. + ``ctx`` is the receiver :class:`SessionContext` used to resolve + function references not inlined by the codec (aggregate UDFs, + window UDFs, FFI UDFs). When ``ctx`` is ``None`` the worker + context set via :func:`datafusion.ipc.set_worker_ctx` is + consulted; if no worker context is set, a fresh + :class:`SessionContext` is used. + """ + from datafusion.ipc import _resolve_ctx + + resolved = _resolve_ctx(ctx) + return cls(expr_internal.RawExpr.from_bytes(resolved.ctx, buf)) + + def __reduce__(self) -> tuple: + """Pickle protocol hook. + + :class:`PythonLogicalCodec` cloudpickles referenced Python + scalar UDFs directly into the proto wire format, so the + returned blob is self-contained. On unpickle the bytes are + decoded against the worker context set via + :func:`datafusion.ipc.set_worker_ctx` (or a fresh + :class:`SessionContext` if none) for any remaining + registry-resolved references. """ - return Expr(expr_internal.RawExpr.from_bytes(ctx.ctx, data)) + return (Expr._reconstruct, (self.to_bytes(),)) + + @classmethod + def _reconstruct(cls, proto_bytes: bytes) -> Expr: + """Internal entry point used by :meth:`__reduce__` on unpickle.""" + return cls.from_bytes(proto_bytes) def __richcmp__(self, other: Expr, op: int) -> Expr: """Comparison operator.""" diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py new file mode 100644 index 000000000..7ec3498fa --- /dev/null +++ b/python/datafusion/ipc.py @@ -0,0 +1,98 @@ +# 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. + +"""Inter-process communication helpers for distributing DataFusion expressions. + +This module provides a worker-scoped :class:`SessionContext` slot that +:meth:`Expr.__reduce__` consults when unpickling expressions across process +boundaries. Set the worker context once per worker process (typically from a +``multiprocessing.Pool`` initializer or a Ray actor ``__init__``): + +>>> # doctest: +SKIP +>>> from datafusion import SessionContext +>>> from datafusion.ipc import set_worker_ctx +>>> +>>> def init_worker(): +... ctx = SessionContext() +... # register Rust-backed UDFs / aggregates / window functions here +... set_worker_ctx(ctx) + +Python scalar UDFs do not need pre-registration: their definitions are +cloudpickled into the proto wire format by ``PythonLogicalCodec`` and +reconstructed on the receiver automatically. The worker context is only +needed when the expression references aggregate / window UDFs, table +providers, or Rust-side function registrations the receiver wouldn't +otherwise have. +""" + +from __future__ import annotations + +import threading +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from datafusion.context import SessionContext + + +__all__ = [ + "clear_worker_ctx", + "get_worker_ctx", + "set_worker_ctx", +] + + +_local = threading.local() + + +def set_worker_ctx(ctx: SessionContext) -> None: + """Register the receiver :class:`SessionContext` for this worker. + + Call once per worker process — typically from a ``Pool`` initializer or a + Ray actor ``__init__``. Idempotent: overwrites any previous value. + + The worker context is stored in a thread-local slot, so each thread within + a worker can install its own context independently. + """ + _local.ctx = ctx + + +def clear_worker_ctx() -> None: + """Remove the worker context, restoring fresh-context fallback behavior.""" + if hasattr(_local, "ctx"): + del _local.ctx + + +def get_worker_ctx() -> SessionContext | None: + """Return the worker context if set, else ``None``.""" + return getattr(_local, "ctx", None) + + +def _resolve_ctx( + explicit_ctx: SessionContext | None = None, +) -> SessionContext: + """Resolve a context for Expr reconstruction. + + Priority: explicit argument > worker context > fresh context. + """ + if explicit_ctx is not None: + return explicit_ctx + worker = get_worker_ctx() + if worker is not None: + return worker + from datafusion.context import SessionContext # noqa: PLC0415 + + return SessionContext() diff --git a/python/tests/_pickle_multiprocessing_helpers.py b/python/tests/_pickle_multiprocessing_helpers.py new file mode 100644 index 000000000..4aa56efab --- /dev/null +++ b/python/tests/_pickle_multiprocessing_helpers.py @@ -0,0 +1,89 @@ +# 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. + +# The leading underscore is load-bearing: pytest with --import-mode=importlib +# (used in CI) assigns synthetic module names to test modules, which breaks +# subprocess imports during multiprocessing. An underscore-prefixed module is +# not collected as a test module, so it imports under its normal __name__ +# inside worker processes. + +from __future__ import annotations + +import pyarrow as pa +from datafusion import SessionContext, udf +from datafusion.ipc import clear_worker_ctx, set_worker_ctx + + +def make_double_udf(): + """Build the canonical UDF used in the multiprocessing tests.""" + return udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], + pa.int64(), + volatility="immutable", + name="double", + ) + + +def make_times_seven_udf(): + """Closure-capturing UDF — verifies cloudpickle preserves closed-over state.""" + multiplier = 7 + + def fn(arr): + return pa.array([(v.as_py() or 0) * multiplier for v in arr]) + + return udf( + fn, + [pa.int64()], + pa.int64(), + volatility="immutable", + name="times_seven", + ) + + +def init_worker_empty(): + """Pool initializer: install an empty SessionContext (no UDFs).""" + set_worker_ctx(SessionContext()) + + +def init_worker_clear(): + """Pool initializer: explicitly clear any prior worker context.""" + clear_worker_ctx() + + +def unpickle_and_describe(blob: bytes) -> str: + """Unpickle a proto-bytes blob and return its canonical name.""" + import pickle + + expr = pickle.loads(blob) + return expr.canonical_name() + + +def unpickle_and_evaluate(blob: bytes, batch: list[int]) -> list[int]: + """Unpickle an expression and evaluate it against an in-memory batch. + + Returns the result column as a Python list. Used to verify that + cloudpickled UDFs (including closure state) execute correctly in + a fresh worker process. + """ + import pickle + + expr = pickle.loads(blob) + ctx = SessionContext() + df = ctx.from_pydict({"a": batch}) + out = df.with_column("result", expr).select("result") + return out.to_pydict()["result"] diff --git a/python/tests/test_expr.py b/python/tests/test_expr.py index 6a466f6f2..e1fdeab44 100644 --- a/python/tests/test_expr.py +++ b/python/tests/test_expr.py @@ -1186,7 +1186,7 @@ def test_expr_to_bytes_roundtrip(ctx: SessionContext) -> None: original = col("a") + lit(1) blob = original.to_bytes(ctx) - restored = Expr.from_bytes(ctx, blob) + restored = Expr.from_bytes(blob, ctx=ctx) # Canonical name preserves the structure of the expression even # though the underlying PyExpr instances are different. @@ -1201,6 +1201,6 @@ def test_expr_to_bytes_no_ctx_default_codec() -> None: fresh = SessionContext() original = col("a") * lit(2) blob = original.to_bytes() # encode side: default codec - restored = Expr.from_bytes(fresh, blob) + restored = Expr.from_bytes(blob, ctx=fresh) assert restored.canonical_name() == original.canonical_name() diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py new file mode 100644 index 000000000..c1fc81651 --- /dev/null +++ b/python/tests/test_pickle_expr.py @@ -0,0 +1,164 @@ +# 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. + +"""In-process pickle round-trip tests for :class:`Expr`. + +The Rust-side ``PythonUDFCodec`` cloudpickles Python scalar UDF callables +directly into the proto wire format, so pickle blobs are self-contained. +The worker context (:mod:`datafusion.ipc`) is only needed for references +the codec can't inline — aggregate UDFs, window UDFs, FFI capsule UDFs. + +Cross-process tests live in ``test_pickle_multiprocessing.py``. +""" + +from __future__ import annotations + +import pickle +import threading + +import pyarrow as pa +import pytest +from datafusion import Expr, SessionContext, col, lit, udf +from datafusion.ipc import clear_worker_ctx, get_worker_ctx, set_worker_ctx + + +@pytest.fixture(autouse=True) +def _reset_worker_ctx(): + """Ensure every test starts with no worker context installed.""" + clear_worker_ctx() + yield + clear_worker_ctx() + + +def _double_udf(): + return udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], + pa.int64(), + volatility="immutable", + name="double", + ) + + +class TestProtoRoundTrip: + def test_builtin_round_trip(self): + e = col("a") + lit(1) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + assert decoded.canonical_name() == e.canonical_name() + + def test_to_bytes_from_bytes(self): + e = col("x") * lit(7) + blob = e.to_bytes() + assert isinstance(blob, bytes) + decoded = Expr.from_bytes(blob) + assert decoded.canonical_name() == e.canonical_name() + + def test_explicit_ctx_used(self, ctx): + e = col("a") + lit(1) + decoded = Expr.from_bytes(e.to_bytes(), ctx=ctx) + assert decoded.canonical_name() == e.canonical_name() + + +class TestUDFCodec: + """Python scalar UDFs ride inside the proto blob via the Rust codec. + + No worker context needed on the receiver — the cloudpickled callable is + embedded in ``fun_definition`` and reconstructed automatically. + """ + + def test_udf_self_contained_blob(self): + e = _double_udf()(col("a")) + blob = pickle.dumps(e) + # The codec inlines the callable, so the blob is much bigger than a + # pure built-in blob but doesn't depend on receiver-side registration. + assert len(blob) > 200 + + def test_udf_decodes_into_fresh_ctx(self): + e = _double_udf()(col("a")) + blob = e.to_bytes() + fresh = SessionContext() + decoded = Expr.from_bytes(blob, ctx=fresh) + assert "double" in decoded.canonical_name() + + def test_udf_decodes_via_pickle_with_no_worker_ctx(self): + e = _double_udf()(col("a")) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + assert "double" in decoded.canonical_name() + + def test_udf_decodes_via_pickle_with_worker_ctx(self): + set_worker_ctx(SessionContext()) + e = _double_udf()(col("a")) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + assert "double" in decoded.canonical_name() + + def test_closure_capturing_udf_names_match(self): + captured_multiplier = 7 + + def fn(arr): + return pa.array([(v.as_py() or 0) * captured_multiplier for v in arr]) + + u = udf( + fn, + [pa.int64()], + pa.int64(), + volatility="immutable", + name="times_seven", + ) + e = u(col("a")) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + # Round-trip names match; functional verification of captured state + # happens in test_pickle_multiprocessing via an actual UDF call. + assert decoded.canonical_name() == e.canonical_name() + + +class TestWorkerCtxLifecycle: + def test_set_and_clear(self): + assert get_worker_ctx() is None + ctx = SessionContext() + set_worker_ctx(ctx) + assert get_worker_ctx() is ctx + clear_worker_ctx() + assert get_worker_ctx() is None + + def test_clear_when_unset_is_noop(self): + clear_worker_ctx() # no error + assert get_worker_ctx() is None + + def test_thread_local_isolation(self): + main_ctx = SessionContext() + set_worker_ctx(main_ctx) + + seen_in_thread: list = [] + + def worker(): + seen_in_thread.append(get_worker_ctx()) + set_worker_ctx(SessionContext()) + seen_in_thread.append(get_worker_ctx()) + + t = threading.Thread(target=worker) + t.start() + t.join() + + # Thread saw no ctx initially (thread-local), then its own. + assert seen_in_thread[0] is None + assert seen_in_thread[1] is not main_ctx + # Main thread's ctx is unchanged by the thread's actions. + assert get_worker_ctx() is main_ctx diff --git a/python/tests/test_pickle_multiprocessing.py b/python/tests/test_pickle_multiprocessing.py new file mode 100644 index 000000000..89396de81 --- /dev/null +++ b/python/tests/test_pickle_multiprocessing.py @@ -0,0 +1,125 @@ +# 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. + +"""Cross-process pickle tests for :class:`Expr`. + +Workers run with each :mod:`multiprocessing` start method (``fork``, +``forkserver``, ``spawn``). Python scalar UDFs travel inside the proto blob +via the Rust-side ``PythonUDFCodec`` — no worker-side pre-registration +needed. Worker-side helpers live in ``_pickle_multiprocessing_helpers`` — +the underscore prefix avoids pytest collection so the module imports under +its real name in worker subprocesses. +""" + +from __future__ import annotations + +import multiprocessing as mp +import pickle +import sys + +import pytest +from datafusion import col, lit + +from . import _pickle_multiprocessing_helpers as helpers + + +def _multiprocessing_available() -> tuple[bool, str]: + """Return (available, reason). Some sandboxed environments deny semaphore + creation; without semaphores, ``multiprocessing.Pool`` cannot start. + """ + try: + ctx = mp.get_context("spawn") + with ctx.Pool(processes=1) as pool: + pool.map(int, [0]) + except PermissionError as exc: + return False, f"multiprocessing.Pool unavailable: {exc}" + except OSError as exc: + return False, f"multiprocessing.Pool unavailable: {exc}" + return True, "" + + +_MP_AVAILABLE, _MP_SKIP_REASON = _multiprocessing_available() + +pytestmark = pytest.mark.skipif(not _MP_AVAILABLE, reason=_MP_SKIP_REASON) + + +START_METHODS = [ + pytest.param( + "fork", + marks=pytest.mark.skipif( + sys.platform == "darwin", + reason="fork start method is unsafe with PyArrow/tokio on macOS", + ), + ), + "forkserver", + "spawn", +] + + +@pytest.mark.parametrize("start_method", START_METHODS) +@pytest.mark.timeout(120) +def test_builtin_pickle_via_pool(start_method): + """Built-in expressions round-trip in every start method.""" + expr = col("a") + lit(1) + blob = pickle.dumps(expr) + + ctx = mp.get_context(start_method) + with ctx.Pool(processes=2) as pool: + results = pool.map(helpers.unpickle_and_describe, [blob, blob, blob]) + + assert all(r == expr.canonical_name() for r in results) + + +@pytest.mark.parametrize("start_method", START_METHODS) +@pytest.mark.timeout(120) +def test_udf_pickle_self_contained(start_method): + """Scalar UDF travels inside the proto blob — no worker pre-registration. + + Workers start with no UDF registered. The Rust-side ``PythonUDFCodec`` + reconstructs the UDF from bytes embedded in the pickle blob. + """ + udf_obj = helpers.make_double_udf() + expr = udf_obj(col("a")) + blob = pickle.dumps(expr) + + ctx = mp.get_context(start_method) + with ctx.Pool(processes=2) as pool: + results = pool.starmap( + helpers.unpickle_and_evaluate, + [(blob, [1, 2, 3]), (blob, [10, 20, 30])], + ) + + assert results[0] == [2, 4, 6] + assert results[1] == [20, 40, 60] + + +@pytest.mark.parametrize("start_method", START_METHODS) +@pytest.mark.timeout(120) +def test_closure_capturing_udf_via_pool(start_method): + """Cloudpickle preserves closure state across the codec boundary.""" + udf_obj = helpers.make_times_seven_udf() + expr = udf_obj(col("a")) + blob = pickle.dumps(expr) + + ctx = mp.get_context(start_method) + with ctx.Pool(processes=2) as pool: + results = pool.starmap( + helpers.unpickle_and_evaluate, + [(blob, [1, 2, 3])], + ) + + assert results[0] == [7, 14, 21] diff --git a/uv.lock b/uv.lock index 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"sha256:b18f2d1533a71f069c7f82d524a52599053d4c7166e9dd374ae2136b7f40f7c8", size = 23594 }, ] From c7f95ac5eaddc02ffc346849451d84032c8f0f62 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 15:14:15 -0400 Subject: [PATCH 02/57] docs(pickle): user guide + Ray example + CI backstop + UDF.name MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Companion to the pickle work in the previous commit. Ships the discoverable surface a user would actually reach for when they hit "how do I distribute these expressions": * `docs/source/user-guide/io/distributing_expressions.rst` — end-to-end user guide covering the recommended `Pool(initializer=...)` pattern, the worker context shape, what does and does not survive the round-trip (scalar UDFs yes, UDAF/UDWF/FFI by name), Python 3.14 start-method change, and the cloudpickle security note. * `examples/ray_pickle_expr.py` — runnable Ray actor demo using `set_worker_ctx` from an actor `__init__`. * `examples/README.md` — links to the Ray example. * `docs/source/user-guide/io/index.rst` — adds the new page to the IO TOC. * `.github/workflows/test.yml` — 30-minute `timeout-minutes` backstop on the test matrix so a hung multiprocessing worker (e.g. during a pickle regression) does not block CI indefinitely. * `python/datafusion/user_defined.py` — `ScalarUDF` / `AggregateUDF` / `WindowUDF` get a `.name` property surfacing the registered name. Useful for tests asserting an expression carries a specific UDF reference, and for users debugging worker registrations. Co-Authored-By: Claude Opus 4.7 (1M context) --- .github/workflows/test.yml | 3 + .../io/distributing_expressions.rst | 141 ++++++++++++++++++ docs/source/user-guide/io/index.rst | 1 + examples/README.md | 4 + examples/ray_pickle_expr.py | 96 ++++++++++++ python/datafusion/user_defined.py | 18 +++ 6 files changed, 263 insertions(+) create mode 100644 docs/source/user-guide/io/distributing_expressions.rst create mode 100644 examples/ray_pickle_expr.py diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index c597ab308..2cd792ea9 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -29,6 +29,9 @@ env: jobs: test-matrix: runs-on: ubuntu-latest + # Backstop: a hung multiprocessing worker (e.g. during a pickle regression) + # should not block CI longer than this. + timeout-minutes: 30 strategy: fail-fast: false matrix: diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst new file mode 100644 index 000000000..520e291c6 --- /dev/null +++ b/docs/source/user-guide/io/distributing_expressions.rst @@ -0,0 +1,141 @@ +.. 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. + +Distributing expressions across processes +========================================= + +DataFusion expressions (:py:class:`~datafusion.Expr`) can be serialized and +shipped across process boundaries — useful for distributing work over a +``multiprocessing.Pool``, a Ray actor pool, or any framework that supports a +per-worker initialization hook. + +Pickle support +-------------- + +:py:class:`~datafusion.Expr` implements the pickle protocol directly. Call +:py:func:`pickle.dumps` on an expression and ship the bytes; the receiver +calls :py:func:`pickle.loads`. Python *scalar UDFs* are cloudpickled into the +proto wire format by a Rust-side codec (``PythonUDFCodec``), so the blob is +self-contained — the receiver does not need to pre-register the UDF. + +.. code-block:: python + + import multiprocessing as mp + import pickle + + import pyarrow as pa + from datafusion import SessionContext, col, lit, udf + + def init_worker(): + # Optional: install a worker context for aggregate / window UDFs, + # table providers, or Rust-side function registrations. Not needed + # for built-ins or Python scalar UDFs. + pass + + def evaluate(blob_and_batch): + blob, batch = blob_and_batch + expr = pickle.loads(blob) + ctx = SessionContext() + df = ctx.from_pydict({"a": batch}) + return df.with_column("result", expr).select("result").to_pydict()["result"] + + if __name__ == "__main__": + double = udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], pa.int64(), volatility="immutable", name="double", + ) + blob = pickle.dumps(double(col("a"))) + + mp_ctx = mp.get_context("forkserver") + with mp_ctx.Pool(processes=4) as pool: + results = pool.map( + evaluate, + [(blob, [1, 2, 3]), (blob, [10, 20, 30])], + ) + print(results) # [[2, 4, 6], [20, 40, 60]] + +Worker-scoped context +--------------------- + +For references the codec cannot inline — aggregate UDFs, window UDFs, FFI +capsule UDFs, or anything resolved through the +:class:`SessionContext`'s function registry — set a worker-scoped context +once per process using :py:func:`datafusion.ipc.set_worker_ctx`: + +.. code-block:: python + + from datafusion import SessionContext + from datafusion.ipc import set_worker_ctx + + def init_worker(): + ctx = SessionContext() + ctx.register_udaf(my_aggregate) # if needed + set_worker_ctx(ctx) + + with mp.get_context("forkserver").Pool( + processes=4, initializer=init_worker + ) as pool: + ... + +Without a worker context, unpickling falls back to a fresh +:py:class:`SessionContext`. Built-in functions resolve; Python scalar UDFs +ride along inside the blob via the codec. References to aggregate / window +UDFs or other registry-only entries raise an informative error if not +registered on the worker. + +Python 3.14 default change +-------------------------- + +Python 3.14 changed the POSIX default start method for +:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With ``fork``, a +context set in the parent was visible in workers via copy-on-write; with +``forkserver`` and ``spawn`` it is not. The codec + worker-init pattern works +on every start method — prefer it over relying on inherited state. + +Trade-offs of inline UDFs +------------------------- + +* **Blob size** — cloudpickled callables add bytes per blob. A trivial + built-in expression is ~20 bytes; an expression referencing a Python scalar + UDF is hundreds of bytes (the cloudpickled callable + signature). Pre-register + shared UDFs on workers via :py:func:`~datafusion.ipc.set_worker_ctx` when + the same UDF is shipped many times and you want to avoid the overhead. +* **Closure capture** — cloudpickle captures closure state. Surprises are + possible if the UDF closes over large objects, module-level mutable state, + or non-portable file paths. +* **FFI scalar UDFs cannot be inlined** — PyCapsule-backed UDFs have no + Python callable to cloudpickle. The codec leaves their ``fun_definition`` + empty; the receiver must have a matching registration. +* **Aggregate and window UDFs cannot be inlined yet** — their Python state + is held inside opaque factory closures on the Rust side. Pre-register on + the worker. + +Security +-------- + +.. warning:: + + Pickle blobs containing inlined UDFs deserialize via :py:mod:`cloudpickle`, + which executes arbitrary code on the receiver. Only :py:func:`pickle.loads` + blobs from trusted sources. For untrusted-source workflows, restrict the + sender to built-in functions and pre-registered Rust-side UDFs. + +See also +-------- + +* :py:mod:`datafusion.ipc` — module-level API reference. +* ``examples/ray_pickle_expr.py`` — runnable Ray actor example. diff --git a/docs/source/user-guide/io/index.rst b/docs/source/user-guide/io/index.rst index b885cfeda..3f6188829 100644 --- a/docs/source/user-guide/io/index.rst +++ b/docs/source/user-guide/io/index.rst @@ -24,6 +24,7 @@ IO arrow avro csv + distributing_expressions json parquet table_provider diff --git a/examples/README.md b/examples/README.md index 3024c782f..68eeb0975 100644 --- a/examples/README.md +++ b/examples/README.md @@ -44,6 +44,10 @@ Here is a direct link to the file used in the examples: - [Register a Python UDF with DataFusion](./python-udf.py) - [Register a Python UDAF with DataFusion](./python-udaf.py) +### Distributing DataFusion expressions + +- [Pickle expressions and send them to Ray actors](./ray_pickle_expr.py) + ### Substrait Support - [Serialize query plans using Substrait](./substrait.py) diff --git a/examples/ray_pickle_expr.py b/examples/ray_pickle_expr.py new file mode 100644 index 000000000..74a135043 --- /dev/null +++ b/examples/ray_pickle_expr.py @@ -0,0 +1,96 @@ +# 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. + +"""Distribute DataFusion expressions to Ray actors. + +This example shows the worker-init pattern from the user guide adapted to +Ray's actor model: each actor builds its own :class:`SessionContext`. +Python scalar UDFs travel inside the pickle blob via the Rust-side +``PythonUDFCodec`` — no actor-side pre-registration is required. The +worker context (set via :func:`datafusion.ipc.set_worker_ctx`) is still +useful for aggregate/window UDFs or other registry-only entries; we set +one here to show the pattern. + +Prerequisites: + pip install ray + +Run: + python examples/ray_pickle_expr.py +""" + +import pickle + +import pyarrow as pa +import ray +from datafusion import SessionContext, col, lit, udf +from datafusion.ipc import set_worker_ctx + + +def _build_double_udf(): + """Return the demo UDF used by the actors.""" + return udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], + pa.int64(), + volatility="immutable", + name="double", + ) + + +@ray.remote +class DataFusionWorker: + """A Ray actor with a private :class:`SessionContext`.""" + + def __init__(self) -> None: + ctx = SessionContext() + ctx.register_udf(_build_double_udf()) + # The worker context is what Expr.__setstate__ consults when + # pickled expressions arrive at this actor. + set_worker_ctx(ctx) + self._ctx = ctx + + def evaluate(self, expr_blob: bytes, batch_pylist: list[int]) -> list[int]: + """Unpickle an Expr, run it over an in-memory batch, return results.""" + expr = pickle.loads(expr_blob) + df = self._ctx.from_pydict({"a": batch_pylist}) + out = df.with_column("result", expr).select("result") + return out.to_pydict()["result"] + + +def main() -> None: + ray.init(ignore_reinit_error=True) + + sender = SessionContext() + sender.register_udf(_build_double_udf()) + expr = _build_double_udf()(col("a")) + lit(1) + blob = pickle.dumps(expr) + print(f"pickled expression: {len(blob)} bytes") + + workers = [DataFusionWorker.remote() for _ in range(2)] + batches = [[1, 2, 3], [10, 20, 30], [100, 200, 300]] + futures = [ + workers[i % len(workers)].evaluate.remote(blob, batch) + for i, batch in enumerate(batches) + ] + for batch, result in zip(batches, ray.get(futures), strict=True): + print(f"input {batch} -> {result}") + + ray.shutdown() + + +if __name__ == "__main__": + main() diff --git a/python/datafusion/user_defined.py b/python/datafusion/user_defined.py index 848ab4cee..c18a14b2e 100644 --- a/python/datafusion/user_defined.py +++ b/python/datafusion/user_defined.py @@ -132,6 +132,7 @@ def __init__( See helper method :py:func:`udf` for argument details. """ + self._name = name if hasattr(func, "__datafusion_scalar_udf__"): self._udf = df_internal.ScalarUDF.from_pycapsule(func) return @@ -141,6 +142,11 @@ def __init__( name, func, input_fields, return_field, str(volatility) ) + @property + def name(self) -> str: + """Return the registered name of this UDF.""" + return self._name + def __repr__(self) -> str: """Print a string representation of the Scalar UDF.""" return self._udf.__repr__() @@ -394,6 +400,7 @@ def __init__( See :py:func:`udaf` for a convenience function and argument descriptions. """ + self._name = name if hasattr(accumulator, "__datafusion_aggregate_udf__"): self._udaf = df_internal.AggregateUDF.from_pycapsule(accumulator) return @@ -418,6 +425,11 @@ def __init__( str(volatility), ) + @property + def name(self) -> str: + """Return the registered name of this UDAF.""" + return self._name + def __repr__(self) -> str: """Print a string representation of the Aggregate UDF.""" return self._udaf.__repr__() @@ -821,6 +833,7 @@ def __init__( See :py:func:`udwf` for a convenience function and argument descriptions. """ + self._name = name if hasattr(func, "__datafusion_window_udf__"): self._udwf = df_internal.WindowUDF.from_pycapsule(func) return @@ -828,6 +841,11 @@ def __init__( name, func, input_types, return_type, str(volatility) ) + @property + def name(self) -> str: + """Return the registered name of this UDWF.""" + return self._name + def __repr__(self) -> str: """Print a string representation of the Window UDF.""" return self._udwf.__repr__() From 045071399c40103088f6edb63d4dd197a53eb56a Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 15:39:02 -0400 Subject: [PATCH 03/57] refactor: drop dead state from PythonFunctionScalarUDF MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `input_fields: Vec` and `volatility: Volatility` were added to the struct so the codec could read them on encode. Both were redundant: * `Signature` already carries the `Vec` (via `TypeSignature::Exact`) and `Volatility` — the constructor collapses the incoming `Vec` to `DataType`s on its way into the signature, so `Field`-level metadata (nullability, attached metadata) is never propagated anywhere on the local side. * On decode, `from_parts` runs that same collapse again. Sender's `Signature` and receiver's `Signature` end up with the same `DataType`s and the same `Volatility`. The reconstructed `PythonFunctionScalarUDF` is functionally equivalent to the original without preserving the input-side `Field`s. Revert the struct to its original 4-field shape (`name`, `func`, `signature`, `return_field`). The codec now derives the input `DataType`s from `signature.type_signature` and reads volatility from `signature.volatility`. Input fields are still serialized into the cloudpickle payload (with synthesized `arg_i` names) so the wire format is unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 31 +++++++++++++++++++++++++------ crates/core/src/udf.rs | 21 +++++++-------------- 2 files changed, 32 insertions(+), 20 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index e6c237ef6..4ccf379a2 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -85,7 +85,7 @@ use datafusion::datasource::TableProvider; use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; use datafusion::logical_expr::{ - AggregateUDF, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, WindowUDF, + AggregateUDF, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, TypeSignature, WindowUDF, }; use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; @@ -357,13 +357,32 @@ pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result, udf: &PythonFunctionScalarUDF) -> PyResult> { let cloudpickle = py.import("cloudpickle")?; - let input_schema = Schema::new(udf.input_fields().to_vec()); + let signature = udf.signature(); + let input_dtypes: Vec = match &signature.type_signature { + TypeSignature::Exact(types) => types.clone(), + other => { + return Err(pyo3::exceptions::PyValueError::new_err(format!( + "PythonFunctionScalarUDF expected Signature::Exact, got {other:?}" + ))); + } + }; + let fields: Vec = input_dtypes + .into_iter() + .enumerate() + .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) + .collect(); + let input_schema = Schema::new(fields); let pa_schema_obj = input_schema.to_pyarrow(py)?; let pa_schema = pa_schema_obj.into_bound(); let schema_bytes: Vec = pa_schema @@ -372,7 +391,7 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py .extract()?; let return_field_obj = udf.return_field().as_ref().to_pyarrow(py)?; - let volatility = format!("{:?}", udf.volatility()).to_lowercase(); + let volatility = format!("{:?}", signature.volatility).to_lowercase(); let payload = PyTuple::new( py, diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index 468888415..875a7352c 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -46,10 +46,8 @@ use crate::expr::PyExpr; pub(crate) struct PythonFunctionScalarUDF { name: String, func: Py, - input_fields: Vec, - return_field: FieldRef, signature: Signature, - volatility: Volatility, + return_field: FieldRef, } impl PythonFunctionScalarUDF { @@ -65,10 +63,8 @@ impl PythonFunctionScalarUDF { Self { name, func, - input_fields, - return_field: Arc::new(return_field), signature, - volatility, + return_field: Arc::new(return_field), } } @@ -78,18 +74,15 @@ impl PythonFunctionScalarUDF { &self.func } - pub(crate) fn input_fields(&self) -> &[Field] { - &self.input_fields - } - pub(crate) fn return_field(&self) -> &FieldRef { &self.return_field } - pub(crate) fn volatility(&self) -> Volatility { - self.volatility - } - + /// Reconstruct a `PythonFunctionScalarUDF` from the parts emitted + /// by the codec. `input_fields` here only contributes `data_type` + /// info (collapsed into `Signature::exact`); their names, + /// nullability, and metadata are not retained, so the decoder is + /// free to fabricate them from `Vec`. pub(crate) fn from_parts( name: String, func: Py, From fd46c94c6f98226424d5fbd5e643098d12845ac3 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 15:47:06 -0400 Subject: [PATCH 04/57] refactor(codec): use arrow-rs native IPC for schema serialization MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Previous round-trip went Rust Schema -> pyarrow Schema -> IPC bytes -> cloudpickle tuple -> pyarrow Schema -> Rust Schema, for both the input schema and the return Field. Two unnecessary pyarrow trips on each side. Replace with `StreamWriter::try_new(&mut buf, &schema)?.finish()?` on the encoder and `StreamReader::try_new(cursor, None)?.schema()` on the decoder. Both ends produce / consume the same Arrow IPC stream bytes — arrow-rs writes a schema-only stream, arrow-rs reads it back, no PyArrow involvement. Tuple shape changes slightly: the fourth field is now a one-field `return_schema_bytes` IPC blob instead of a pickled pyarrow `Field`. Keeps everything in `Vec` form before cloudpickle picks it up. `pyarrow.ipc.read_schema` and the `ToPyArrow` / `FromPyArrow` traits on `Schema` / `Field` are no longer needed on the codec hot path, shaving a noticeable chunk of pyarrow function dispatch from each encode / decode call. Pickle tests still green. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 93 +++++++++++++++++++++++++--------------- 1 file changed, 59 insertions(+), 34 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 4ccf379a2..5311dfc72 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -78,8 +78,8 @@ use std::sync::Arc; use arrow::datatypes::{Field, Schema, SchemaRef}; -use arrow::pyarrow::ToPyArrow; -use datafusion::arrow::pyarrow::FromPyArrow; +use arrow::ipc::reader::StreamReader; +use arrow::ipc::writer::StreamWriter; use datafusion::common::{Result, TableReference}; use datafusion::datasource::TableProvider; use datafusion::datasource::file_format::FileFormatFactory; @@ -91,7 +91,6 @@ use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; use datafusion_proto::logical_plan::{DefaultLogicalExtensionCodec, LogicalExtensionCodec}; use datafusion_proto::physical_plan::{DefaultPhysicalExtensionCodec, PhysicalExtensionCodec}; -use pyo3::BoundObject; use pyo3::prelude::*; use pyo3::types::{PyBytes, PyTuple}; @@ -357,14 +356,14 @@ pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result, udf: &PythonFunctionScalarUDF) -> PyResult> { let cloudpickle = py.import("cloudpickle")?; @@ -377,20 +376,19 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py ))); } }; - let fields: Vec = input_dtypes + let input_fields: Vec = input_dtypes .into_iter() .enumerate() .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) .collect(); - let input_schema = Schema::new(fields); - let pa_schema_obj = input_schema.to_pyarrow(py)?; - let pa_schema = pa_schema_obj.into_bound(); - let schema_bytes: Vec = pa_schema - .call_method0("serialize")? - .call_method0("to_pybytes")? - .extract()?; - - let return_field_obj = udf.return_field().as_ref().to_pyarrow(py)?; + let input_schema = Schema::new(input_fields); + let input_schema_bytes = schema_to_ipc_bytes(&input_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let return_schema = Schema::new(vec![udf.return_field().as_ref().clone()]); + let return_schema_bytes = schema_to_ipc_bytes(&return_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let volatility = format!("{:?}", signature.volatility).to_lowercase(); let payload = PyTuple::new( @@ -398,8 +396,8 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py [ udf.name().into_pyobject(py)?.into_any(), udf.func().bind(py).clone().into_any(), - PyBytes::new(py, &schema_bytes).into_any(), - return_field_obj.into_bound(), + PyBytes::new(py, &input_schema_bytes).into_any(), + PyBytes::new(py, &return_schema_bytes).into_any(), volatility.into_pyobject(py)?.into_any(), ], )?; @@ -411,7 +409,6 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py /// Inverse of [`encode_python_scalar_udf`]. fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult { let cloudpickle = py.import("cloudpickle")?; - let pyarrow = py.import("pyarrow")?; let tuple = cloudpickle .call_method1("loads", (PyBytes::new(py, payload),))? @@ -419,21 +416,30 @@ fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult = tuple.get_item(1)?.unbind(); - let schema_bytes: Vec = tuple.get_item(2)?.extract()?; - let return_field_py = tuple.get_item(3)?; + let input_schema_bytes: Vec = tuple.get_item(2)?.extract()?; + let return_schema_bytes: Vec = tuple.get_item(3)?.extract()?; let volatility_str: String = tuple.get_item(4)?.extract()?; - let buffer = pyarrow.call_method1("py_buffer", (PyBytes::new(py, &schema_bytes),))?; - let pa_schema = pyarrow - .getattr("ipc")? - .call_method1("read_schema", (buffer,))?; - - let schema = Schema::from_pyarrow_bound(&pa_schema) + let input_schema = schema_from_ipc_bytes(&input_schema_bytes) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let input_fields: Vec = schema.fields().iter().map(|f| f.as_ref().clone()).collect(); + let input_fields: Vec = input_schema + .fields() + .iter() + .map(|f| f.as_ref().clone()) + .collect(); - let return_field = Field::from_pyarrow_bound(&return_field_py) + let return_schema = schema_from_ipc_bytes(&return_schema_bytes) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let return_field = return_schema + .fields() + .first() + .ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err( + "PythonFunctionScalarUDF return schema must contain exactly one field", + ) + })? + .as_ref() + .clone(); let volatility = datafusion_python_util::parse_volatility(&volatility_str) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; @@ -446,3 +452,22 @@ fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult arrow::error::Result> { + let mut buf: Vec = Vec::new(); + { + let mut writer = StreamWriter::try_new(&mut buf, schema)?; + writer.finish()?; + } + Ok(buf) +} + +/// Decode an IPC stream containing only a schema message back into a +/// `Schema`. Inverse: [`schema_to_ipc_bytes`]. +fn schema_from_ipc_bytes(bytes: &[u8]) -> arrow::error::Result { + let reader = StreamReader::try_new(std::io::Cursor::new(bytes), None)?; + Ok(reader.schema().as_ref().clone()) +} From 0879309baf1f86b74c04c0f5c88c3819d37e5515 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 15:53:25 -0400 Subject: [PATCH 05/57] docs: strip implementation jargon from Expr pickle docstrings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Previous wording for `Expr.to_bytes`, `Expr.__reduce__`, and the `datafusion.ipc` module header referenced ``PythonLogicalCodec`` and ``cloudpickle`` to explain what survives the wire. Neither name is importable from Python and the mechanism is irrelevant to the end user — only the resulting contract matters. Reword each docstring to describe the user-facing guarantee directly: * Python scalar UDFs travel inside the pickle / serialized blob, no pre-registration needed on the receiver. * Aggregate UDFs, window UDFs, and FFI-capsule UDFs travel by name only and require the receiver to have them registered (typically via `set_worker_ctx`). The implementation can change underneath without invalidating these docs. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/datafusion/expr.py | 19 ++++++++++--------- python/datafusion/ipc.py | 12 ++++++------ 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 26e1f7e85..f9e57c8c9 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -436,9 +436,9 @@ def variant_name(self) -> str: def to_bytes(self, ctx: SessionContext | None = None) -> bytes: """Serialize this expression to protobuf bytes. - Python scalar UDFs are cloudpickled inline by - :class:`PythonLogicalCodec`, so the returned blob is - self-contained for scalar UDFs. Aggregate / window / FFI UDFs + Python scalar UDFs are inlined into the returned bytes — the + receiver does not need to pre-register them. Aggregate UDFs, + window UDFs, and UDFs imported via the FFI capsule protocol are stored by name only; the receiver must have them registered. @@ -467,13 +467,14 @@ def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: def __reduce__(self) -> tuple: """Pickle protocol hook. - :class:`PythonLogicalCodec` cloudpickles referenced Python - scalar UDFs directly into the proto wire format, so the - returned blob is self-contained. On unpickle the bytes are - decoded against the worker context set via + Python scalar UDFs referenced by the expression are inlined + into the pickle blob, so the receiver does not need to + pre-register them. On unpickle the bytes are decoded against + the worker context set via :func:`datafusion.ipc.set_worker_ctx` (or a fresh - :class:`SessionContext` if none) for any remaining - registry-resolved references. + :class:`SessionContext` if none) for any registry-resolved + references — aggregate UDFs, window UDFs, UDFs imported via + the FFI capsule protocol. """ return (Expr._reconstruct, (self.to_bytes(),)) diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 7ec3498fa..907723ab3 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -31,12 +31,12 @@ ... # register Rust-backed UDFs / aggregates / window functions here ... set_worker_ctx(ctx) -Python scalar UDFs do not need pre-registration: their definitions are -cloudpickled into the proto wire format by ``PythonLogicalCodec`` and -reconstructed on the receiver automatically. The worker context is only -needed when the expression references aggregate / window UDFs, table -providers, or Rust-side function registrations the receiver wouldn't -otherwise have. +Python scalar UDFs do not need pre-registration: their definitions +travel inside the pickled expression and are reconstructed on the +receiver automatically. The worker context is only needed when the +expression references aggregate UDFs, window UDFs, table providers, +or UDFs imported via the FFI capsule protocol — anything the +receiver would otherwise resolve from its registered functions. """ from __future__ import annotations From cc5ce7ef7cc1f60965591dc0423e2cc46954a9d5 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 16:00:25 -0400 Subject: [PATCH 06/57] docs: reframe distributed-expression docs around the user goal MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit User-facing docs throughout this PR led with "pickle support": filename-shaped headings, function docstrings describing how things get cloudpickled into a Rust-side codec, etc. That's the implementation pathway, not the user's goal. The user's goal is to build an expression in a driver process and ship it to worker processes for distributed evaluation. Pickle is the mechanism Python provides to make that work; we hook into it. End users typically don't care how the bytes are produced — they care which references survive the trip and what they have to register on each worker. Reframe across user-facing surfaces: * `docs/source/user-guide/io/distributing_expressions.rst` — leads with the worker-pool use case, drops `PythonUDFCodec` / cloudpickle vocabulary, presents "what travels with the expression" as the user contract. * `datafusion.ipc` module docstring + `set_worker_ctx` / `clear_worker_ctx` / `get_worker_ctx` — describes what the user installs and why, not internal lookup details. * `Expr.to_bytes` / `from_bytes` / `__reduce__` — describes what's shipped vs what travels by name; cross-references the user guide instead of repeating the codec story. * `examples/ray_pickle_expr.py` header + comment + README entry — goal-first wording. * Pickle test module docstrings — drop the dangling reference to `PythonUDFCodec` (also a stale name post-PR1). Code behavior unchanged. 1088 tests still green. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../io/distributing_expressions.rst | 127 ++++++++++-------- examples/README.md | 2 +- examples/ray_pickle_expr.py | 17 ++- python/datafusion/expr.py | 48 +++---- python/datafusion/ipc.py | 46 ++++--- python/tests/test_pickle_expr.py | 9 +- python/tests/test_pickle_multiprocessing.py | 10 +- 7 files changed, 139 insertions(+), 120 deletions(-) diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst index 520e291c6..77f24740c 100644 --- a/docs/source/user-guide/io/distributing_expressions.rst +++ b/docs/source/user-guide/io/distributing_expressions.rst @@ -18,19 +18,18 @@ Distributing expressions across processes ========================================= -DataFusion expressions (:py:class:`~datafusion.Expr`) can be serialized and -shipped across process boundaries — useful for distributing work over a -``multiprocessing.Pool``, a Ray actor pool, or any framework that supports a -per-worker initialization hook. +A common pattern is to build a DataFusion expression +(:py:class:`~datafusion.Expr`) in a driver process, hand it to a pool of +worker processes (``multiprocessing.Pool``, a Ray actor pool, or any other +framework with a per-worker initialization hook), and have each worker +evaluate the expression against its own slice of data. -Pickle support --------------- +DataFusion expressions support this directly: they can be sent through +:py:mod:`pickle` like any other Python object. Python scalar UDFs ride along +inside the pickled bytes — the receiver does not need to pre-register them. -:py:class:`~datafusion.Expr` implements the pickle protocol directly. Call -:py:func:`pickle.dumps` on an expression and ship the bytes; the receiver -calls :py:func:`pickle.loads`. Python *scalar UDFs* are cloudpickled into the -proto wire format by a Rust-side codec (``PythonUDFCodec``), so the blob is -self-contained — the receiver does not need to pre-register the UDF. +Basic worker-pool example +------------------------- .. code-block:: python @@ -38,21 +37,17 @@ self-contained — the receiver does not need to pre-register the UDF. import pickle import pyarrow as pa - from datafusion import SessionContext, col, lit, udf + from datafusion import SessionContext, col, udf - def init_worker(): - # Optional: install a worker context for aggregate / window UDFs, - # table providers, or Rust-side function registrations. Not needed - # for built-ins or Python scalar UDFs. - pass def evaluate(blob_and_batch): blob, batch = blob_and_batch - expr = pickle.loads(blob) + expr = pickle.loads(blob) # Python scalar UDFs ride along inline. ctx = SessionContext() df = ctx.from_pydict({"a": batch}) return df.with_column("result", expr).select("result").to_pydict()["result"] + if __name__ == "__main__": double = udf( lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), @@ -68,74 +63,92 @@ self-contained — the receiver does not need to pre-register the UDF. ) print(results) # [[2, 4, 6], [20, 40, 60]] -Worker-scoped context ---------------------- -For references the codec cannot inline — aggregate UDFs, window UDFs, FFI -capsule UDFs, or anything resolved through the -:class:`SessionContext`'s function registry — set a worker-scoped context -once per process using :py:func:`datafusion.ipc.set_worker_ctx`: +What travels with the expression +-------------------------------- + +* **Built-in functions** (``abs``, ``length``, arithmetic, comparisons, etc.) + — fully portable. Worker needs nothing pre-registered. +* **Python scalar UDFs** (defined with :py:func:`datafusion.udf`) — fully + portable. The callable and its signature travel inside the pickled bytes + and are reconstructed on the worker automatically. +* **Aggregate UDFs**, **window UDFs**, **UDFs imported via the FFI capsule + protocol** — travel **by name only**. The worker must already have a + matching registration on its :py:class:`SessionContext`. Without that + registration, evaluation raises an error. + +Registering shared UDFs on workers +---------------------------------- + +When an expression references something that travels by name only (aggregate +UDF, window UDF, FFI UDF), set up the worker's :py:class:`SessionContext` +once per process and install it as the *worker context*: .. code-block:: python from datafusion import SessionContext from datafusion.ipc import set_worker_ctx + def init_worker(): ctx = SessionContext() - ctx.register_udaf(my_aggregate) # if needed + ctx.register_udaf(my_aggregate) set_worker_ctx(ctx) + with mp.get_context("forkserver").Pool( processes=4, initializer=init_worker ) as pool: ... -Without a worker context, unpickling falls back to a fresh -:py:class:`SessionContext`. Built-in functions resolve; Python scalar UDFs -ride along inside the blob via the codec. References to aggregate / window -UDFs or other registry-only entries raise an informative error if not -registered on the worker. +Inside a worker, expressions reconstructed by :py:func:`pickle.loads` resolve +their by-name references against the installed worker context. If no worker +context is installed, a fresh empty :py:class:`SessionContext` is used — +fine for expressions that only reference built-ins and Python scalar UDFs, +but anything by-name-only will fail to resolve. Python 3.14 default change -------------------------- Python 3.14 changed the POSIX default start method for -:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With ``fork``, a -context set in the parent was visible in workers via copy-on-write; with -``forkserver`` and ``spawn`` it is not. The codec + worker-init pattern works -on every start method — prefer it over relying on inherited state. - -Trade-offs of inline UDFs -------------------------- - -* **Blob size** — cloudpickled callables add bytes per blob. A trivial - built-in expression is ~20 bytes; an expression referencing a Python scalar - UDF is hundreds of bytes (the cloudpickled callable + signature). Pre-register - shared UDFs on workers via :py:func:`~datafusion.ipc.set_worker_ctx` when - the same UDF is shipped many times and you want to avoid the overhead. -* **Closure capture** — cloudpickle captures closure state. Surprises are - possible if the UDF closes over large objects, module-level mutable state, - or non-portable file paths. -* **FFI scalar UDFs cannot be inlined** — PyCapsule-backed UDFs have no - Python callable to cloudpickle. The codec leaves their ``fun_definition`` - empty; the receiver must have a matching registration. -* **Aggregate and window UDFs cannot be inlined yet** — their Python state - is held inside opaque factory closures on the Rust side. Pre-register on - the worker. +:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With ``fork``, any +state set in the parent was visible in workers via copy-on-write; with +``forkserver`` and ``spawn`` it is not. The +:py:func:`~datafusion.ipc.set_worker_ctx` pattern works on every start +method — prefer it over relying on inherited state. + +Practical considerations +------------------------ + +* **Pickled size scales with what travels inline.** A pickled expression of + just built-ins is small (tens of bytes). An expression carrying a Python + scalar UDF is hundreds of bytes (the callable and its signature). When the + same UDF is shipped many times, pre-registering it on each worker via + :py:func:`~datafusion.ipc.set_worker_ctx` and referring to it by name + cuts the per-blob overhead. +* **Closure capture.** When a Python scalar UDF closes over surrounding + state — local variables, module-level objects, file paths — that state + is captured at pickling time. Surprises are possible if the captured + state is large, mutable, or not portable to the worker's environment. +* **Aggregate and window UDFs always travel by name.** Their Python state + is held inside opaque factory closures that cannot be reconstructed from + bytes alone. Use :py:func:`~datafusion.ipc.set_worker_ctx` to register + them on each worker. Security -------- .. warning:: - Pickle blobs containing inlined UDFs deserialize via :py:mod:`cloudpickle`, - which executes arbitrary code on the receiver. Only :py:func:`pickle.loads` - blobs from trusted sources. For untrusted-source workflows, restrict the - sender to built-in functions and pre-registered Rust-side UDFs. + Reconstructing an expression containing a Python scalar UDF executes + arbitrary Python code on the receiver. Only :py:func:`pickle.loads` + expressions from trusted sources. For untrusted-source workflows, + restrict senders to built-in functions and pre-registered Rust-side + UDFs, and never feed externally supplied bytes through + :py:func:`pickle.loads`. See also -------- -* :py:mod:`datafusion.ipc` — module-level API reference. +* :py:mod:`datafusion.ipc` — worker context API. * ``examples/ray_pickle_expr.py`` — runnable Ray actor example. diff --git a/examples/README.md b/examples/README.md index 68eeb0975..df082506c 100644 --- a/examples/README.md +++ b/examples/README.md @@ -46,7 +46,7 @@ Here is a direct link to the file used in the examples: ### Distributing DataFusion expressions -- [Pickle expressions and send them to Ray actors](./ray_pickle_expr.py) +- [Distribute expression evaluation across Ray actors](./ray_pickle_expr.py) ### Substrait Support diff --git a/examples/ray_pickle_expr.py b/examples/ray_pickle_expr.py index 74a135043..e87adf5ef 100644 --- a/examples/ray_pickle_expr.py +++ b/examples/ray_pickle_expr.py @@ -17,13 +17,11 @@ """Distribute DataFusion expressions to Ray actors. -This example shows the worker-init pattern from the user guide adapted to -Ray's actor model: each actor builds its own :class:`SessionContext`. -Python scalar UDFs travel inside the pickle blob via the Rust-side -``PythonUDFCodec`` — no actor-side pre-registration is required. The -worker context (set via :func:`datafusion.ipc.set_worker_ctx`) is still -useful for aggregate/window UDFs or other registry-only entries; we set -one here to show the pattern. +Build an expression in the driver, ship it to a pool of Ray actors, and have +each actor evaluate it against its own slice of data. Each actor sets up +its own :class:`SessionContext` once in `__init__` and registers any UDFs +it needs to resolve by name. Python scalar UDFs travel with the shipped +expression and need no actor-side pre-registration. Prerequisites: pip install ray @@ -58,8 +56,9 @@ class DataFusionWorker: def __init__(self) -> None: ctx = SessionContext() ctx.register_udf(_build_double_udf()) - # The worker context is what Expr.__setstate__ consults when - # pickled expressions arrive at this actor. + # Install the actor's SessionContext as its worker context; + # expressions reconstructed in this actor will resolve their + # by-name references against it. set_worker_ctx(ctx) self._ctx = ctx diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index f9e57c8c9..089b666f0 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -434,30 +434,32 @@ def variant_name(self) -> str: return self.expr.variant_name() def to_bytes(self, ctx: SessionContext | None = None) -> bytes: - """Serialize this expression to protobuf bytes. + """Serialize this expression to bytes for shipping to another process. - Python scalar UDFs are inlined into the returned bytes — the - receiver does not need to pre-register them. Aggregate UDFs, - window UDFs, and UDFs imported via the FFI capsule protocol - are stored by name only; the receiver must have them - registered. + Use this — or :func:`pickle.dumps` — to send an expression to a + worker process for distributed evaluation. - When ``ctx`` is supplied, encoding also routes through the - session's installed codec stack. + Built-in functions and Python scalar UDFs travel inside the + returned bytes; the worker does not need to pre-register them. + Aggregate UDFs, window UDFs, and UDFs imported via the FFI + capsule protocol travel by name only and must be registered on + the worker. See :doc:`/user-guide/io/distributing_expressions`. """ ctx_arg = ctx.ctx if ctx is not None else None return bytes(self.expr.to_bytes(ctx_arg)) @classmethod def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: - """Decode an expression from serialized protobuf bytes. - - ``ctx`` is the receiver :class:`SessionContext` used to resolve - function references not inlined by the codec (aggregate UDFs, - window UDFs, FFI UDFs). When ``ctx`` is ``None`` the worker - context set via :func:`datafusion.ipc.set_worker_ctx` is - consulted; if no worker context is set, a fresh - :class:`SessionContext` is used. + """Reconstruct an expression from serialized bytes. + + Accepts output of :meth:`to_bytes` or :func:`pickle.dumps`. + ``ctx`` is the :class:`SessionContext` used to resolve any + function references that travel by name — aggregate UDFs, window + UDFs, FFI UDFs. When ``ctx`` is ``None`` the worker context + installed via :func:`datafusion.ipc.set_worker_ctx` is consulted; + if no worker context is installed, a fresh + :class:`SessionContext` is used (sufficient for built-ins and + Python scalar UDFs). """ from datafusion.ipc import _resolve_ctx @@ -467,14 +469,12 @@ def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: def __reduce__(self) -> tuple: """Pickle protocol hook. - Python scalar UDFs referenced by the expression are inlined - into the pickle blob, so the receiver does not need to - pre-register them. On unpickle the bytes are decoded against - the worker context set via - :func:`datafusion.ipc.set_worker_ctx` (or a fresh - :class:`SessionContext` if none) for any registry-resolved - references — aggregate UDFs, window UDFs, UDFs imported via - the FFI capsule protocol. + Lets expressions be shipped to worker processes via + :func:`pickle.dumps` / :func:`pickle.loads`. The worker's + :class:`SessionContext` for resolving by-name references is + looked up via :func:`datafusion.ipc.set_worker_ctx`, falling + back to a fresh empty :class:`SessionContext` if none has been + installed on the worker. """ return (Expr._reconstruct, (self.to_bytes(),)) diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 907723ab3..5ed6553ca 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -15,12 +15,15 @@ # specific language governing permissions and limitations # under the License. -"""Inter-process communication helpers for distributing DataFusion expressions. +"""Worker-side setup for distributing DataFusion expressions. -This module provides a worker-scoped :class:`SessionContext` slot that -:meth:`Expr.__reduce__` consults when unpickling expressions across process -boundaries. Set the worker context once per worker process (typically from a -``multiprocessing.Pool`` initializer or a Ray actor ``__init__``): +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 +aggregate UDFs, window UDFs, table providers, or UDFs imported via the FFI +capsule protocol — anything the worker would otherwise resolve from its +registered functions — install a configured :class:`SessionContext` once +per worker: >>> # doctest: +SKIP >>> from datafusion import SessionContext @@ -28,15 +31,13 @@ >>> >>> def init_worker(): ... ctx = SessionContext() -... # register Rust-backed UDFs / aggregates / window functions here +... ctx.register_udaf(my_aggregate) ... set_worker_ctx(ctx) -Python scalar UDFs do not need pre-registration: their definitions -travel inside the pickled expression and are reconstructed on the -receiver automatically. The worker context is only needed when the -expression references aggregate UDFs, window UDFs, table providers, -or UDFs imported via the FFI capsule protocol — anything the -receiver would otherwise resolve from its registered functions. +Built-in functions and Python scalar UDFs travel inside the shipped +expression itself and do not need pre-registration on the worker. + +See :doc:`/user-guide/io/distributing_expressions` for the full pattern. """ from __future__ import annotations @@ -59,25 +60,30 @@ def set_worker_ctx(ctx: SessionContext) -> None: - """Register the receiver :class:`SessionContext` for this worker. - - Call once per worker process — typically from a ``Pool`` initializer or a - Ray actor ``__init__``. Idempotent: overwrites any previous value. + """Install this worker's :class:`SessionContext` for shipped expressions. - The worker context is stored in a thread-local slot, so each thread within - a worker can install its own context independently. + 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. """ _local.ctx = ctx def clear_worker_ctx() -> None: - """Remove the worker context, restoring fresh-context fallback behavior.""" + """Remove this worker's installed :class:`SessionContext`. + + After clearing, expressions reconstructed in this worker fall back to a + fresh empty :class:`SessionContext` — adequate for built-ins and Python + scalar UDFs, but anything that travels by name only (aggregate UDFs, + window UDFs, FFI UDFs) will fail to resolve. + """ if hasattr(_local, "ctx"): del _local.ctx def get_worker_ctx() -> SessionContext | None: - """Return the worker context if set, else ``None``.""" + """Return this worker's installed :class:`SessionContext`, or ``None``.""" return getattr(_local, "ctx", None) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index c1fc81651..87ca16954 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -17,10 +17,11 @@ """In-process pickle round-trip tests for :class:`Expr`. -The Rust-side ``PythonUDFCodec`` cloudpickles Python scalar UDF callables -directly into the proto wire format, so pickle blobs are self-contained. -The worker context (:mod:`datafusion.ipc`) is only needed for references -the codec can't inline — aggregate UDFs, window UDFs, FFI capsule UDFs. +Built-in functions and Python scalar UDFs travel with the pickled +expression and do not need worker-side pre-registration. The worker +context (:mod:`datafusion.ipc`) is only consulted for references that +travel by name — aggregate UDFs, window UDFs, UDFs imported via the FFI +capsule protocol. Cross-process tests live in ``test_pickle_multiprocessing.py``. """ diff --git a/python/tests/test_pickle_multiprocessing.py b/python/tests/test_pickle_multiprocessing.py index 89396de81..08a567971 100644 --- a/python/tests/test_pickle_multiprocessing.py +++ b/python/tests/test_pickle_multiprocessing.py @@ -18,11 +18,11 @@ """Cross-process pickle tests for :class:`Expr`. Workers run with each :mod:`multiprocessing` start method (``fork``, -``forkserver``, ``spawn``). Python scalar UDFs travel inside the proto blob -via the Rust-side ``PythonUDFCodec`` — no worker-side pre-registration -needed. Worker-side helpers live in ``_pickle_multiprocessing_helpers`` — -the underscore prefix avoids pytest collection so the module imports under -its real name in worker subprocesses. +``forkserver``, ``spawn``). Python scalar UDFs travel with the pickled +expression and need no worker-side pre-registration. Worker-side helpers +live in ``_pickle_multiprocessing_helpers`` — the underscore prefix +avoids pytest collection so the module imports under its real name in +worker subprocesses. """ from __future__ import annotations From 89d119f190ca0969c30b69193fee1ca516fb1301 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 16:43:35 -0400 Subject: [PATCH 07/57] feat: inline encoding for Python window UDFs Window UDFs no longer need worker-side pre-registration. The codec serializes the Python evaluator factory into the wire format and the receiver reconstructs the UDF from bytes alone, same as scalar UDFs. Refactor `MultiColumnWindowUDF` to store the Python evaluator callable directly (`evaluator: Py`) instead of a `PartitionEvaluatorFactory` closure. The factory closure was a boxed `Fn` that captured the Python state opaquely, with nothing for the codec to downcast back to. Now the named struct holds the `Py` and builds a partition evaluator inside `partition_evaluator()` on demand. `PyWindowUDF::new` constructs `MultiColumnWindowUDF` directly with the evaluator. `to_rust_partition_evaluator` is replaced by `instantiate_partition_evaluator`, called from the trait method. Codec wiring: * `crates/core/src/codec.rs` adds `try_encode_python_window_udf` / `try_decode_python_window_udf` plus the `DFPYUDW1` magic prefix. * `PythonLogicalCodec.try_encode_udwf` / `try_decode_udwf` and the matching `PythonPhysicalCodec` methods consult the helpers first and fall back to `inner` for non-Python window UDFs. Test coverage in `test_pickle_expr.py::TestWindowUDFCodec` mirrors the scalar UDF cases: self-contained blob, decode into fresh context, decode via pickle with no worker context. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 142 +++++++++++++++++++++++++++++++ crates/core/src/udwf.rs | 93 +++++++++++++------- python/tests/test_pickle_expr.py | 43 ++++++++++ 3 files changed, 248 insertions(+), 30 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 5311dfc72..a728fedf3 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -86,6 +86,7 @@ use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; use datafusion::logical_expr::{ AggregateUDF, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, TypeSignature, WindowUDF, + WindowUDFImpl, }; use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; @@ -95,6 +96,7 @@ use pyo3::prelude::*; use pyo3::types::{PyBytes, PyTuple}; use crate::udf::PythonFunctionScalarUDF; +use crate::udwf::MultiColumnWindowUDF; /// Wire-format prefix that tags a `fun_definition` payload as an /// inlined Python scalar UDF (cloudpickled tuple of name, callable, @@ -102,6 +104,11 @@ use crate::udf::PythonFunctionScalarUDF; /// the encoder and decoder cannot drift. pub(crate) const PY_SCALAR_UDF_MAGIC: &[u8] = b"DFPYUDF1"; +/// Wire-format prefix for an inlined Python window UDF (cloudpickled +/// tuple of name, evaluator factory, input schema, return type, +/// volatility). +pub(crate) const PY_WINDOW_UDF_MAGIC: &[u8] = b"DFPYUDW1"; + /// `LogicalExtensionCodec` parked on every `SessionContext`. Holds /// the Python-aware encoding hooks for logical-layer types /// (`LogicalPlan`, `Expr`) and delegates everything it does not @@ -206,10 +213,16 @@ impl LogicalExtensionCodec for PythonLogicalCodec { } fn try_encode_udwf(&self, node: &WindowUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_window_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udwf(node, buf) } fn try_decode_udwf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udwf) = try_decode_python_window_udf(buf)? { + return Ok(udwf); + } self.inner.try_decode_udwf(name, buf) } } @@ -296,10 +309,16 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { } fn try_encode_udwf(&self, node: &WindowUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_window_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udwf(node, buf) } fn try_decode_udwf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udwf) = try_decode_python_window_udf(buf)? { + return Ok(udwf); + } self.inner.try_decode_udwf(name, buf) } } @@ -471,3 +490,126 @@ fn schema_from_ipc_bytes(bytes: &[u8]) -> arrow::error::Result { let reader = StreamReader::try_new(std::io::Cursor::new(bytes), None)?; Ok(reader.schema().as_ref().clone()) } + +// ============================================================================= +// Shared Python window UDF encode / decode helpers +// +// Cloudpickle tuple shape: `(name, evaluator_factory, input_schema_bytes, +// return_schema_bytes, volatility_str)`. The evaluator factory is the +// Python callable that produces a new evaluator instance per partition. +// ============================================================================= + +pub(crate) fn try_encode_python_window_udf(node: &WindowUDF, buf: &mut Vec) -> Result { + let Some(py_udf) = node.inner().as_any().downcast_ref::() else { + return Ok(false); + }; + + Python::attach(|py| -> Result { + let bytes = encode_python_window_udf(py, py_udf) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + buf.extend_from_slice(PY_WINDOW_UDF_MAGIC); + buf.extend_from_slice(&bytes); + Ok(true) + }) +} + +pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result>> { + if buf.is_empty() || !buf.starts_with(PY_WINDOW_UDF_MAGIC) { + return Ok(None); + } + let payload = &buf[PY_WINDOW_UDF_MAGIC.len()..]; + + Python::attach(|py| -> Result>> { + let udf = decode_python_window_udf(py, payload) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + Ok(Some(Arc::new(WindowUDF::new_from_impl(udf)))) + }) +} + +fn encode_python_window_udf(py: Python<'_>, udf: &MultiColumnWindowUDF) -> PyResult> { + let cloudpickle = py.import("cloudpickle")?; + + let signature = WindowUDFImpl::signature(udf); + let input_dtypes: Vec = match &signature.type_signature { + TypeSignature::Exact(types) => types.clone(), + other => { + return Err(pyo3::exceptions::PyValueError::new_err(format!( + "MultiColumnWindowUDF expected Signature::Exact, got {other:?}" + ))); + } + }; + let input_fields: Vec = input_dtypes + .into_iter() + .enumerate() + .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) + .collect(); + let input_schema = Schema::new(input_fields); + let input_schema_bytes = schema_to_ipc_bytes(&input_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let return_schema = Schema::new(vec![Field::new("result", udf.return_type().clone(), true)]); + let return_schema_bytes = schema_to_ipc_bytes(&return_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let volatility = format!("{:?}", signature.volatility).to_lowercase(); + + let payload = PyTuple::new( + py, + [ + WindowUDFImpl::name(udf).into_pyobject(py)?.into_any(), + udf.evaluator().bind(py).clone().into_any(), + PyBytes::new(py, &input_schema_bytes).into_any(), + PyBytes::new(py, &return_schema_bytes).into_any(), + volatility.into_pyobject(py)?.into_any(), + ], + )?; + + let blob = cloudpickle.call_method1("dumps", (payload,))?; + blob.extract::>() +} + +fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult { + let cloudpickle = py.import("cloudpickle")?; + + let tuple = cloudpickle + .call_method1("loads", (PyBytes::new(py, payload),))? + .cast_into::()?; + + let name: String = tuple.get_item(0)?.extract()?; + let evaluator: Py = tuple.get_item(1)?.unbind(); + let input_schema_bytes: Vec = tuple.get_item(2)?.extract()?; + let return_schema_bytes: Vec = tuple.get_item(3)?.extract()?; + let volatility_str: String = tuple.get_item(4)?.extract()?; + + let input_schema = schema_from_ipc_bytes(&input_schema_bytes) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let input_types: Vec = input_schema + .fields() + .iter() + .map(|f| f.data_type().clone()) + .collect(); + + let return_schema = schema_from_ipc_bytes(&return_schema_bytes) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let return_type = return_schema + .fields() + .first() + .ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err( + "MultiColumnWindowUDF return schema must contain exactly one field", + ) + })? + .data_type() + .clone(); + + let volatility = datafusion_python_util::parse_volatility(&volatility_str) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + Ok(MultiColumnWindowUDF::from_parts( + name, + evaluator, + input_types, + return_type, + volatility, + )) +} diff --git a/crates/core/src/udwf.rs b/crates/core/src/udwf.rs index 1d3608ada..df4197778 100644 --- a/crates/core/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -25,10 +25,9 @@ use datafusion::arrow::datatypes::DataType; use datafusion::arrow::pyarrow::{FromPyArrow, PyArrowType, ToPyArrow}; use datafusion::error::{DataFusionError, Result}; use datafusion::logical_expr::function::{PartitionEvaluatorArgs, WindowUDFFieldArgs}; -use datafusion::logical_expr::ptr_eq::PtrEq; use datafusion::logical_expr::window_state::WindowAggState; use datafusion::logical_expr::{ - PartitionEvaluator, PartitionEvaluatorFactory, Signature, Volatility, WindowUDF, WindowUDFImpl, + PartitionEvaluator, Signature, Volatility, WindowUDF, WindowUDFImpl, }; use datafusion::scalar::ScalarValue; use datafusion_ffi::udwf::FFI_WindowUDF; @@ -198,15 +197,13 @@ impl PartitionEvaluator for RustPartitionEvaluator { } } -pub fn to_rust_partition_evaluator(evaluator: Py) -> PartitionEvaluatorFactory { - Arc::new(move || -> Result> { - let evaluator = Python::attach(|py| { - evaluator - .call0(py) - .map_err(|e| DataFusionError::Execution(e.to_string())) - })?; - Ok(Box::new(RustPartitionEvaluator::new(evaluator))) - }) +fn instantiate_partition_evaluator(evaluator: &Py) -> Result> { + let instance = Python::attach(|py| { + evaluator + .call0(py) + .map_err(|e| DataFusionError::Execution(e.to_string())) + })?; + Ok(Box::new(RustPartitionEvaluator::new(instance))) } /// Represents an WindowUDF @@ -234,14 +231,14 @@ impl PyWindowUDF { volatility: &str, ) -> PyResult { let return_type = return_type.0; - let input_types = input_types.into_iter().map(|t| t.0).collect(); + let input_types: Vec = input_types.into_iter().map(|t| t.0).collect(); let function = WindowUDF::from(MultiColumnWindowUDF::new( name, + evaluator, input_types, return_type, parse_volatility(volatility)?, - to_rust_partition_evaluator(evaluator), )); Ok(Self { function }) } @@ -278,42 +275,79 @@ impl PyWindowUDF { } } -#[derive(Hash, Eq, PartialEq)] +#[derive(Debug)] pub struct MultiColumnWindowUDF { name: String, + evaluator: Py, signature: Signature, return_type: DataType, - partition_evaluator_factory: PtrEq, -} - -impl std::fmt::Debug for MultiColumnWindowUDF { - fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result { - f.debug_struct("WindowUDF") - .field("name", &self.name) - .field("signature", &self.signature) - .field("return_type", &"") - .field("partition_evaluator_factory", &"") - .finish() - } } impl MultiColumnWindowUDF { pub fn new( name: impl Into, + evaluator: Py, input_types: Vec, return_type: DataType, volatility: Volatility, - partition_evaluator_factory: PartitionEvaluatorFactory, ) -> Self { let name = name.into(); let signature = Signature::exact(input_types, volatility); Self { name, + evaluator, signature, return_type, - partition_evaluator_factory: partition_evaluator_factory.into(), } } + + /// Stored Python callable that produces a fresh partition + /// evaluator instance per partition. Consumed by the codec to + /// cloudpickle the evaluator factory across process boundaries. + pub(crate) fn evaluator(&self) -> &Py { + &self.evaluator + } + + pub(crate) fn return_type(&self) -> &DataType { + &self.return_type + } + + pub(crate) fn from_parts( + name: String, + evaluator: Py, + input_types: Vec, + return_type: DataType, + volatility: Volatility, + ) -> Self { + Self::new(name, evaluator, input_types, return_type, volatility) + } +} + +impl Eq for MultiColumnWindowUDF {} +impl PartialEq for MultiColumnWindowUDF { + fn eq(&self, other: &Self) -> bool { + self.name == other.name + && self.signature == other.signature + && self.return_type == other.return_type + && Python::attach(|py| { + self.evaluator + .bind(py) + .eq(other.evaluator.bind(py)) + .unwrap_or(false) + }) + } +} + +impl std::hash::Hash for MultiColumnWindowUDF { + fn hash(&self, state: &mut H) { + self.name.hash(state); + self.signature.hash(state); + self.return_type.hash(state); + Python::attach(|py| { + let py_hash = self.evaluator.bind(py).hash().unwrap_or(0); + state.write_isize(py_hash); + }); + } } impl WindowUDFImpl for MultiColumnWindowUDF { @@ -339,7 +373,6 @@ impl WindowUDFImpl for MultiColumnWindowUDF { &self, _partition_evaluator_args: PartitionEvaluatorArgs, ) -> Result> { - let _ = _partition_evaluator_args; - (self.partition_evaluator_factory)() + instantiate_partition_evaluator(&self.evaluator) } } diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 87ca16954..5f84a008e 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -130,6 +130,49 @@ def fn(arr): assert decoded.canonical_name() == e.canonical_name() +class TestWindowUDFCodec: + """Python window UDFs travel inline like scalar UDFs.""" + + def _build_window_udf(self): + from datafusion import udwf + from datafusion.user_defined import WindowEvaluator + + class CountUpEvaluator(WindowEvaluator): + def evaluate_all(self, values, num_rows): + return pa.array(list(range(num_rows))) + + return udwf( + CountUpEvaluator, + [pa.int64()], + pa.int64(), + "immutable", + name="count_up", + ) + + def test_window_udf_self_contained_blob(self): + u = self._build_window_udf() + e = u(col("a")) + blob = pickle.dumps(e) + assert len(blob) > 200 + + def test_window_udf_decodes_into_fresh_ctx(self): + u = self._build_window_udf() + e = u(col("a")) + blob = e.to_bytes() + fresh = SessionContext() + from datafusion import Expr + + decoded = Expr.from_bytes(blob, ctx=fresh) + assert "count_up" in decoded.canonical_name() + + def test_window_udf_decodes_via_pickle_with_no_worker_ctx(self): + u = self._build_window_udf() + e = u(col("a")) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + assert "count_up" in decoded.canonical_name() + + class TestWorkerCtxLifecycle: def test_set_and_clear(self): assert get_worker_ctx() is None From 4b51402047fcd19b9f046746517c0cadbf1fc2b2 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 16:47:42 -0400 Subject: [PATCH 08/57] feat: inline encoding for Python aggregate UDFs Aggregate UDFs no longer need worker-side pre-registration. The codec serializes the Python accumulator factory + state schema into the wire format and the receiver reconstructs the UDF from bytes alone. New `PythonFunctionAggregateUDF` named struct (in `crates/core/src/udaf.rs`) holds `accumulator: Py` plus signature, return type, and state fields directly. Full `AggregateUDFImpl` impl mirroring upstream `SimpleAggregateUDF`: `as_any`, `name`, `signature`, `return_type`, `accumulator`, `state_fields`. `accumulator()` lazily instantiates a fresh accumulator per partition via the new `instantiate_accumulator()` helper. `PyAggregateUDF::new` now constructs `PythonFunctionAggregateUDF` directly via `AggregateUDF::new_from_impl(...)` instead of routing through `create_udaf(...)` + `to_rust_accumulator(...)`. The closure- based factory path is gone; the Python state stays addressable. Codec wiring: * `crates/core/src/codec.rs` adds `try_encode_python_agg_udf` / `try_decode_python_agg_udf` plus the `DFPYUDA1` magic prefix. Tuple shape: `(name, accumulator, input_schema_bytes, return_schema_bytes, state_schema_bytes, volatility_str)`. * `PythonLogicalCodec.try_encode_udaf` / `try_decode_udaf` and the matching `PythonPhysicalCodec` methods consult the helpers first and fall back to `inner` for non-Python aggregate UDFs. Test coverage in `test_pickle_expr.py::TestAggregateUDFCodec` mirrors the scalar / window UDF cases. 1094 root tests pass (up from 1088, plus 3 new UDAF cases and 3 new UDWF cases from the prior commit). Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 168 ++++++++++++++++++++++++++++++- crates/core/src/udaf.rs | 160 ++++++++++++++++++++++++++--- python/tests/test_pickle_expr.py | 57 +++++++++++ 3 files changed, 367 insertions(+), 18 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index a728fedf3..5dc91f215 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -85,8 +85,8 @@ use datafusion::datasource::TableProvider; use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; use datafusion::logical_expr::{ - AggregateUDF, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, TypeSignature, WindowUDF, - WindowUDFImpl, + AggregateUDF, AggregateUDFImpl, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, + TypeSignature, WindowUDF, WindowUDFImpl, }; use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; @@ -95,6 +95,7 @@ use datafusion_proto::physical_plan::{DefaultPhysicalExtensionCodec, PhysicalExt use pyo3::prelude::*; use pyo3::types::{PyBytes, PyTuple}; +use crate::udaf::PythonFunctionAggregateUDF; use crate::udf::PythonFunctionScalarUDF; use crate::udwf::MultiColumnWindowUDF; @@ -104,6 +105,11 @@ use crate::udwf::MultiColumnWindowUDF; /// the encoder and decoder cannot drift. pub(crate) const PY_SCALAR_UDF_MAGIC: &[u8] = b"DFPYUDF1"; +/// Wire-format prefix for an inlined Python aggregate UDF +/// (cloudpickled tuple of name, accumulator factory, input schema, +/// return type, state types schema, volatility). +pub(crate) const PY_AGG_UDF_MAGIC: &[u8] = b"DFPYUDA1"; + /// Wire-format prefix for an inlined Python window UDF (cloudpickled /// tuple of name, evaluator factory, input schema, return type, /// volatility). @@ -205,10 +211,16 @@ impl LogicalExtensionCodec for PythonLogicalCodec { } fn try_encode_udaf(&self, node: &AggregateUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_agg_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udaf(node, buf) } fn try_decode_udaf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udaf) = try_decode_python_agg_udf(buf)? { + return Ok(udaf); + } self.inner.try_decode_udaf(name, buf) } @@ -301,10 +313,16 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { } fn try_encode_udaf(&self, node: &AggregateUDF, buf: &mut Vec) -> Result<()> { + if try_encode_python_agg_udf(node, buf)? { + return Ok(()); + } self.inner.try_encode_udaf(node, buf) } fn try_decode_udaf(&self, name: &str, buf: &[u8]) -> Result> { + if let Some(udaf) = try_decode_python_agg_udf(buf)? { + return Ok(udaf); + } self.inner.try_decode_udaf(name, buf) } @@ -613,3 +631,149 @@ fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult) -> Result { + let Some(py_udf) = node + .inner() + .as_any() + .downcast_ref::() + else { + return Ok(false); + }; + + Python::attach(|py| -> Result { + let bytes = encode_python_agg_udf(py, py_udf) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + buf.extend_from_slice(PY_AGG_UDF_MAGIC); + buf.extend_from_slice(&bytes); + Ok(true) + }) +} + +pub(crate) fn try_decode_python_agg_udf(buf: &[u8]) -> Result>> { + if buf.is_empty() || !buf.starts_with(PY_AGG_UDF_MAGIC) { + return Ok(None); + } + let payload = &buf[PY_AGG_UDF_MAGIC.len()..]; + + Python::attach(|py| -> Result>> { + let udf = decode_python_agg_udf(py, payload) + .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; + Ok(Some(Arc::new(AggregateUDF::new_from_impl(udf)))) + }) +} + +fn encode_python_agg_udf(py: Python<'_>, udf: &PythonFunctionAggregateUDF) -> PyResult> { + let cloudpickle = py.import("cloudpickle")?; + + let signature = AggregateUDFImpl::signature(udf); + let input_dtypes: Vec = match &signature.type_signature { + TypeSignature::Exact(types) => types.clone(), + other => { + return Err(pyo3::exceptions::PyValueError::new_err(format!( + "PythonFunctionAggregateUDF expected Signature::Exact, got {other:?}" + ))); + } + }; + let input_fields: Vec = input_dtypes + .into_iter() + .enumerate() + .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) + .collect(); + let input_schema_bytes = schema_to_ipc_bytes(&Schema::new(input_fields)) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let return_schema = Schema::new(vec![Field::new("result", udf.return_type().clone(), true)]); + let return_schema_bytes = schema_to_ipc_bytes(&return_schema) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let state_fields: Vec = udf + .state_fields_ref() + .iter() + .map(|f| f.as_ref().clone()) + .collect(); + let state_schema_bytes = schema_to_ipc_bytes(&Schema::new(state_fields)) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + let volatility = format!("{:?}", signature.volatility).to_lowercase(); + + let payload = PyTuple::new( + py, + [ + AggregateUDFImpl::name(udf).into_pyobject(py)?.into_any(), + udf.accumulator().bind(py).clone().into_any(), + PyBytes::new(py, &input_schema_bytes).into_any(), + PyBytes::new(py, &return_schema_bytes).into_any(), + PyBytes::new(py, &state_schema_bytes).into_any(), + volatility.into_pyobject(py)?.into_any(), + ], + )?; + + let blob = cloudpickle.call_method1("dumps", (payload,))?; + blob.extract::>() +} + +fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult { + let cloudpickle = py.import("cloudpickle")?; + + let tuple = cloudpickle + .call_method1("loads", (PyBytes::new(py, payload),))? + .cast_into::()?; + + let name: String = tuple.get_item(0)?.extract()?; + let accumulator: Py = tuple.get_item(1)?.unbind(); + let input_schema_bytes: Vec = tuple.get_item(2)?.extract()?; + let return_schema_bytes: Vec = tuple.get_item(3)?.extract()?; + let state_schema_bytes: Vec = tuple.get_item(4)?.extract()?; + let volatility_str: String = tuple.get_item(5)?.extract()?; + + let input_schema = schema_from_ipc_bytes(&input_schema_bytes) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let input_types: Vec = input_schema + .fields() + .iter() + .map(|f| f.data_type().clone()) + .collect(); + + let return_schema = schema_from_ipc_bytes(&return_schema_bytes) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let return_type = return_schema + .fields() + .first() + .ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err( + "PythonFunctionAggregateUDF return schema must contain exactly one field", + ) + })? + .data_type() + .clone(); + + let state_schema = schema_from_ipc_bytes(&state_schema_bytes) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let state_types: Vec = state_schema + .fields() + .iter() + .map(|f| f.data_type().clone()) + .collect(); + + let volatility = datafusion_python_util::parse_volatility(&volatility_str) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + + Ok(PythonFunctionAggregateUDF::from_parts( + name, + accumulator, + input_types, + return_type, + state_types, + volatility, + )) +} diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index 80ef51716..194a31c6d 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -15,16 +15,18 @@ // specific language governing permissions and limitations // under the License. +use std::any::Any; use std::ptr::NonNull; use std::sync::Arc; use datafusion::arrow::array::ArrayRef; -use datafusion::arrow::datatypes::DataType; +use datafusion::arrow::datatypes::{DataType, Field, FieldRef}; use datafusion::arrow::pyarrow::{PyArrowType, ToPyArrow}; use datafusion::common::ScalarValue; use datafusion::error::{DataFusionError, Result}; +use datafusion::logical_expr::function::{AccumulatorArgs, StateFieldsArgs}; use datafusion::logical_expr::{ - Accumulator, AccumulatorFactoryFunction, AggregateUDF, AggregateUDFImpl, create_udaf, + Accumulator, AggregateUDF, AggregateUDFImpl, Signature, Volatility, }; use datafusion_ffi::udaf::FFI_AggregateUDF; use datafusion_python_util::parse_volatility; @@ -144,15 +146,140 @@ impl Accumulator for RustAccumulator { } } -pub fn to_rust_accumulator(accum: Py) -> AccumulatorFactoryFunction { - Arc::new(move |_args| -> Result> { - let accum = Python::attach(|py| { - accum - .call0(py) - .map_err(|e| DataFusionError::Execution(format!("{e}"))) - })?; - Ok(Box::new(RustAccumulator::new(accum))) - }) +fn instantiate_accumulator(accum: &Py) -> Result> { + let instance = Python::attach(|py| { + accum + .call0(py) + .map_err(|e| DataFusionError::Execution(format!("{e}"))) + })?; + Ok(Box::new(RustAccumulator::new(instance))) +} + +/// Named-struct `AggregateUDFImpl` for Python-defined aggregate UDFs. +/// Holds the Python accumulator factory directly so the codec can +/// downcast and cloudpickle it across process boundaries. +#[derive(Debug)] +pub(crate) struct PythonFunctionAggregateUDF { + name: String, + accumulator: Py, + signature: Signature, + return_type: DataType, + state_fields: Vec, +} + +impl PythonFunctionAggregateUDF { + fn new( + name: String, + accumulator: Py, + input_types: Vec, + return_type: DataType, + state_types: Vec, + volatility: Volatility, + ) -> Self { + let signature = Signature::exact(input_types, volatility); + let state_fields = state_types + .into_iter() + .enumerate() + .map(|(i, t)| Arc::new(Field::new(format!("{i}"), t, true))) + .collect(); + Self { + name, + accumulator, + signature, + return_type, + state_fields, + } + } + + /// Stored Python callable that returns a fresh accumulator instance + /// per partition. Consumed by the codec to cloudpickle the factory + /// across process boundaries. + pub(crate) fn accumulator(&self) -> &Py { + &self.accumulator + } + + pub(crate) fn return_type(&self) -> &DataType { + &self.return_type + } + + pub(crate) fn state_fields_ref(&self) -> &[FieldRef] { + &self.state_fields + } + + pub(crate) fn from_parts( + name: String, + accumulator: Py, + input_types: Vec, + return_type: DataType, + state_types: Vec, + volatility: Volatility, + ) -> Self { + Self::new( + name, + accumulator, + input_types, + return_type, + state_types, + volatility, + ) + } +} + +impl Eq for PythonFunctionAggregateUDF {} +impl PartialEq for PythonFunctionAggregateUDF { + fn eq(&self, other: &Self) -> bool { + self.name == other.name + && self.signature == other.signature + && self.return_type == other.return_type + && self.state_fields == other.state_fields + && Python::attach(|py| { + self.accumulator + .bind(py) + .eq(other.accumulator.bind(py)) + .unwrap_or(false) + }) + } +} + +impl std::hash::Hash for PythonFunctionAggregateUDF { + fn hash(&self, state: &mut H) { + self.name.hash(state); + self.signature.hash(state); + self.return_type.hash(state); + for f in &self.state_fields { + f.hash(state); + } + Python::attach(|py| { + let py_hash = self.accumulator.bind(py).hash().unwrap_or(0); + state.write_isize(py_hash); + }); + } +} + +impl AggregateUDFImpl for PythonFunctionAggregateUDF { + fn as_any(&self) -> &dyn Any { + self + } + + fn name(&self) -> &str { + &self.name + } + + fn signature(&self) -> &Signature { + &self.signature + } + + fn return_type(&self, _arg_types: &[DataType]) -> Result { + Ok(self.return_type.clone()) + } + + fn accumulator(&self, _acc_args: AccumulatorArgs) -> Result> { + instantiate_accumulator(&self.accumulator) + } + + fn state_fields(&self, _args: StateFieldsArgs) -> Result> { + Ok(self.state_fields.clone()) + } } fn aggregate_udf_from_capsule(capsule: &Bound<'_, PyCapsule>) -> PyDataFusionResult { @@ -190,14 +317,15 @@ impl PyAggregateUDF { state_type: PyArrowType>, volatility: &str, ) -> PyResult { - let function = create_udaf( - name, + let py_udf = PythonFunctionAggregateUDF::new( + name.to_string(), + accumulator, input_type.0, - Arc::new(return_type.0), + return_type.0, + state_type.0, parse_volatility(volatility)?, - to_rust_accumulator(accumulator), - Arc::new(state_type.0), ); + let function = AggregateUDF::new_from_impl(py_udf); Ok(Self { function }) } diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 5f84a008e..1b7d0b469 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -130,6 +130,63 @@ def fn(arr): assert decoded.canonical_name() == e.canonical_name() +class TestAggregateUDFCodec: + """Python aggregate UDFs travel inline like scalar UDFs.""" + + def _build_aggregate_udf(self): + from datafusion import udaf + from datafusion.user_defined import Accumulator + + class CountAcc(Accumulator): + def __init__(self): + self._count = 0 + + def state(self): + return [pa.scalar(self._count, type=pa.int64())] + + def update(self, values): + self._count += len(values) + + def merge(self, states): + for s in states: + self._count += s[0].as_py() + + def evaluate(self): + return pa.scalar(self._count, type=pa.int64()) + + return udaf( + CountAcc, + [pa.int64()], + pa.int64(), + [pa.int64()], + "immutable", + name="count_all", + ) + + def test_agg_udf_self_contained_blob(self): + u = self._build_aggregate_udf() + e = u(col("a")) + blob = pickle.dumps(e) + assert len(blob) > 200 + + def test_agg_udf_decodes_into_fresh_ctx(self): + u = self._build_aggregate_udf() + e = u(col("a")) + blob = e.to_bytes() + fresh = SessionContext() + from datafusion import Expr + + decoded = Expr.from_bytes(blob, ctx=fresh) + assert "count_all" in decoded.canonical_name() + + def test_agg_udf_decodes_via_pickle_with_no_worker_ctx(self): + u = self._build_aggregate_udf() + e = u(col("a")) + blob = pickle.dumps(e) + decoded = pickle.loads(blob) + assert "count_all" in decoded.canonical_name() + + class TestWindowUDFCodec: """Python window UDFs travel inline like scalar UDFs.""" From 71760c58fdddaef39ddb065844b57860ffbf7d88 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 16:49:27 -0400 Subject: [PATCH 09/57] docs: extend the inline-UDF guarantee to aggregate + window UDFs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit With aggregate UDFs and window UDFs now reconstructable from bytes alone, the user-facing contract simplifies to: * Built-in functions and **all** Python UDFs (scalar, aggregate, window) travel inside the shipped expression. No worker-side pre-registration. * Only UDFs imported via the FFI capsule protocol travel by name and require pre-registration via `set_worker_ctx`. Update each user-facing surface: * `docs/source/user-guide/io/distributing_expressions.rst` — drop the "aggregate/window UDFs travel by name only" caveat; rename the practical-considerations entry that called out the limitation. * `python/datafusion/ipc.py` module + `clear_worker_ctx` — explicitly list scalar, aggregate, and window as inline-portable. * `python/datafusion/expr.py` — `to_bytes` and `__reduce__` docstrings updated. * Test module docstrings updated. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../io/distributing_expressions.rst | 62 +++++++++---------- python/datafusion/expr.py | 16 ++--- python/datafusion/ipc.py | 19 +++--- python/tests/test_pickle_expr.py | 9 ++- python/tests/test_pickle_multiprocessing.py | 10 +-- 5 files changed, 59 insertions(+), 57 deletions(-) diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst index 77f24740c..fa6e11675 100644 --- a/docs/source/user-guide/io/distributing_expressions.rst +++ b/docs/source/user-guide/io/distributing_expressions.rst @@ -69,20 +69,25 @@ What travels with the expression * **Built-in functions** (``abs``, ``length``, arithmetic, comparisons, etc.) — fully portable. Worker needs nothing pre-registered. -* **Python scalar UDFs** (defined with :py:func:`datafusion.udf`) — fully - portable. The callable and its signature travel inside the pickled bytes - and are reconstructed on the worker automatically. -* **Aggregate UDFs**, **window UDFs**, **UDFs imported via the FFI capsule - protocol** — travel **by name only**. The worker must already have a - matching registration on its :py:class:`SessionContext`. Without that - registration, evaluation raises an error. +* **Python UDFs** — fully portable. The callable, its signature, and any + state captured in closures travel inside the pickled bytes and are + reconstructed on the worker automatically. Applies equally to: + + * **scalar UDFs** (:py:func:`datafusion.udf`) + * **aggregate UDFs** (:py:func:`datafusion.udaf`) + * **window UDFs** (:py:func:`datafusion.udwf`) +* **UDFs imported via the FFI capsule protocol** — travel **by name only**. + The worker must already have a matching registration on its + :py:class:`SessionContext`. Without that registration, evaluation raises + an error. Registering shared UDFs on workers ---------------------------------- -When an expression references something that travels by name only (aggregate -UDF, window UDF, FFI UDF), set up the worker's :py:class:`SessionContext` -once per process and install it as the *worker context*: +When an expression references an FFI capsule UDF (or any UDF the worker +must resolve from its registered functions), set up the worker's +:py:class:`SessionContext` once per process and install it as the +*worker context*: .. code-block:: python @@ -92,7 +97,7 @@ once per process and install it as the *worker context*: def init_worker(): ctx = SessionContext() - ctx.register_udaf(my_aggregate) + ctx.register_udaf(my_ffi_aggregate) set_worker_ctx(ctx) @@ -104,8 +109,8 @@ once per process and install it as the *worker context*: Inside a worker, expressions reconstructed by :py:func:`pickle.loads` resolve their by-name references against the installed worker context. If no worker context is installed, a fresh empty :py:class:`SessionContext` is used — -fine for expressions that only reference built-ins and Python scalar UDFs, -but anything by-name-only will fail to resolve. +fine for expressions that only reference built-ins and Python UDFs, but +FFI-capsule-backed registrations will fail to resolve. Python 3.14 default change -------------------------- @@ -122,30 +127,25 @@ Practical considerations * **Pickled size scales with what travels inline.** A pickled expression of just built-ins is small (tens of bytes). An expression carrying a Python - scalar UDF is hundreds of bytes (the callable and its signature). When the - same UDF is shipped many times, pre-registering it on each worker via - :py:func:`~datafusion.ipc.set_worker_ctx` and referring to it by name - cuts the per-blob overhead. -* **Closure capture.** When a Python scalar UDF closes over surrounding - state — local variables, module-level objects, file paths — that state - is captured at pickling time. Surprises are possible if the captured - state is large, mutable, or not portable to the worker's environment. -* **Aggregate and window UDFs always travel by name.** Their Python state - is held inside opaque factory closures that cannot be reconstructed from - bytes alone. Use :py:func:`~datafusion.ipc.set_worker_ctx` to register - them on each worker. + UDF is hundreds of bytes (the callable and its signature). When the same + UDF is shipped many times, registering an equivalent FFI-capsule UDF on + each worker via :py:func:`~datafusion.ipc.set_worker_ctx` and referring + to it by name cuts the per-blob overhead. +* **Closure capture.** When a Python UDF closes over surrounding state — + local variables, module-level objects, file paths — that state is + captured at pickling time. Surprises are possible if the captured state + is large, mutable, or not portable to the worker's environment. Security -------- .. warning:: - Reconstructing an expression containing a Python scalar UDF executes - arbitrary Python code on the receiver. Only :py:func:`pickle.loads` - expressions from trusted sources. For untrusted-source workflows, - restrict senders to built-in functions and pre-registered Rust-side - UDFs, and never feed externally supplied bytes through - :py:func:`pickle.loads`. + Reconstructing an expression containing a Python UDF executes arbitrary + Python code on the receiver. Only :py:func:`pickle.loads` expressions + from trusted sources. For untrusted-source workflows, restrict senders + to built-in functions and pre-registered Rust-side UDFs, and never feed + externally supplied bytes through :py:func:`pickle.loads`. See also -------- diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 089b666f0..47032bdf8 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -439,11 +439,11 @@ def to_bytes(self, ctx: SessionContext | None = None) -> bytes: Use this — or :func:`pickle.dumps` — to send an expression to a worker process for distributed evaluation. - Built-in functions and Python scalar UDFs travel inside the - returned bytes; the worker does not need to pre-register them. - Aggregate UDFs, window UDFs, and UDFs imported via the FFI - capsule protocol travel by name only and must be registered on - the worker. See :doc:`/user-guide/io/distributing_expressions`. + Built-in functions and Python UDFs (scalar, aggregate, window) + travel inside the returned bytes; the worker does not need to + pre-register them. UDFs imported via the FFI capsule protocol + travel by name only and must be registered on the worker. See + :doc:`/user-guide/io/distributing_expressions`. """ ctx_arg = ctx.ctx if ctx is not None else None return bytes(self.expr.to_bytes(ctx_arg)) @@ -470,8 +470,10 @@ def __reduce__(self) -> tuple: """Pickle protocol hook. Lets expressions be shipped to worker processes via - :func:`pickle.dumps` / :func:`pickle.loads`. The worker's - :class:`SessionContext` for resolving by-name references is + :func:`pickle.dumps` / :func:`pickle.loads`. Built-in functions + and Python UDFs travel inside the pickle bytes; only FFI-capsule + UDFs require pre-registration on the worker. The worker's + :class:`SessionContext` for resolving those references is looked up via :func:`datafusion.ipc.set_worker_ctx`, falling back to a fresh empty :class:`SessionContext` if none has been installed on the worker. diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 5ed6553ca..365efa700 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -20,10 +20,10 @@ 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 -aggregate UDFs, window UDFs, table providers, or UDFs imported via the FFI -capsule protocol — anything the worker would otherwise resolve from its -registered functions — install a configured :class:`SessionContext` once -per worker: +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: >>> # doctest: +SKIP >>> from datafusion import SessionContext @@ -31,11 +31,12 @@ >>> >>> def init_worker(): ... ctx = SessionContext() -... ctx.register_udaf(my_aggregate) +... ctx.register_udaf(my_ffi_aggregate) ... set_worker_ctx(ctx) -Built-in functions and Python scalar UDFs travel inside the shipped -expression itself and do not need pre-registration on the worker. +Built-in functions and Python UDFs (scalar, aggregate, window) travel +inside the shipped expression itself and do not need pre-registration +on the worker. See :doc:`/user-guide/io/distributing_expressions` for the full pattern. """ @@ -75,8 +76,8 @@ def clear_worker_ctx() -> None: After clearing, expressions reconstructed in this worker fall back to a fresh empty :class:`SessionContext` — adequate for built-ins and Python - scalar UDFs, but anything that travels by name only (aggregate UDFs, - window UDFs, FFI UDFs) will fail to resolve. + UDFs (scalar, aggregate, window), but anything imported via the FFI + capsule protocol will fail to resolve. """ if hasattr(_local, "ctx"): del _local.ctx diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 1b7d0b469..8d3d033e1 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -17,11 +17,10 @@ """In-process pickle round-trip tests for :class:`Expr`. -Built-in functions and Python scalar UDFs travel with the pickled -expression and do not need worker-side pre-registration. The worker -context (:mod:`datafusion.ipc`) is only consulted for references that -travel by name — aggregate UDFs, window UDFs, UDFs imported via the FFI -capsule protocol. +Built-in functions and Python UDFs (scalar, aggregate, window) travel +with the pickled expression and do not need worker-side pre-registration. +The worker context (:mod:`datafusion.ipc`) is only consulted for UDFs +imported via the FFI capsule protocol. Cross-process tests live in ``test_pickle_multiprocessing.py``. """ diff --git a/python/tests/test_pickle_multiprocessing.py b/python/tests/test_pickle_multiprocessing.py index 08a567971..043634c25 100644 --- a/python/tests/test_pickle_multiprocessing.py +++ b/python/tests/test_pickle_multiprocessing.py @@ -18,11 +18,11 @@ """Cross-process pickle tests for :class:`Expr`. Workers run with each :mod:`multiprocessing` start method (``fork``, -``forkserver``, ``spawn``). Python scalar UDFs travel with the pickled -expression and need no worker-side pre-registration. Worker-side helpers -live in ``_pickle_multiprocessing_helpers`` — the underscore prefix -avoids pytest collection so the module imports under its real name in -worker subprocesses. +``forkserver``, ``spawn``). Python UDFs (scalar, aggregate, window) travel +with the pickled expression and need no worker-side pre-registration. +Worker-side helpers live in ``_pickle_multiprocessing_helpers`` — the +underscore prefix avoids pytest collection so the module imports under +its real name in worker subprocesses. """ from __future__ import annotations From 9b4bbd6d6f7bc3222363d41275e88f9ec380784c Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 17:02:05 -0400 Subject: [PATCH 10/57] docs(distributing-expressions): link to pickle docs, generalize UDF kinds Two fixes in the intro paragraph: * Link to the standard library pickle docs rather than relying on the reader's familiarity with `pickle.dumps` / `pickle.loads`. * "Python scalar UDFs ride along" only covered scalar UDFs. With aggregate and window UDFs now also traveling inline, the line is reworded to call out all three kinds. Also updates the inline code comment in the worker-pool example. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/source/user-guide/io/distributing_expressions.rst | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst index fa6e11675..635869d60 100644 --- a/docs/source/user-guide/io/distributing_expressions.rst +++ b/docs/source/user-guide/io/distributing_expressions.rst @@ -25,8 +25,10 @@ framework with a per-worker initialization hook), and have each worker evaluate the expression against its own slice of data. DataFusion expressions support this directly: they can be sent through -:py:mod:`pickle` like any other Python object. Python scalar UDFs ride along -inside the pickled bytes — the receiver does not need to pre-register them. +Python's standard `pickle `_ +module like any other Python object. Python UDFs — scalar, aggregate, and +window — travel inside the pickled bytes; the receiver does not need to +pre-register them. Basic worker-pool example ------------------------- @@ -42,7 +44,7 @@ Basic worker-pool example def evaluate(blob_and_batch): blob, batch = blob_and_batch - expr = pickle.loads(blob) # Python scalar UDFs ride along inline. + expr = pickle.loads(blob) # Python UDFs travel inside the bytes. ctx = SessionContext() df = ctx.from_pydict({"a": batch}) return df.with_column("result", expr).select("result").to_pydict()["result"] From ca778102df13338a9910e691735edd60492cf3f9 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Thu, 14 May 2026 17:07:51 -0400 Subject: [PATCH 11/57] docs: drop manual pickle.dumps/loads from worker examples MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The Pool / Ray-actor examples called `pickle.dumps` on the sender and `pickle.loads` on the worker explicitly. That's not what real user code looks like — `multiprocessing.Pool.starmap`, Ray's `@ray.remote`, and similar frameworks serialize their function arguments automatically. Showing the manual wrapping makes the API look more involved than it is and obscures the point: users hand a DataFusion `Expr` to their distribution framework like any other Python object, and it Just Works. Rewrites: * User guide worker-pool example switches from `pool.map(evaluate, [(blob, batch), ...])` (where `blob = pickle.dumps(expr)`) to `pool.starmap(evaluate, [(expr, batch), ...])`. `evaluate(expr, batch)` receives the reconstructed expression directly. * Ray example drops the `pickle.dumps(expr)` / `pickle.loads(blob)` pair; `evaluate(expr, batch)` takes a typed `Expr`. Drops the unused `pickle` import. * Worker-context narrative updated: "expressions reconstructed by pickle.loads" -> "expressions arriving from the driver". * Security warning reworded to mention pickle as the underlying mechanism while still framing the contract in user terms (only accept expressions from trusted sources). Co-Authored-By: Claude Opus 4.7 (1M context) --- .../io/distributing_expressions.rst | 39 ++++++++++--------- examples/ray_pickle_expr.py | 15 +++---- 2 files changed, 27 insertions(+), 27 deletions(-) diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst index 635869d60..e07b3b008 100644 --- a/docs/source/user-guide/io/distributing_expressions.rst +++ b/docs/source/user-guide/io/distributing_expressions.rst @@ -24,11 +24,15 @@ worker processes (``multiprocessing.Pool``, a Ray actor pool, or any other framework with a per-worker initialization hook), and have each worker evaluate the expression against its own slice of data. -DataFusion expressions support this directly: they can be sent through -Python's standard `pickle `_ -module like any other Python object. Python UDFs — scalar, aggregate, and -window — travel inside the pickled bytes; the receiver does not need to -pre-register them. +DataFusion expressions support this directly: pass one to a worker +process and Python's standard +`pickle `_ machinery +serializes it transparently — the same machinery +:py:meth:`multiprocessing.pool.Pool.map`, Ray's +``@ray.remote``, and similar libraries already use to ship function +arguments. Python UDFs — scalar, aggregate, and window — travel inside +the serialized expression; the receiver does not need to pre-register +them. Basic worker-pool example ------------------------- @@ -36,15 +40,14 @@ Basic worker-pool example .. code-block:: python import multiprocessing as mp - import pickle import pyarrow as pa from datafusion import SessionContext, col, udf - def evaluate(blob_and_batch): - blob, batch = blob_and_batch - expr = pickle.loads(blob) # Python UDFs travel inside the bytes. + def evaluate(expr, batch): + # `expr` arrived here via the pool's automatic pickling — + # no manual serialization needed in user code. ctx = SessionContext() df = ctx.from_pydict({"a": batch}) return df.with_column("result", expr).select("result").to_pydict()["result"] @@ -55,13 +58,13 @@ Basic worker-pool example lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), [pa.int64()], pa.int64(), volatility="immutable", name="double", ) - blob = pickle.dumps(double(col("a"))) + expr = double(col("a")) mp_ctx = mp.get_context("forkserver") with mp_ctx.Pool(processes=4) as pool: - results = pool.map( + results = pool.starmap( evaluate, - [(blob, [1, 2, 3]), (blob, [10, 20, 30])], + [(expr, [1, 2, 3]), (expr, [10, 20, 30])], ) print(results) # [[2, 4, 6], [20, 40, 60]] @@ -108,8 +111,8 @@ must resolve from its registered functions), set up the worker's ) as pool: ... -Inside a worker, expressions reconstructed by :py:func:`pickle.loads` resolve -their by-name references against the installed worker context. If no worker +Inside a worker, expressions arriving from the driver resolve their +by-name references against the installed worker context. If no worker context is installed, a fresh empty :py:class:`SessionContext` is used — fine for expressions that only reference built-ins and Python UDFs, but FFI-capsule-backed registrations will fail to resolve. @@ -144,10 +147,10 @@ Security .. warning:: Reconstructing an expression containing a Python UDF executes arbitrary - Python code on the receiver. Only :py:func:`pickle.loads` expressions - from trusted sources. For untrusted-source workflows, restrict senders - to built-in functions and pre-registered Rust-side UDFs, and never feed - externally supplied bytes through :py:func:`pickle.loads`. + Python code on the receiver — pickle is doing the work under the hood + and pickle is unsafe on untrusted input. Only accept expressions from + trusted sources. For untrusted-source workflows, restrict senders to + built-in functions and pre-registered Rust-side UDFs. See also -------- diff --git a/examples/ray_pickle_expr.py b/examples/ray_pickle_expr.py index e87adf5ef..9123e4cfa 100644 --- a/examples/ray_pickle_expr.py +++ b/examples/ray_pickle_expr.py @@ -30,11 +30,9 @@ python examples/ray_pickle_expr.py """ -import pickle - import pyarrow as pa import ray -from datafusion import SessionContext, col, lit, udf +from datafusion import Expr, SessionContext, col, lit, udf from datafusion.ipc import set_worker_ctx @@ -62,9 +60,10 @@ def __init__(self) -> None: set_worker_ctx(ctx) self._ctx = ctx - def evaluate(self, expr_blob: bytes, batch_pylist: list[int]) -> list[int]: - """Unpickle an Expr, run it over an in-memory batch, return results.""" - expr = pickle.loads(expr_blob) + def evaluate(self, expr: Expr, batch_pylist: list[int]) -> list[int]: + """Run the expression against an in-memory batch.""" + # `expr` arrived here via Ray's automatic argument serialization — + # no manual pickle handling needed in user code. df = self._ctx.from_pydict({"a": batch_pylist}) out = df.with_column("result", expr).select("result") return out.to_pydict()["result"] @@ -76,13 +75,11 @@ def main() -> None: sender = SessionContext() sender.register_udf(_build_double_udf()) expr = _build_double_udf()(col("a")) + lit(1) - blob = pickle.dumps(expr) - print(f"pickled expression: {len(blob)} bytes") workers = [DataFusionWorker.remote() for _ in range(2)] batches = [[1, 2, 3], [10, 20, 30], [100, 200, 300]] futures = [ - workers[i % len(workers)].evaluate.remote(blob, batch) + workers[i % len(workers)].evaluate.remote(expr, batch) for i, batch in enumerate(batches) ] for batch, result in zip(batches, ray.get(futures), strict=True): From ba5edf257b47b80c2bdf74ec85cf8afe3d45f9be Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 06:10:42 -0400 Subject: [PATCH 12/57] docs: restructure distribution page for the multiple-approach story MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The page previously framed itself entirely around expression-level distribution. With datafusion-distributed and datafusion-ballista integrations in progress upstream, an overview of *all* distribution approaches is more durable: it establishes the page as the distribution landing spot, sets reader expectations about what is ready today versus what is on the way, and lets the future integrations slot in without renaming or restructuring. Rename `distributing_expressions.rst` → `distributing_work.rst` and rewrite as: * Overview lead — three approaches (expression-level, query-level via datafusion-distributed, query-level via Ballista) with status markers. * "Expression-level distribution" — the existing content, slotted in as a sub-section. * "Query-level distribution via datafusion-distributed" — placeholder noting the upstream WIP and that the integration will be documented here once usable. * "Query-level distribution via Apache Ballista" — same. Cross-references in `datafusion.ipc` and `Expr.to_bytes` / `__reduce__` docstrings updated to the new doc name. TOC entry in io/index.rst updated. Filename and URL stable from here on. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../io/distributing_expressions.rst | 159 ---- .../user-guide/io/distributing_work.rst | 207 +++++ docs/source/user-guide/io/index.rst | 2 +- pickle-redesign-plan.md | 784 ++++++++++++++++++ python/datafusion/expr.py | 2 +- python/datafusion/ipc.py | 2 +- 6 files changed, 994 insertions(+), 162 deletions(-) delete mode 100644 docs/source/user-guide/io/distributing_expressions.rst create mode 100644 docs/source/user-guide/io/distributing_work.rst create mode 100644 pickle-redesign-plan.md diff --git a/docs/source/user-guide/io/distributing_expressions.rst b/docs/source/user-guide/io/distributing_expressions.rst deleted file mode 100644 index e07b3b008..000000000 --- a/docs/source/user-guide/io/distributing_expressions.rst +++ /dev/null @@ -1,159 +0,0 @@ -.. 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. - -Distributing expressions across processes -========================================= - -A common pattern is to build a DataFusion expression -(:py:class:`~datafusion.Expr`) in a driver process, hand it to a pool of -worker processes (``multiprocessing.Pool``, a Ray actor pool, or any other -framework with a per-worker initialization hook), and have each worker -evaluate the expression against its own slice of data. - -DataFusion expressions support this directly: pass one to a worker -process and Python's standard -`pickle `_ machinery -serializes it transparently — the same machinery -:py:meth:`multiprocessing.pool.Pool.map`, Ray's -``@ray.remote``, and similar libraries already use to ship function -arguments. Python UDFs — scalar, aggregate, and window — travel inside -the serialized expression; the receiver does not need to pre-register -them. - -Basic worker-pool example -------------------------- - -.. code-block:: python - - import multiprocessing as mp - - import pyarrow as pa - from datafusion import SessionContext, col, udf - - - def evaluate(expr, batch): - # `expr` arrived here via the pool's automatic pickling — - # no manual serialization needed in user code. - ctx = SessionContext() - df = ctx.from_pydict({"a": batch}) - return df.with_column("result", expr).select("result").to_pydict()["result"] - - - if __name__ == "__main__": - double = udf( - lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), - [pa.int64()], pa.int64(), volatility="immutable", name="double", - ) - expr = double(col("a")) - - mp_ctx = mp.get_context("forkserver") - with mp_ctx.Pool(processes=4) as pool: - results = pool.starmap( - evaluate, - [(expr, [1, 2, 3]), (expr, [10, 20, 30])], - ) - print(results) # [[2, 4, 6], [20, 40, 60]] - - -What travels with the expression --------------------------------- - -* **Built-in functions** (``abs``, ``length``, arithmetic, comparisons, etc.) - — fully portable. Worker needs nothing pre-registered. -* **Python UDFs** — fully portable. The callable, its signature, and any - state captured in closures travel inside the pickled bytes and are - reconstructed on the worker automatically. Applies equally to: - - * **scalar UDFs** (:py:func:`datafusion.udf`) - * **aggregate UDFs** (:py:func:`datafusion.udaf`) - * **window UDFs** (:py:func:`datafusion.udwf`) -* **UDFs imported via the FFI capsule protocol** — travel **by name only**. - The worker must already have a matching registration on its - :py:class:`SessionContext`. Without that registration, evaluation raises - an error. - -Registering shared UDFs on workers ----------------------------------- - -When an expression references an FFI capsule UDF (or any UDF the worker -must resolve from its registered functions), set up the worker's -:py:class:`SessionContext` once per process and install it as the -*worker context*: - -.. 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) - - - with mp.get_context("forkserver").Pool( - processes=4, initializer=init_worker - ) as pool: - ... - -Inside a worker, expressions arriving from the driver resolve their -by-name references against the installed worker context. If no worker -context is installed, a fresh empty :py:class:`SessionContext` is used — -fine for expressions that only reference built-ins and Python UDFs, but -FFI-capsule-backed registrations will fail to resolve. - -Python 3.14 default change --------------------------- - -Python 3.14 changed the POSIX default start method for -:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With ``fork``, any -state set in the parent was visible in workers via copy-on-write; with -``forkserver`` and ``spawn`` it is not. The -:py:func:`~datafusion.ipc.set_worker_ctx` pattern works on every start -method — prefer it over relying on inherited state. - -Practical considerations ------------------------- - -* **Pickled size scales with what travels inline.** A pickled expression of - just built-ins is small (tens of bytes). An expression carrying a Python - UDF is hundreds of bytes (the callable and its signature). When the same - UDF is shipped many times, registering an equivalent FFI-capsule UDF on - each worker via :py:func:`~datafusion.ipc.set_worker_ctx` and referring - to it by name cuts the per-blob overhead. -* **Closure capture.** When a Python UDF closes over surrounding state — - local variables, module-level objects, file paths — that state is - captured at pickling time. Surprises are possible if the captured state - is large, mutable, or not portable to the worker's environment. - -Security --------- - -.. warning:: - - Reconstructing an expression containing a Python UDF executes arbitrary - Python code on the receiver — pickle is doing the work under the hood - and pickle is unsafe on untrusted input. Only accept expressions from - trusted sources. For untrusted-source workflows, restrict senders to - built-in functions and pre-registered Rust-side UDFs. - -See also --------- - -* :py:mod:`datafusion.ipc` — worker context API. -* ``examples/ray_pickle_expr.py`` — runnable Ray actor example. diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst new file mode 100644 index 000000000..0c5009d2a --- /dev/null +++ b/docs/source/user-guide/io/distributing_work.rst @@ -0,0 +1,207 @@ +.. 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. + +Distributing DataFusion work +============================ + +Splitting a DataFusion workload across multiple processes — for +throughput, isolation, or to use a worker pool — comes in a few +different shapes depending on what is being split. + +* **Expression-level distribution** ✅ *supported today*. The driver + builds a DataFusion :py:class:`~datafusion.Expr`, sends it to + worker processes, and each worker evaluates the expression against + its own slice of data. Suits embarrassingly-parallel workloads + where the driver decides up front how to partition. +* **Query-level distribution via datafusion-distributed** 🚧 *work in + progress upstream*. A single logical / physical plan is split into + stages and run across worker nodes. The driver writes one SQL or + DataFrame query; the runtime decides partitioning. +* **Query-level distribution via Apache Ballista** 🚧 *work in + progress upstream*. Similar query-level model, with a more + cluster-management-oriented runtime. + +Only the first option is ready for use from datafusion-python today. +The other two are documented below so the surrounding story is in +one place; integration details will land here as those projects +become usable from datafusion-python. + +Expression-level distribution +----------------------------- + +DataFusion expressions support distribution directly: pass one to a +worker process and Python's standard +`pickle `_ machinery +serializes it transparently — the same machinery +:py:meth:`multiprocessing.pool.Pool.map`, Ray's ``@ray.remote``, and +similar libraries already use to ship function arguments. Python UDFs +— scalar, aggregate, and window — travel inside the serialized +expression; the receiver does not need to pre-register them. + +Basic worker-pool example +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: python + + import multiprocessing as mp + + import pyarrow as pa + from datafusion import SessionContext, col, udf + + + def evaluate(expr, batch): + # `expr` arrived here via the pool's automatic pickling — + # no manual serialization needed in user code. + ctx = SessionContext() + df = ctx.from_pydict({"a": batch}) + return df.with_column("result", expr).select("result").to_pydict()["result"] + + + if __name__ == "__main__": + double = udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], pa.int64(), volatility="immutable", name="double", + ) + expr = double(col("a")) + + mp_ctx = mp.get_context("forkserver") + with mp_ctx.Pool(processes=4) as pool: + results = pool.starmap( + evaluate, + [(expr, [1, 2, 3]), (expr, [10, 20, 30])], + ) + print(results) # [[2, 4, 6], [20, 40, 60]] + + +What travels with the expression +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +* **Built-in functions** (``abs``, ``length``, arithmetic, comparisons, + etc.) — fully portable. Worker needs nothing pre-registered. +* **Python UDFs** — fully portable. The callable, its signature, and + any state captured in closures travel inside the serialized + expression and are reconstructed on the worker automatically. + Applies equally to: + + * **scalar UDFs** (:py:func:`datafusion.udf`) + * **aggregate UDFs** (:py:func:`datafusion.udaf`) + * **window UDFs** (:py:func:`datafusion.udwf`) +* **UDFs imported via the FFI capsule protocol** — travel **by name + only**. The worker must already have a matching registration on its + :py:class:`SessionContext`. Without that registration, evaluation + raises an error. + +Registering shared UDFs on workers +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +When an expression references an FFI capsule UDF (or any UDF the +worker must resolve from its registered functions), set up the +worker's :py:class:`SessionContext` once per process and install it +as the *worker context*: + +.. 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) + + + with mp.get_context("forkserver").Pool( + processes=4, initializer=init_worker + ) as pool: + ... + +Inside a worker, expressions arriving from the driver resolve their +by-name references against the installed worker context. If no worker +context is installed, a fresh empty :py:class:`SessionContext` is +used — fine for expressions that only reference built-ins and Python +UDFs, but FFI-capsule-backed registrations will fail to resolve. + +Python 3.14 default change +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Python 3.14 changed the POSIX default start method for +:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With +``fork``, any state set in the parent was visible in workers via +copy-on-write; with ``forkserver`` and ``spawn`` it is not. The +:py:func:`~datafusion.ipc.set_worker_ctx` pattern works on every +start method — prefer it over relying on inherited state. + +Practical considerations +~~~~~~~~~~~~~~~~~~~~~~~~ + +* **Serialized size scales with what travels inline.** A serialized + expression of just built-ins is small (tens of bytes). An + expression carrying a Python UDF is hundreds of bytes (the callable + and its signature). When the same UDF is shipped many times, + registering an equivalent FFI-capsule UDF on each worker via + :py:func:`~datafusion.ipc.set_worker_ctx` and referring to it by + name cuts the per-trip overhead. +* **Closure capture.** When a Python UDF closes over surrounding + state — local variables, module-level objects, file paths — that + state is captured at serialization time. Surprises are possible if + the captured state is large, mutable, or not portable to the + worker's environment. + +Security +~~~~~~~~ + +.. warning:: + + Reconstructing an expression containing a Python UDF executes + arbitrary Python code on the receiver — pickle is doing the work + under the hood and pickle is unsafe on untrusted input. Only + accept expressions from trusted sources. For untrusted-source + workflows, restrict senders to built-in functions and + pre-registered Rust-side UDFs. + +Query-level distribution via datafusion-distributed +--------------------------------------------------- + +🚧 *Work in progress upstream — not yet usable from datafusion-python.* + +`datafusion-distributed `_ +splits a single physical plan into stages and runs each stage on a +different worker node. The driver writes a SQL or DataFrame query +once; the runtime handles partitioning, shuffles, and reassembly. + +A datafusion-python integration is in development. This section will +document the integration once it lands. In the meantime, the +expression-level approach above covers most use cases that do not +require automatic plan partitioning. + +Query-level distribution via Apache Ballista +-------------------------------------------- + +🚧 *Work in progress upstream — not yet usable from datafusion-python.* + +`Apache Ballista `_ +provides distributed query execution on top of DataFusion with a +scheduler / executor model better suited to long-lived cluster +deployments. A datafusion-python integration is on the roadmap; this +section will fill in once the integration is usable. + +See also +-------- + +* :py:mod:`datafusion.ipc` — worker context API. +* ``examples/ray_pickle_expr.py`` — runnable Ray actor example. diff --git a/docs/source/user-guide/io/index.rst b/docs/source/user-guide/io/index.rst index 3f6188829..73f9babf8 100644 --- a/docs/source/user-guide/io/index.rst +++ b/docs/source/user-guide/io/index.rst @@ -24,7 +24,7 @@ IO arrow avro csv - distributing_expressions + distributing_work json parquet table_provider diff --git a/pickle-redesign-plan.md b/pickle-redesign-plan.md new file mode 100644 index 000000000..8ded1b6f6 --- /dev/null +++ b/pickle-redesign-plan.md @@ -0,0 +1,784 @@ +# Plan: Decouple `Expr` pickle support from global `SessionContext` + +## Context + +[apache/datafusion-python#1517](https://github.com/apache/datafusion-python/pull/1517) +("Allow pickling PyExpr") adds `pickle`/`dill` support to `Expr` so that +expressions can be shipped across process boundaries — primarily for +`multiprocessing.Pool`-style fan-out within a single machine, with an eye toward +working around `datafusion-distributed` not being deployed everywhere. + +This document proposes a redesign of how `__setstate__` resolves the +reconstruction context, replacing the current process-global singleton with an +explicit, framework-agnostic worker-scoped context. The new design works +identically under `fork`, `forkserver`, `spawn`, Ray actors, and any other +worker model that exposes an initialization hook. + +The plan is staged: Phase 1 is the minimal structural change (small, low risk, +unblocks all the downstream patterns). Phase 2 is additive (cloudpickle-based +UDF inlining). Phase 3 is documentation/examples. + +--- + +## What the PR currently does + +Two structural changes from the existing commits on +`rerun-io:nick/pickle_expr`: + +1. **Mutable global context.** `datafusion-python-util` promoted its global + slot from `OnceLock>` to `RwLock>`, + exposed as `set_global_ctx` (Rust) and `SessionContext.set_as_global` + (Python). Users register UDFs on a context and call `set_as_global()` to + make those UDFs visible to subsequent pickle reconstructions. + +2. **Pickle protocol on `Expr`.** `Expr.to_bytes` / `Expr.from_bytes` were + added on top of `datafusion-proto`'s `Serializeable` trait. The Python + wrapper implements `__getstate__` (returns proto bytes) and `__setstate__` + (decodes proto bytes by looking up function references against the + *process-wide global* `SessionContext`). Built-in functions always + roundtrip; user-defined functions roundtrip when registered on a context + that has been installed via `SessionContext.set_as_global()`. + +## Why the current design is fragile + +### Coupling to start-method semantics + +`SessionContext.set_as_global()` propagates to worker processes only when those +workers inherit the parent's memory. That is the `fork` start method, and +only `fork`. + +- **fork (POSIX, pre-3.14 default):** worker is a COW clone; the global is + present in the worker because it was present in the parent at fork time. +- **forkserver (POSIX, 3.14+ default):** a long-lived helper process forks + workers. The helper started clean; module-level `set_as_global()` calls + that ran in the parent are *not* in the helper, so workers don't see them + unless setup re-runs. +- **spawn (Windows default, macOS default since 3.8, optional elsewhere):** worker + is a fresh interpreter. Module-level setup runs again in the worker; whether + `set_as_global()` ran depends on whether it sits in module-level code or in + the parent's `__main__`. + +Python 3.14 changed the POSIX default from `fork` to `forkserver`. The PR's +pattern silently degrades on the now-default start method unless the user +happens to structure their imports correctly. + +### Pickle has no context channel + +`__setstate__(state)` is invoked with exactly one positional argument: the +state blob returned by `__getstate__`. There is no idiomatic way for the +caller of `pickle.loads` to pass an explicit receiver context. The PR's +solution — consult a process-global — is the natural workaround, but it +couples deserialization to mutable ambient state in a way that: + +- Makes the same blob deserialize differently depending on what the receiver + has installed. +- Conflicts when multiple libraries try to use the global slot. +- Fails silently if a UDF name happens to collide with a different + implementation registered on the receiver. + +### Not extensible to other worker models + +Ray actors, `concurrent.futures.ProcessPoolExecutor`, Dask workers, and other +multi-process frameworks all have per-worker initialization hooks. The PR's +pattern bypasses all of them by routing through a global. The result is that +each framework needs its own integration shim instead of a single uniform API. + +--- + +## Proposed design + +### Worker-scoped context, set by an initialization hook + +Replace the "process-global ctx" lookup inside `__setstate__` with a +"worker-scoped ctx" lookup. The worker scope is populated by the framework's +worker-init hook (e.g. `Pool(initializer=...)`, Ray actor `__init__`, +`ProcessPoolExecutor(initializer=...)`). This is the pattern PySpark uses for +executor startup and Ray actors use for per-actor state. + +``` ++-- Driver process ---------+ +-- Worker process ----------+ +| | | | +| ctx = SessionContext() | pickle | init_worker(): | +| ctx.register_udf(...) | ====> | ctx = SessionContext() | +| blob = pickle.dumps(expr) | | ctx.register_udf(...) | +| | | set_worker_ctx(ctx) | +| | | | +| | | expr = pickle.loads(blob) | +| | | # reads _worker_ctx() | ++---------------------------+ +----------------------------+ +``` + +The worker scope is just a module-level (or thread-local) variable in +`datafusion`. Each worker process has its own. No fork-time copy-on-write +dependency; no module-level magic; no global mutation that can collide. + +### Envelope as the canonical wire format (Phase 2) + +For UDFs the receiver doesn't already have registered, ship them *inside the +blob* via cloudpickle. The state stored by `__getstate__` becomes: + +```python +ExprEnvelope = { + "version": int, + "proto_bytes": bytes, # existing datafusion-proto encoding + "udf_definitions": dict, # {name: cloudpickled_callable_def} for Python UDFs + "config_overrides": dict, # subset of SessionConfig that affects evaluation +} +``` + +`__setstate__` resolves UDFs in priority order: (1) worker ctx if set, (2) +envelope's `udf_definitions`, (3) fail loudly listing what was needed and +what's available. The receiver only ever constructs a *fresh local* +`SessionContext` if the user hasn't set a worker ctx — never reaches for a +global. + +### Explicit `from_bytes(buf, ctx=...)` as the canonical Python API + +The pickle protocol is the *convenience* layer. The supported, documented +path is: + +```python +env = expr.to_envelope(ctx=sender_ctx, include_udfs="referenced") +blob = cloudpickle.dumps(env) # or any serializer + +# Receiver: +env = cloudpickle.loads(blob) +expr = Expr.from_envelope(env, ctx=receiver_ctx) # explicit, no globals +``` + +`__getstate__` / `__setstate__` delegate to these methods. Users who want +explicit control bypass pickle entirely. + +--- + +## Implementation plan + +### Phase 1 — Decouple from global ctx (minimum viable change) + +Goal: stop `__setstate__` from depending on `set_as_global()`. Do not change +the wire format. Do not add cloudpickle. Just rewire where the reconstruction +ctx comes from. + +**Python side (`python/datafusion/`):** + +1. New module `python/datafusion/_ipc.py` (or `ipc.py` if public): + ```python + import threading + _local = threading.local() + + def set_worker_ctx(ctx: "SessionContext") -> None: + """Register the receiver context for Expr unpickling in this worker. + + Call once per worker process, typically from a Pool initializer or + Ray actor __init__. Idempotent; overwrites any previous value. + """ + _local.ctx = ctx + + def clear_worker_ctx() -> None: + """Remove the worker ctx, restoring fresh-context fallback behavior.""" + if hasattr(_local, "ctx"): + del _local.ctx + + def _get_worker_ctx() -> "SessionContext | None": + return getattr(_local, "ctx", None) + ``` + +2. Modify `Expr.__setstate__` to: + - Call `_get_worker_ctx()`. If set, decode against it. + - If not set, build a fresh `SessionContext()` and decode against that. + This will succeed for built-in functions and fail informatively for + unregistered UDFs. + - Do *not* read from `set_as_global` / `get_global_ctx`. + +3. Update `Expr.from_bytes` (Python wrapper) to take an explicit `ctx` + parameter. Keep backward-compatible default — if `ctx=None`, behave as + `__setstate__` does (worker ctx → fresh ctx → error). + +**Rust side (`src/`):** + +The underlying `RawExpr::from_bytes` likely already takes a `SessionContext` +parameter (this is how the current PR's global lookup wires through). Verify +this; if it does not, plumb one through. Do not introduce any new Rust-side +worker-ctx state — keep that entirely in Python. + +The existing `set_global_ctx` / `SessionContext.set_as_global` can stay for +backward compatibility but are no longer consulted during pickle +reconstruction. Add a docstring note that `set_as_global` is legacy and +should not be relied on for pickle behavior. + +**Tests:** + +Replace the existing pickle tests with a parametrized matrix over +`(serializer, ctx_strategy)`: + +| ctx_strategy | expected behavior | +|------------------------|--------------------------------------------------| +| `worker_ctx_with_udfs` | UDFs resolve, expression evaluates correctly | +| `worker_ctx_empty` | Built-ins work, UDF references raise | +| `no_worker_ctx` | Built-ins work, UDF references raise informatively | +| `global_ctx_set` | Should NOT affect outcome (asserts decoupling) | + +Add three multiprocessing tests parametrized over start methods: + +```python +@pytest.mark.parametrize("start_method", ["fork", "forkserver", "spawn"]) +def test_pickle_roundtrip_via_pool(start_method): + ctx = mp.get_context(start_method) + with ctx.Pool(initializer=init_worker, initargs=()) as pool: + results = pool.map(evaluate_one, [(expr, batch) for ...]) + assert results == expected +``` + +The worker initializer registers UDFs and calls `set_worker_ctx`. The +`init_worker` function must live in a non-test module (per the lessons learned +in commit `bdeedd7` about pytest-importlib synthetic module names — keep that +fix). + +### Phase 2 — Envelope-based UDF inlining (additive) + +Goal: support pickling expressions that reference Python UDFs the receiver +hasn't pre-registered. Sender embeds cloudpickled UDF definitions; receiver +unpacks them onto its ctx (or a fresh one) before decoding. + +**Python side:** + +1. Add `Expr.referenced_functions() -> list[str]`. Walks the expression tree, + yields names of every UDF/UDAF/UDWF/builtin referenced. May need a small + Rust-side helper that exposes the walker. + +2. Define `ExprEnvelope` as a Python dataclass (kept Python-side; the Rust + layer only deals with proto bytes): + ```python + @dataclass + class ExprEnvelope: + version: int + proto_bytes: bytes + udf_definitions: dict[str, bytes] # cloudpickle blobs + config_overrides: dict[str, str] + ``` + +3. Add `Expr.to_envelope(ctx, *, include_udfs="referenced")`: + - Walk `referenced_functions()`. + - For each name, check if it's a built-in (skip; receiver always has it). + - Otherwise look up the UDF on the sender's `ctx`, extract the underlying + Python callable + signature + volatility, cloudpickle it, store under + the name. + - Rust-only UDFs (no Python callable) cannot be inlined — store just the + name and document that the receiver must have a matching registration. + - `include_udfs="none"` skips inlining entirely (envelope carries only + proto bytes); `include_udfs="all"` walks and inlines every UDF on the + sender ctx regardless of reference. + +4. Add `Expr.from_envelope(env, ctx=None)`: + - If `ctx` is None, use `_get_worker_ctx()` or build a fresh one. + - For each `(name, blob)` in `env.udf_definitions`, cloudpickle.loads and + register on `ctx`. If `ctx` already has a UDF named `name`, raise unless + a `prefer_envelope=False` knob says otherwise (avoid silent override). + - Apply `env.config_overrides` to `ctx`. + - Decode `env.proto_bytes` against `ctx`. + +5. Rewire `__getstate__` / `__setstate__` to delegate to `to_envelope` / + `from_envelope`. The state stored by `__getstate__` is the `ExprEnvelope` + itself (which pickle/cloudpickle then serializes recursively). + +**Dependencies:** + +Add `cloudpickle` to runtime deps (not just dev). `dill` stays as optional; +the test matrix can keep covering both. + +**Tests:** + +- Lambda UDF roundtrip via envelope, no pre-registered worker ctx (envelope + is fully self-contained). +- Closure-capturing UDF roundtrip; verify captured state is reconstructed. +- Name collision between envelope UDF and worker ctx UDF — verify the + documented resolution rule (raise by default). +- Rust UDF referenced but not registered on receiver — verify clear error + message identifying which UDFs are missing. +- Cross-version cloudpickle (different Python minors) — document expected + failure mode if any. + +### Phase 3 — Documentation and integration examples + +Goal: make the new patterns discoverable and lower the integration cost for +Ray/Dask/concurrent.futures users. + +1. **User guide section** "Distributing expressions across processes": + - Recommended `Pool(initializer=...)` pattern with full example. + - Note about Python 3.14 default start method change. + - Trade-offs of envelope inlining vs pre-registered UDFs (blob size, + closure capture surprises, Rust UDFs can't be inlined). + - Security note: envelope deserialization runs cloudpickle, which + executes arbitrary code. Only deserialize blobs from trusted sources. + +2. **Ray integration example** (separate file, not a runtime dep): + ```python + import ray + from datafusion import SessionContext + from datafusion.ipc import set_worker_ctx + + @ray.remote + class DataFusionWorker: + def __init__(self, udf_defs): + ctx = SessionContext() + for name, fn in udf_defs.items(): + ctx.register_udf(name, fn) + set_worker_ctx(ctx) + self.ctx = ctx + + def evaluate(self, expr, batch): + return self.ctx.evaluate(expr, batch) + ``` + +3. **Migration note for `set_as_global`:** explain that the API is still + available but no longer affects pickle behavior. Recommend + `set_worker_ctx` for new code. Consider a `DeprecationWarning` if + `set_as_global` is called (separate decision — may break existing users + who rely on it for non-pickle reasons). + +--- + +## Open design questions + +Things worth deciding explicitly before implementation, ideally on the PR or +the linked issue: + +1. **Should `__setstate__` fall back to a fresh ctx when no worker ctx is + set, or raise immediately?** Falling back is friendlier for trivial cases + (no UDFs, just built-ins) but hides the "you forgot to set up the + worker" error in the common case. Suggest: fall back, but log a debug + message; raise only if the proto references a non-built-in UDF that + can't be resolved. + +2. **Name collision policy in `from_envelope`.** If the worker ctx has a + UDF named `foo` and the envelope also carries one named `foo`, what + wins? Suggest: raise by default with a clear error; offer + `prefer="worker" | "envelope"` parameter. Silent override is the worst + outcome. + +3. **Is `ipc` the right module name?** Alternatives: `_runtime`, `dist`, + `workers`, `transport`. Whatever is chosen should be stable since the + `set_worker_ctx` symbol becomes user-facing API. + +4. **Should `set_as_global` be deprecated?** It has uses outside pickle + (e.g. controlling the default ctx for `read_*` module helpers). Suggest: + keep without deprecation, just stop using it from pickle paths, document + the new boundary. + +5. **Cloudpickle as a runtime dep.** Adds ~50KB and a transitive dep on + nothing serious. Probably fine, but worth flagging in the PR. Alternative: + make Phase 2 require `pip install datafusion[distributed]` with + cloudpickle as an optional extra. + +6. **Should the envelope version be wire-compatible across DataFusion + releases?** The proto encoding has its own version story; the envelope + adds another layer. Suggest: explicit `version: int` field, refuse to + decode envelopes from a future major. + +--- + +## Testing strategy summary + +The test pyramid for both phases: + +- **Unit:** envelope construction/round-trip, UDF enumeration, error paths + (missing UDFs, name collisions, version mismatches). +- **Integration:** pickle roundtrip across all three start methods via + `mp.get_context(method).Pool`. Run under CI on Linux at minimum, ideally + also macOS (where `spawn` is default). +- **Negative:** ensure setting `set_as_global` does *not* affect a worker + that doesn't call `set_worker_ctx` — verifies decoupling. +- **Stress:** large expressions with many UDFs, expressions with deeply + nested case/when, expressions referencing both built-ins and UDFs. +- **Pool hang regression:** the original PR hit a deadlock when worker + processes died during unpickle due to pytest-importlib synthetic module + names. Keep `tests/_pickle_multiprocessing_helpers.py` (or equivalent + non-test module) for worker-side function definitions. Keep the + `timeout-minutes: 30` job cap as a backstop for future regressions. + +--- + +## Implementation outcome (commit `aa08438`, local branch `feat/pickle-pyexpr`) + +> **Workflow note (2026-05-14).** The work captured below was prototyped on +> `feat/pickle-pyexpr` but is *not* the merge target. We are landing the +> codec consistency work first as PR1 on a separate branch +> (`feat/proto-codecs`, off `main`), then rebasing the pickle work onto +> PR1 as PR2. See "Phasing the consistency work" below for the revised +> ordering. The text in this section describes the prototype state on +> `feat/pickle-pyexpr` and is preserved as design context for PR2. + +Phases 1 and 2 landed with a design change worth recording: instead of a +Python-side `ExprEnvelope` dataclass, Python scalar UDFs are serialized +directly into the proto wire format by a new Rust-side +`LogicalExtensionCodec` (`crates/core/src/codec.rs`). The codec's +`try_encode_udf` downcasts the DataFusion `ScalarUDF` to the private +`PythonFunctionScalarUDF` struct, cloudpickles +`(name, callable, input_schema, return_field, volatility)` into the +proto `fun_definition` field, and tags the payload with the `DFPYUDF1` +magic prefix. `try_decode_udf` reverses it. Single self-contained wire +format — no two-layer envelope, no Python-side UDF registry on +`SessionContext`. + +`Expr.__reduce__` simply calls `to_bytes` / `from_bytes`, both of which +route through the codec. The worker-scoped ctx +(`datafusion.ipc.set_worker_ctx`) is still consulted on decode for +references the codec can't inline — currently aggregate UDFs, window +UDFs, and FFI capsule UDFs. + +UDAF / UDWF inlining was descoped: their Python state is held inside +opaque factory closures on the Rust side, and the codec needs a +downcastable named struct to extract the callable. A future change can +refactor `RustAccumulator` / `MultiColumnWindowUDF` to expose the same +shape as `PythonFunctionScalarUDF` and extend the codec. + +> **Naming note.** The committed code uses `PythonUDFCodec` as the +> Rust struct name. The consistency proposal below renames it to +> `PythonLogicalCodec` — see [R1]. + +--- + +## Phase 4 — Cross-cutting consistency with other serialization paths + +`feat/pickle-pyexpr` adds proto-based serialization to `Expr`. The +codebase already has, or has WIP for, two other serialization flows: + +1. **`PyLogicalPlan.to_proto` / `from_proto`** on `main` + (`crates/core/src/sql/logical.rs`) — hardcodes + `DefaultLogicalExtensionCodec`. +2. **`PyExecutionPlan.to_proto` / `from_proto`** plus module-level + `serialize_execution_plan` / `deserialize_execution_plan` / + `serialize_physical_expr` / `deserialize_physical_expr` — on the WIP + branch `poc_ffi_query_planner` (`src/physical_plan.rs`). Takes a + codec PyCapsule and a `task_ctx_provider` PyCapsule. + +Without a deliberate plan these flows will drift further apart. This +section proposes a consistency target so future merges (`Expr` pickle +landed, `LogicalPlan` codec stack, eventual POC merge) produce a +uniform API. + +### Observed inconsistencies + +| Concern | `LogicalPlan` (main) | `Expr` (this branch) | `ExecutionPlan` / `PhysicalExpr` (POC) | +| ------------------- | -------------------- | -------------------- | -------------------------------------- | +| Codec source | Hardcoded default | Hardcoded `PythonUDFCodec` | Explicit codec PyCapsule per call | +| Method name | `to_proto` / `from_proto` | `to_bytes` / `from_bytes` | `to_proto` / `from_proto` + module-level `serialize_*` / `deserialize_*` | +| Decoder input | `PySessionContext` | `PySessionContext` | `task_ctx_provider` PyCapsule | +| PyCapsule protocol on input | None | None | Accepts both typed class + capsule-protocol objects | +| Reads `SessionContext.logical_codec` | No | No | N/A (physical codec is separate) | + +`SessionContext` already stores +`logical_codec: Arc` +(`crates/core/src/context.rs:368`) and exposes +`with_logical_extension_codec` plus `__datafusion_logical_extension_codec__` +getter. Nothing currently consults it for proto serialization — it is +only used to plumb codecs through the catalog/table FFI surface. That +field is the obvious home for the codec the recommendations below +prescribe. + +### Recommendations + +**R1. Rename `PythonUDFCodec` → `PythonLogicalCodec`; make it composable.** + +The codec layer is *Python-side logical-layer extensions*, not just +scalar UDFs. Today it handles `try_encode_udf` / `try_decode_udf`; a +future patch adds UDAF/UDWF handling. Reserve the broader name now to +avoid renaming churn: + +```rust +pub struct PythonLogicalCodec { + /// Fallback for anything this codec does not handle directly: + /// non-Python ScalarUDFs, UDAFs, UDWFs, table providers, + /// extension nodes, etc. Typically supplied by the user via + /// `SessionContext.with_logical_extension_codec(...)`. Defaults + /// to `DefaultLogicalExtensionCodec` when no user codec is set. + inner: Arc, +} + +impl PythonLogicalCodec { + pub fn new(inner: Arc) -> Self { + Self { inner } + } + + pub fn with_default_inner() -> Self { + Self::new(Arc::new(DefaultLogicalExtensionCodec {})) + } +} +``` + +Dispatch story (already implemented for `try_decode_udf`; generalize): + +- **encode** — Downcast to the named Python impl struct + (`PythonFunctionScalarUDF` today, `PythonAggregateUDF` / + `PythonWindowUDF` later). On success: write magic-prefixed + cloudpickle payload. On failure: delegate to `self.inner.try_encode_*` + so user-supplied FFI codecs can handle their own types. +- **decode** — Check magic prefix: + - `DFPYUDF1` → scalar UDF cloudpickle path + - `DFPYUDA1` → aggregate UDF cloudpickle path (future) + - `DFPYUDW1` → window UDF cloudpickle path (future) + - anything else (including empty buf) → delegate to + `self.inner.try_decode_*`, which either handles a user FFI payload + or returns `not_impl_err` causing the caller (`parse_expr`) to fall + back to the receiver's `FunctionRegistry`. + +This gives a strict precedence: **Python inline → user FFI codec → +registry lookup**, decided per-payload by the magic prefix, with no +ambiguity about which path produced a given blob. + +**R2. One codec lives on `SessionContext`; every serializer reads it.** + +`SessionContext` already stores `logical_codec`. Wrap that field so the +session-attached codec *is* a `PythonLogicalCodec` whose inner is the +user-supplied FFI codec (or `Default` when none). Constructor sketch +(adapter glue elided — `FFI_LogicalExtensionCodec` already implements +`LogicalExtensionCodec`): + +```rust +let inner: Arc = user_codec + .map(|c| c as Arc) + .unwrap_or_else(|| Arc::new(DefaultLogicalExtensionCodec {})); +let logical_codec = Arc::new(PythonLogicalCodec::new(inner)); +``` + +`with_logical_extension_codec(new_inner)` rebuilds the +`PythonLogicalCodec` with the new inner. `PyExpr::to_bytes` / +`from_bytes` (this branch) drop their hardcoded +`PythonUDFCodec::new()` and read `session.logical_codec` instead. +`PyLogicalPlan::to_proto` / `from_proto` switch from hardcoded +`DefaultLogicalExtensionCodec` to the same field. Default behavior +unchanged for users who never call `with_logical_extension_codec`. + +Same pattern on the physical side: introduce +`PythonPhysicalCodec` (mirror of `PythonLogicalCodec`, also +composable), park it on `SessionContext` as `physical_codec`, have +`PyExecutionPlan` / future `PyPhysicalExpr` consult it. + +**R3. Single naming convention: `to_bytes` / `from_bytes` on the class.** + +Choose one pair. Recommend `to_bytes` / `from_bytes` because: +- This branch already uses it for `Expr`. +- The proto encoding is the *byte* representation, not the proto + object; `to_proto` was a slight misnomer on `LogicalPlan`. +- Symmetric with `Serializeable::{to_bytes, from_bytes_with_registry}` + upstream. + +Rename `PyLogicalPlan::to_proto` → `to_bytes` and `from_proto` → +`from_bytes` as a follow-up. Keep `to_proto` / `from_proto` as +deprecated aliases for at least one release. POC's module-level +`serialize_execution_plan` / `deserialize_execution_plan` collapse +into `PyExecutionPlan::to_bytes` / `from_bytes`; the FFI-bridging +PyCapsule-input variants stay as low-level helpers in the +`physical_plan` submodule for external Rust consumers (rename to +clarify their role, e.g. `bytes_from_capsule_plan`). + +**R4. Decoders take `SessionContext`, not raw PyCapsule plumbing.** + +The public Python API for every decoder accepts a `SessionContext`. +The Rust side pulls `task_ctx`, the function registry, and the codec +stack from that single argument. POC's split into +`(plan_bytes, task_ctx_provider_capsule, codec_capsule)` is FFI-glue +and should be wrapped behind `SessionContext.from_capsules(...)` for +callers who only have PyCapsules — same low-level access without +making it the default decoder shape. + +**R5. PyCapsule protocol acceptance on every serialized type.** + +POC's `PyExecutionPlan.from_pycapsule` and the dual-input +`serialize_execution_plan` pattern (extract typed class *or* capsule) +should generalize. `PyLogicalPlan`, `PyExpr` (this branch), and any +future serialized types expose `__datafusion___(py) -> PyCapsule` +getters and accept capsule-protocol objects on input. POC's +`from_pycapsule!` / `try_from_pycapsule!` macros (POC +`src/utils.rs:182–223`) move into `datafusion-python-util` and become +the canonical extractor pattern. Current main already uses them for +table/catalog providers; extend to plans/exprs. + +**R6. Wire-format magic prefix registry.** + +`PythonLogicalCodec` prepends a magic byte sequence per payload kind. +Establish a convention table: + +| Codec layer + kind | Magic prefix | Owner | +| ----------------------------- | --------------- | ----------------- | +| `PythonLogicalCodec` (scalar) | `DFPYUDF1` | this branch | +| `PythonLogicalCodec` (agg) | `DFPYUDA1` | future | +| `PythonLogicalCodec` (window) | `DFPYUDW1` | future | +| `PythonPhysicalCodec` (expr) | `DFPYPE1` | future / POC merge | +| User FFI extension codec | user-supplied | downstream crates | +| Default codec (no magic) | (none) | upstream | + +`PythonLogicalCodec` decode dispatches by prefix; unknown prefixes (or +no prefix) delegate to the configured `inner` codec. User FFI codecs +must pick non-colliding magic prefixes (recommend a `DF` namespace plus +crate-specific suffix). + +**R7. Cross-language story made explicit.** + +The pre-existing "Out of scope" note that *"the bare `to_bytes` / +`from_bytes` proto path remains for cross-language use"* is no longer +accurate: with `PythonLogicalCodec` in the default stack, blobs +containing Python scalar UDFs are not portable to non-Python decoders. +Two remediations: + +a. **Optional codec override on `to_bytes`** — + `expr.to_bytes(cross_language=True)` builds bytes using only the + inner codec (typically `DefaultLogicalExtensionCodec` or a user + FFI codec), skipping Python-side encoding. Decoder must have UDFs + pre-registered or supply a matching FFI codec. +b. **Document the trade-off** in the user guide — the default codec + stack is Python-aware; the wire format is Python-receiver-only + unless the sender opts out. + +**R8. Move helpers into shared crate.** + +POC plumbing (`from_pycapsule!`, `try_from_pycapsule!`, +`task_context_from_pycapsule`, `physical_codec_from_pycapsule`, +`validate_pycapsule`) is duplicated material. Park them in +`crates/util` (already houses `create_logical_extension_capsule`, +`ffi_logical_codec_from_pycapsule`) so every consumer pulls from one +place. + +**R9. Pickle = `to_bytes` + worker ctx, for every serialized type.** + +`Expr.__reduce__` (this branch) is the template. When `LogicalPlan` / +`ExecutionPlan` pickle support is added, they use the same shape: +`__reduce__` returns `(cls._reconstruct, (self.to_bytes(),))`; +`_reconstruct` resolves the receiver context via +`datafusion.ipc._resolve_ctx(None)` and calls `cls.from_bytes`. No +per-type envelope dataclass. The worker-ctx mechanism in +`datafusion.ipc` is reused unchanged. + +### Phasing the consistency work + +Reordered (2026-05-14) after deciding to land codec consistency *before* +pickle work, on a fresh branch off `main` (`feat/proto-codecs`). The +pickle prototype on `feat/pickle-pyexpr` (local commit `aa08438`) +rebases onto the codec PR after it merges. Two PRs: + +**PR1 — Logical + physical codec consistency (`feat/proto-codecs`).** + +1. **R1 — Introduce `PythonLogicalCodec` and `PythonPhysicalCodec`.** + Both follow the composable-inner pattern (Python-aware dispatch + first, delegate to a user-supplied inner codec, then registry + fallback). Both implement scalar-UDF inline encoding via the + shared `DFPYUDF1` magic prefix ported from `aa08438` — the + `PhysicalExtensionCodec` trait exposes `try_encode_udf` / + `try_decode_udf` (and the UDAF/UDWF variants) the same way the + logical trait does, so an `ExecutionPlan` or `PhysicalExpr` that + references a Python `ScalarUDF` round-trips only if the physical + codec inlines it too. Factor the cloudpickle tuple + `(name, callable, input_schema, return_field, volatility)` and + the magic-prefix framing into a shared helper that both codecs + call. What stays deferred on the physical side is the + `DFPYPE*` namespace: encoding for Python-defined physical + extension nodes (`ExecutionPlan` impls) and Python-defined + `PhysicalExpr` impls. No concrete Python-side physical extension + type exists today, so no prefix assignment in PR1. +2. **R2 — Park codecs on `SessionContext`.** Replace the existing + `logical_codec: Arc` field with + `Arc`; add `physical_codec: Arc`. + `with_logical_extension_codec` and a new `with_physical_extension_codec` + rebuild the wrapper with the supplied inner. Every serializer reads + from these session fields. +3. **R3 — Rename `PyLogicalPlan::to_proto` → `to_bytes`** and + `from_proto` → `from_bytes`. Keep `to_proto` / `from_proto` as + deprecated thin wrappers that emit `DeprecationWarning` and call + the new names. Targeted for removal one release after PR1. +4. **R5 — PyCapsule protocol on serialized types.** Move + `from_pycapsule!` / `try_from_pycapsule!` from POC `src/utils.rs` + into `datafusion-python-util`. `PyExecutionPlan` and `PyPhysicalExpr` + expose `__datafusion___` capsule getters and accept + capsule-protocol input on `from_pycapsule`. `PyLogicalPlan` is + excluded: `datafusion-ffi` does not expose a `FFI_LogicalPlan` + representation, so there is no stable capsule shape to publish. + Round-tripping a `LogicalPlan` across a process boundary goes + through `to_bytes` / `from_bytes` only. +5. **R6 — Magic-prefix registry doc comment** in + `crates/core/src/codec.rs`. `DFPYUDF1` in use; `DFPYUDA1`, + `DFPYUDW1`, `DFPYPE1` reserved. +6. **R8 — Shared helpers into `crates/util`.** + `task_context_from_pycapsule`, `physical_codec_from_pycapsule`, + `validate_pycapsule`, and the capsule macros land here. Core and + downstream consumers pull from one place. +7. **POC equivalent surface area.** Pull + `PyExecutionPlan::to_bytes` / `from_bytes`, `PyPhysicalExpr::to_bytes` / + `from_bytes`, and `task_ctx_provider` capsule extraction off + `poc_ffi_query_planner`. Module-level `serialize_execution_plan` / + `deserialize_execution_plan` / `serialize_physical_expr` / + `deserialize_physical_expr` collapse into the class methods; the + FFI-bridging capsule-input variants stay as low-level helpers in + the `physical_plan` submodule (rename for clarity, e.g. + `bytes_from_capsule_plan`). + +**PR2 — Pickle and worker-scoped distribution (rebase of `aa08438`).** + +1. `PyExpr::to_bytes` / `from_bytes` reading `session.logical_codec` + (no hardcoded `PythonUDFCodec`). +2. `Expr.__reduce__` + `datafusion.ipc.set_worker_ctx` worker-scoped + fallback (Phase 1 of the original plan). +3. Multiprocessing tests across `fork`, `forkserver`, `spawn`. +4. **R9 — Pickle template reused on other serialized types.** Apply + `__reduce__` returning `(cls._reconstruct, (self.to_bytes(),))` to + `PyLogicalPlan` and `PyExecutionPlan` once the pickle infra exists. +5. **R7 — `to_bytes(cross_language=True)` opt-out** for blobs that + must round-trip through non-Python decoders. + +**Deferred (separate follow-ups after PR2).** + +- **UDAF / UDWF inline encoding.** Needs `RustAccumulator` / + `MultiColumnWindowUDF` refactored to expose a downcastable named + struct. Magic prefixes `DFPYUDA1` / `DFPYUDW1` reserved. +- **`PythonPhysicalCodec` extension-node / physical-expr inline + payloads.** Scalar-UDF (and the UDAF/UDWF variants, once those + ship logical-side) round-trip through `DFPYUDF1` / + `DFPYUDA1` / `DFPYUDW1` from PR1 onward. Distinct from those: + Python-defined physical extension nodes (`ExecutionPlan` impls) + and Python-defined `PhysicalExpr` impls. Once a concrete + Python-side physical extension type exists, assign a magic + prefix from the `DFPYPE*` namespace and add encode/decode. + +--- + +## Out of scope + +Things this plan deliberately does not address — flag as follow-ups if +needed: + +- **Cross-language serialization.** Blobs produced by the default codec + stack are not cross-language because they may contain cloudpickle + payloads. The plan addresses this in Phase 4 R7 (opt-out + `to_bytes(cross_language=True)`); full cross-language interop with a + Rust-only receiver is otherwise out of scope. +- **Distributed catalog / object store / table provider replication.** + Expressions don't typically reference tables directly (that's a `LogicalPlan` + concern). If pickle support is later extended to `LogicalPlan` or + `DataFrame`, those will have their own envelope concerns. +- **Authentication of envelopes.** Cloudpickle is arbitrary-code-execution + on deserialize. If untrusted-source deserialization becomes a real use + case, add a signed-envelope variant; not part of this work. +- **Replacing `datafusion-distributed`.** This is for local multi-process + fan-out and Python-orchestrated frameworks (Ray, Dask). True distributed + execution remains datafusion-distributed's domain. + +--- + +## References + +- PR #1517: +- Issue #1520 (motivating): +- Python 3.14 multiprocessing default change: + — `forkserver` is now the + POSIX default for `ProcessPoolExecutor` and `multiprocessing`. +- `datafusion-proto` `Serializeable` trait (the underlying wire format + the envelope wraps). +- `cloudpickle`: +- Ray custom serializer API: + `ray.util.register_serializer(cls, serializer, deserializer)` +- Relevant commits on `rerun-io:nick/pickle_expr`: + - `23543fc` — `OnceLock` → `RwLock` for global ctx (subject of decoupling) + - `5796b53` — proto-based `to_bytes`/`from_bytes` and pickle hooks + (the to_bytes/from_bytes plumbing is reusable; the `__setstate__` + global-ctx lookup is what changes) + - `bdeedd7` — pytest-importlib helper-module fix (keep) + - `106ea3c` — 30-minute job timeout (keep) diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 47032bdf8..5a0b8e5fb 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -443,7 +443,7 @@ def to_bytes(self, ctx: SessionContext | None = None) -> bytes: travel inside the returned bytes; the worker does not need to pre-register them. UDFs imported via the FFI capsule protocol travel by name only and must be registered on the worker. See - :doc:`/user-guide/io/distributing_expressions`. + :doc:`/user-guide/io/distributing_work`. """ ctx_arg = ctx.ctx if ctx is not None else None return bytes(self.expr.to_bytes(ctx_arg)) diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 365efa700..33869da34 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -38,7 +38,7 @@ inside the shipped expression itself and do not need pre-registration on the worker. -See :doc:`/user-guide/io/distributing_expressions` for the full pattern. +See :doc:`/user-guide/io/distributing_work` for the full pattern. """ from __future__ import annotations From b99d9731ec9c8f9fd5985effccb10046d4d0222c Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 06:11:01 -0400 Subject: [PATCH 13/57] chore: untrack internal design doc that was committed by accident `pickle-redesign-plan.md` is an off-tree working doc for PR1/PR2 sequencing decisions, not user-facing documentation. It was added by a stray `git add -A` in the previous commit. Untrack it; the file stays in the working tree as untracked for local reference. Co-Authored-By: Claude Opus 4.7 (1M context) --- pickle-redesign-plan.md | 784 ---------------------------------------- 1 file changed, 784 deletions(-) delete mode 100644 pickle-redesign-plan.md diff --git a/pickle-redesign-plan.md b/pickle-redesign-plan.md deleted file mode 100644 index 8ded1b6f6..000000000 --- a/pickle-redesign-plan.md +++ /dev/null @@ -1,784 +0,0 @@ -# Plan: Decouple `Expr` pickle support from global `SessionContext` - -## Context - -[apache/datafusion-python#1517](https://github.com/apache/datafusion-python/pull/1517) -("Allow pickling PyExpr") adds `pickle`/`dill` support to `Expr` so that -expressions can be shipped across process boundaries — primarily for -`multiprocessing.Pool`-style fan-out within a single machine, with an eye toward -working around `datafusion-distributed` not being deployed everywhere. - -This document proposes a redesign of how `__setstate__` resolves the -reconstruction context, replacing the current process-global singleton with an -explicit, framework-agnostic worker-scoped context. The new design works -identically under `fork`, `forkserver`, `spawn`, Ray actors, and any other -worker model that exposes an initialization hook. - -The plan is staged: Phase 1 is the minimal structural change (small, low risk, -unblocks all the downstream patterns). Phase 2 is additive (cloudpickle-based -UDF inlining). Phase 3 is documentation/examples. - ---- - -## What the PR currently does - -Two structural changes from the existing commits on -`rerun-io:nick/pickle_expr`: - -1. **Mutable global context.** `datafusion-python-util` promoted its global - slot from `OnceLock>` to `RwLock>`, - exposed as `set_global_ctx` (Rust) and `SessionContext.set_as_global` - (Python). Users register UDFs on a context and call `set_as_global()` to - make those UDFs visible to subsequent pickle reconstructions. - -2. **Pickle protocol on `Expr`.** `Expr.to_bytes` / `Expr.from_bytes` were - added on top of `datafusion-proto`'s `Serializeable` trait. The Python - wrapper implements `__getstate__` (returns proto bytes) and `__setstate__` - (decodes proto bytes by looking up function references against the - *process-wide global* `SessionContext`). Built-in functions always - roundtrip; user-defined functions roundtrip when registered on a context - that has been installed via `SessionContext.set_as_global()`. - -## Why the current design is fragile - -### Coupling to start-method semantics - -`SessionContext.set_as_global()` propagates to worker processes only when those -workers inherit the parent's memory. That is the `fork` start method, and -only `fork`. - -- **fork (POSIX, pre-3.14 default):** worker is a COW clone; the global is - present in the worker because it was present in the parent at fork time. -- **forkserver (POSIX, 3.14+ default):** a long-lived helper process forks - workers. The helper started clean; module-level `set_as_global()` calls - that ran in the parent are *not* in the helper, so workers don't see them - unless setup re-runs. -- **spawn (Windows default, macOS default since 3.8, optional elsewhere):** worker - is a fresh interpreter. Module-level setup runs again in the worker; whether - `set_as_global()` ran depends on whether it sits in module-level code or in - the parent's `__main__`. - -Python 3.14 changed the POSIX default from `fork` to `forkserver`. The PR's -pattern silently degrades on the now-default start method unless the user -happens to structure their imports correctly. - -### Pickle has no context channel - -`__setstate__(state)` is invoked with exactly one positional argument: the -state blob returned by `__getstate__`. There is no idiomatic way for the -caller of `pickle.loads` to pass an explicit receiver context. The PR's -solution — consult a process-global — is the natural workaround, but it -couples deserialization to mutable ambient state in a way that: - -- Makes the same blob deserialize differently depending on what the receiver - has installed. -- Conflicts when multiple libraries try to use the global slot. -- Fails silently if a UDF name happens to collide with a different - implementation registered on the receiver. - -### Not extensible to other worker models - -Ray actors, `concurrent.futures.ProcessPoolExecutor`, Dask workers, and other -multi-process frameworks all have per-worker initialization hooks. The PR's -pattern bypasses all of them by routing through a global. The result is that -each framework needs its own integration shim instead of a single uniform API. - ---- - -## Proposed design - -### Worker-scoped context, set by an initialization hook - -Replace the "process-global ctx" lookup inside `__setstate__` with a -"worker-scoped ctx" lookup. The worker scope is populated by the framework's -worker-init hook (e.g. `Pool(initializer=...)`, Ray actor `__init__`, -`ProcessPoolExecutor(initializer=...)`). This is the pattern PySpark uses for -executor startup and Ray actors use for per-actor state. - -``` -+-- Driver process ---------+ +-- Worker process ----------+ -| | | | -| ctx = SessionContext() | pickle | init_worker(): | -| ctx.register_udf(...) | ====> | ctx = SessionContext() | -| blob = pickle.dumps(expr) | | ctx.register_udf(...) | -| | | set_worker_ctx(ctx) | -| | | | -| | | expr = pickle.loads(blob) | -| | | # reads _worker_ctx() | -+---------------------------+ +----------------------------+ -``` - -The worker scope is just a module-level (or thread-local) variable in -`datafusion`. Each worker process has its own. No fork-time copy-on-write -dependency; no module-level magic; no global mutation that can collide. - -### Envelope as the canonical wire format (Phase 2) - -For UDFs the receiver doesn't already have registered, ship them *inside the -blob* via cloudpickle. The state stored by `__getstate__` becomes: - -```python -ExprEnvelope = { - "version": int, - "proto_bytes": bytes, # existing datafusion-proto encoding - "udf_definitions": dict, # {name: cloudpickled_callable_def} for Python UDFs - "config_overrides": dict, # subset of SessionConfig that affects evaluation -} -``` - -`__setstate__` resolves UDFs in priority order: (1) worker ctx if set, (2) -envelope's `udf_definitions`, (3) fail loudly listing what was needed and -what's available. The receiver only ever constructs a *fresh local* -`SessionContext` if the user hasn't set a worker ctx — never reaches for a -global. - -### Explicit `from_bytes(buf, ctx=...)` as the canonical Python API - -The pickle protocol is the *convenience* layer. The supported, documented -path is: - -```python -env = expr.to_envelope(ctx=sender_ctx, include_udfs="referenced") -blob = cloudpickle.dumps(env) # or any serializer - -# Receiver: -env = cloudpickle.loads(blob) -expr = Expr.from_envelope(env, ctx=receiver_ctx) # explicit, no globals -``` - -`__getstate__` / `__setstate__` delegate to these methods. Users who want -explicit control bypass pickle entirely. - ---- - -## Implementation plan - -### Phase 1 — Decouple from global ctx (minimum viable change) - -Goal: stop `__setstate__` from depending on `set_as_global()`. Do not change -the wire format. Do not add cloudpickle. Just rewire where the reconstruction -ctx comes from. - -**Python side (`python/datafusion/`):** - -1. New module `python/datafusion/_ipc.py` (or `ipc.py` if public): - ```python - import threading - _local = threading.local() - - def set_worker_ctx(ctx: "SessionContext") -> None: - """Register the receiver context for Expr unpickling in this worker. - - Call once per worker process, typically from a Pool initializer or - Ray actor __init__. Idempotent; overwrites any previous value. - """ - _local.ctx = ctx - - def clear_worker_ctx() -> None: - """Remove the worker ctx, restoring fresh-context fallback behavior.""" - if hasattr(_local, "ctx"): - del _local.ctx - - def _get_worker_ctx() -> "SessionContext | None": - return getattr(_local, "ctx", None) - ``` - -2. Modify `Expr.__setstate__` to: - - Call `_get_worker_ctx()`. If set, decode against it. - - If not set, build a fresh `SessionContext()` and decode against that. - This will succeed for built-in functions and fail informatively for - unregistered UDFs. - - Do *not* read from `set_as_global` / `get_global_ctx`. - -3. Update `Expr.from_bytes` (Python wrapper) to take an explicit `ctx` - parameter. Keep backward-compatible default — if `ctx=None`, behave as - `__setstate__` does (worker ctx → fresh ctx → error). - -**Rust side (`src/`):** - -The underlying `RawExpr::from_bytes` likely already takes a `SessionContext` -parameter (this is how the current PR's global lookup wires through). Verify -this; if it does not, plumb one through. Do not introduce any new Rust-side -worker-ctx state — keep that entirely in Python. - -The existing `set_global_ctx` / `SessionContext.set_as_global` can stay for -backward compatibility but are no longer consulted during pickle -reconstruction. Add a docstring note that `set_as_global` is legacy and -should not be relied on for pickle behavior. - -**Tests:** - -Replace the existing pickle tests with a parametrized matrix over -`(serializer, ctx_strategy)`: - -| ctx_strategy | expected behavior | -|------------------------|--------------------------------------------------| -| `worker_ctx_with_udfs` | UDFs resolve, expression evaluates correctly | -| `worker_ctx_empty` | Built-ins work, UDF references raise | -| `no_worker_ctx` | Built-ins work, UDF references raise informatively | -| `global_ctx_set` | Should NOT affect outcome (asserts decoupling) | - -Add three multiprocessing tests parametrized over start methods: - -```python -@pytest.mark.parametrize("start_method", ["fork", "forkserver", "spawn"]) -def test_pickle_roundtrip_via_pool(start_method): - ctx = mp.get_context(start_method) - with ctx.Pool(initializer=init_worker, initargs=()) as pool: - results = pool.map(evaluate_one, [(expr, batch) for ...]) - assert results == expected -``` - -The worker initializer registers UDFs and calls `set_worker_ctx`. The -`init_worker` function must live in a non-test module (per the lessons learned -in commit `bdeedd7` about pytest-importlib synthetic module names — keep that -fix). - -### Phase 2 — Envelope-based UDF inlining (additive) - -Goal: support pickling expressions that reference Python UDFs the receiver -hasn't pre-registered. Sender embeds cloudpickled UDF definitions; receiver -unpacks them onto its ctx (or a fresh one) before decoding. - -**Python side:** - -1. Add `Expr.referenced_functions() -> list[str]`. Walks the expression tree, - yields names of every UDF/UDAF/UDWF/builtin referenced. May need a small - Rust-side helper that exposes the walker. - -2. Define `ExprEnvelope` as a Python dataclass (kept Python-side; the Rust - layer only deals with proto bytes): - ```python - @dataclass - class ExprEnvelope: - version: int - proto_bytes: bytes - udf_definitions: dict[str, bytes] # cloudpickle blobs - config_overrides: dict[str, str] - ``` - -3. Add `Expr.to_envelope(ctx, *, include_udfs="referenced")`: - - Walk `referenced_functions()`. - - For each name, check if it's a built-in (skip; receiver always has it). - - Otherwise look up the UDF on the sender's `ctx`, extract the underlying - Python callable + signature + volatility, cloudpickle it, store under - the name. - - Rust-only UDFs (no Python callable) cannot be inlined — store just the - name and document that the receiver must have a matching registration. - - `include_udfs="none"` skips inlining entirely (envelope carries only - proto bytes); `include_udfs="all"` walks and inlines every UDF on the - sender ctx regardless of reference. - -4. Add `Expr.from_envelope(env, ctx=None)`: - - If `ctx` is None, use `_get_worker_ctx()` or build a fresh one. - - For each `(name, blob)` in `env.udf_definitions`, cloudpickle.loads and - register on `ctx`. If `ctx` already has a UDF named `name`, raise unless - a `prefer_envelope=False` knob says otherwise (avoid silent override). - - Apply `env.config_overrides` to `ctx`. - - Decode `env.proto_bytes` against `ctx`. - -5. Rewire `__getstate__` / `__setstate__` to delegate to `to_envelope` / - `from_envelope`. The state stored by `__getstate__` is the `ExprEnvelope` - itself (which pickle/cloudpickle then serializes recursively). - -**Dependencies:** - -Add `cloudpickle` to runtime deps (not just dev). `dill` stays as optional; -the test matrix can keep covering both. - -**Tests:** - -- Lambda UDF roundtrip via envelope, no pre-registered worker ctx (envelope - is fully self-contained). -- Closure-capturing UDF roundtrip; verify captured state is reconstructed. -- Name collision between envelope UDF and worker ctx UDF — verify the - documented resolution rule (raise by default). -- Rust UDF referenced but not registered on receiver — verify clear error - message identifying which UDFs are missing. -- Cross-version cloudpickle (different Python minors) — document expected - failure mode if any. - -### Phase 3 — Documentation and integration examples - -Goal: make the new patterns discoverable and lower the integration cost for -Ray/Dask/concurrent.futures users. - -1. **User guide section** "Distributing expressions across processes": - - Recommended `Pool(initializer=...)` pattern with full example. - - Note about Python 3.14 default start method change. - - Trade-offs of envelope inlining vs pre-registered UDFs (blob size, - closure capture surprises, Rust UDFs can't be inlined). - - Security note: envelope deserialization runs cloudpickle, which - executes arbitrary code. Only deserialize blobs from trusted sources. - -2. **Ray integration example** (separate file, not a runtime dep): - ```python - import ray - from datafusion import SessionContext - from datafusion.ipc import set_worker_ctx - - @ray.remote - class DataFusionWorker: - def __init__(self, udf_defs): - ctx = SessionContext() - for name, fn in udf_defs.items(): - ctx.register_udf(name, fn) - set_worker_ctx(ctx) - self.ctx = ctx - - def evaluate(self, expr, batch): - return self.ctx.evaluate(expr, batch) - ``` - -3. **Migration note for `set_as_global`:** explain that the API is still - available but no longer affects pickle behavior. Recommend - `set_worker_ctx` for new code. Consider a `DeprecationWarning` if - `set_as_global` is called (separate decision — may break existing users - who rely on it for non-pickle reasons). - ---- - -## Open design questions - -Things worth deciding explicitly before implementation, ideally on the PR or -the linked issue: - -1. **Should `__setstate__` fall back to a fresh ctx when no worker ctx is - set, or raise immediately?** Falling back is friendlier for trivial cases - (no UDFs, just built-ins) but hides the "you forgot to set up the - worker" error in the common case. Suggest: fall back, but log a debug - message; raise only if the proto references a non-built-in UDF that - can't be resolved. - -2. **Name collision policy in `from_envelope`.** If the worker ctx has a - UDF named `foo` and the envelope also carries one named `foo`, what - wins? Suggest: raise by default with a clear error; offer - `prefer="worker" | "envelope"` parameter. Silent override is the worst - outcome. - -3. **Is `ipc` the right module name?** Alternatives: `_runtime`, `dist`, - `workers`, `transport`. Whatever is chosen should be stable since the - `set_worker_ctx` symbol becomes user-facing API. - -4. **Should `set_as_global` be deprecated?** It has uses outside pickle - (e.g. controlling the default ctx for `read_*` module helpers). Suggest: - keep without deprecation, just stop using it from pickle paths, document - the new boundary. - -5. **Cloudpickle as a runtime dep.** Adds ~50KB and a transitive dep on - nothing serious. Probably fine, but worth flagging in the PR. Alternative: - make Phase 2 require `pip install datafusion[distributed]` with - cloudpickle as an optional extra. - -6. **Should the envelope version be wire-compatible across DataFusion - releases?** The proto encoding has its own version story; the envelope - adds another layer. Suggest: explicit `version: int` field, refuse to - decode envelopes from a future major. - ---- - -## Testing strategy summary - -The test pyramid for both phases: - -- **Unit:** envelope construction/round-trip, UDF enumeration, error paths - (missing UDFs, name collisions, version mismatches). -- **Integration:** pickle roundtrip across all three start methods via - `mp.get_context(method).Pool`. Run under CI on Linux at minimum, ideally - also macOS (where `spawn` is default). -- **Negative:** ensure setting `set_as_global` does *not* affect a worker - that doesn't call `set_worker_ctx` — verifies decoupling. -- **Stress:** large expressions with many UDFs, expressions with deeply - nested case/when, expressions referencing both built-ins and UDFs. -- **Pool hang regression:** the original PR hit a deadlock when worker - processes died during unpickle due to pytest-importlib synthetic module - names. Keep `tests/_pickle_multiprocessing_helpers.py` (or equivalent - non-test module) for worker-side function definitions. Keep the - `timeout-minutes: 30` job cap as a backstop for future regressions. - ---- - -## Implementation outcome (commit `aa08438`, local branch `feat/pickle-pyexpr`) - -> **Workflow note (2026-05-14).** The work captured below was prototyped on -> `feat/pickle-pyexpr` but is *not* the merge target. We are landing the -> codec consistency work first as PR1 on a separate branch -> (`feat/proto-codecs`, off `main`), then rebasing the pickle work onto -> PR1 as PR2. See "Phasing the consistency work" below for the revised -> ordering. The text in this section describes the prototype state on -> `feat/pickle-pyexpr` and is preserved as design context for PR2. - -Phases 1 and 2 landed with a design change worth recording: instead of a -Python-side `ExprEnvelope` dataclass, Python scalar UDFs are serialized -directly into the proto wire format by a new Rust-side -`LogicalExtensionCodec` (`crates/core/src/codec.rs`). The codec's -`try_encode_udf` downcasts the DataFusion `ScalarUDF` to the private -`PythonFunctionScalarUDF` struct, cloudpickles -`(name, callable, input_schema, return_field, volatility)` into the -proto `fun_definition` field, and tags the payload with the `DFPYUDF1` -magic prefix. `try_decode_udf` reverses it. Single self-contained wire -format — no two-layer envelope, no Python-side UDF registry on -`SessionContext`. - -`Expr.__reduce__` simply calls `to_bytes` / `from_bytes`, both of which -route through the codec. The worker-scoped ctx -(`datafusion.ipc.set_worker_ctx`) is still consulted on decode for -references the codec can't inline — currently aggregate UDFs, window -UDFs, and FFI capsule UDFs. - -UDAF / UDWF inlining was descoped: their Python state is held inside -opaque factory closures on the Rust side, and the codec needs a -downcastable named struct to extract the callable. A future change can -refactor `RustAccumulator` / `MultiColumnWindowUDF` to expose the same -shape as `PythonFunctionScalarUDF` and extend the codec. - -> **Naming note.** The committed code uses `PythonUDFCodec` as the -> Rust struct name. The consistency proposal below renames it to -> `PythonLogicalCodec` — see [R1]. - ---- - -## Phase 4 — Cross-cutting consistency with other serialization paths - -`feat/pickle-pyexpr` adds proto-based serialization to `Expr`. The -codebase already has, or has WIP for, two other serialization flows: - -1. **`PyLogicalPlan.to_proto` / `from_proto`** on `main` - (`crates/core/src/sql/logical.rs`) — hardcodes - `DefaultLogicalExtensionCodec`. -2. **`PyExecutionPlan.to_proto` / `from_proto`** plus module-level - `serialize_execution_plan` / `deserialize_execution_plan` / - `serialize_physical_expr` / `deserialize_physical_expr` — on the WIP - branch `poc_ffi_query_planner` (`src/physical_plan.rs`). Takes a - codec PyCapsule and a `task_ctx_provider` PyCapsule. - -Without a deliberate plan these flows will drift further apart. This -section proposes a consistency target so future merges (`Expr` pickle -landed, `LogicalPlan` codec stack, eventual POC merge) produce a -uniform API. - -### Observed inconsistencies - -| Concern | `LogicalPlan` (main) | `Expr` (this branch) | `ExecutionPlan` / `PhysicalExpr` (POC) | -| ------------------- | -------------------- | -------------------- | -------------------------------------- | -| Codec source | Hardcoded default | Hardcoded `PythonUDFCodec` | Explicit codec PyCapsule per call | -| Method name | `to_proto` / `from_proto` | `to_bytes` / `from_bytes` | `to_proto` / `from_proto` + module-level `serialize_*` / `deserialize_*` | -| Decoder input | `PySessionContext` | `PySessionContext` | `task_ctx_provider` PyCapsule | -| PyCapsule protocol on input | None | None | Accepts both typed class + capsule-protocol objects | -| Reads `SessionContext.logical_codec` | No | No | N/A (physical codec is separate) | - -`SessionContext` already stores -`logical_codec: Arc` -(`crates/core/src/context.rs:368`) and exposes -`with_logical_extension_codec` plus `__datafusion_logical_extension_codec__` -getter. Nothing currently consults it for proto serialization — it is -only used to plumb codecs through the catalog/table FFI surface. That -field is the obvious home for the codec the recommendations below -prescribe. - -### Recommendations - -**R1. Rename `PythonUDFCodec` → `PythonLogicalCodec`; make it composable.** - -The codec layer is *Python-side logical-layer extensions*, not just -scalar UDFs. Today it handles `try_encode_udf` / `try_decode_udf`; a -future patch adds UDAF/UDWF handling. Reserve the broader name now to -avoid renaming churn: - -```rust -pub struct PythonLogicalCodec { - /// Fallback for anything this codec does not handle directly: - /// non-Python ScalarUDFs, UDAFs, UDWFs, table providers, - /// extension nodes, etc. Typically supplied by the user via - /// `SessionContext.with_logical_extension_codec(...)`. Defaults - /// to `DefaultLogicalExtensionCodec` when no user codec is set. - inner: Arc, -} - -impl PythonLogicalCodec { - pub fn new(inner: Arc) -> Self { - Self { inner } - } - - pub fn with_default_inner() -> Self { - Self::new(Arc::new(DefaultLogicalExtensionCodec {})) - } -} -``` - -Dispatch story (already implemented for `try_decode_udf`; generalize): - -- **encode** — Downcast to the named Python impl struct - (`PythonFunctionScalarUDF` today, `PythonAggregateUDF` / - `PythonWindowUDF` later). On success: write magic-prefixed - cloudpickle payload. On failure: delegate to `self.inner.try_encode_*` - so user-supplied FFI codecs can handle their own types. -- **decode** — Check magic prefix: - - `DFPYUDF1` → scalar UDF cloudpickle path - - `DFPYUDA1` → aggregate UDF cloudpickle path (future) - - `DFPYUDW1` → window UDF cloudpickle path (future) - - anything else (including empty buf) → delegate to - `self.inner.try_decode_*`, which either handles a user FFI payload - or returns `not_impl_err` causing the caller (`parse_expr`) to fall - back to the receiver's `FunctionRegistry`. - -This gives a strict precedence: **Python inline → user FFI codec → -registry lookup**, decided per-payload by the magic prefix, with no -ambiguity about which path produced a given blob. - -**R2. One codec lives on `SessionContext`; every serializer reads it.** - -`SessionContext` already stores `logical_codec`. Wrap that field so the -session-attached codec *is* a `PythonLogicalCodec` whose inner is the -user-supplied FFI codec (or `Default` when none). Constructor sketch -(adapter glue elided — `FFI_LogicalExtensionCodec` already implements -`LogicalExtensionCodec`): - -```rust -let inner: Arc = user_codec - .map(|c| c as Arc) - .unwrap_or_else(|| Arc::new(DefaultLogicalExtensionCodec {})); -let logical_codec = Arc::new(PythonLogicalCodec::new(inner)); -``` - -`with_logical_extension_codec(new_inner)` rebuilds the -`PythonLogicalCodec` with the new inner. `PyExpr::to_bytes` / -`from_bytes` (this branch) drop their hardcoded -`PythonUDFCodec::new()` and read `session.logical_codec` instead. -`PyLogicalPlan::to_proto` / `from_proto` switch from hardcoded -`DefaultLogicalExtensionCodec` to the same field. Default behavior -unchanged for users who never call `with_logical_extension_codec`. - -Same pattern on the physical side: introduce -`PythonPhysicalCodec` (mirror of `PythonLogicalCodec`, also -composable), park it on `SessionContext` as `physical_codec`, have -`PyExecutionPlan` / future `PyPhysicalExpr` consult it. - -**R3. Single naming convention: `to_bytes` / `from_bytes` on the class.** - -Choose one pair. Recommend `to_bytes` / `from_bytes` because: -- This branch already uses it for `Expr`. -- The proto encoding is the *byte* representation, not the proto - object; `to_proto` was a slight misnomer on `LogicalPlan`. -- Symmetric with `Serializeable::{to_bytes, from_bytes_with_registry}` - upstream. - -Rename `PyLogicalPlan::to_proto` → `to_bytes` and `from_proto` → -`from_bytes` as a follow-up. Keep `to_proto` / `from_proto` as -deprecated aliases for at least one release. POC's module-level -`serialize_execution_plan` / `deserialize_execution_plan` collapse -into `PyExecutionPlan::to_bytes` / `from_bytes`; the FFI-bridging -PyCapsule-input variants stay as low-level helpers in the -`physical_plan` submodule for external Rust consumers (rename to -clarify their role, e.g. `bytes_from_capsule_plan`). - -**R4. Decoders take `SessionContext`, not raw PyCapsule plumbing.** - -The public Python API for every decoder accepts a `SessionContext`. -The Rust side pulls `task_ctx`, the function registry, and the codec -stack from that single argument. POC's split into -`(plan_bytes, task_ctx_provider_capsule, codec_capsule)` is FFI-glue -and should be wrapped behind `SessionContext.from_capsules(...)` for -callers who only have PyCapsules — same low-level access without -making it the default decoder shape. - -**R5. PyCapsule protocol acceptance on every serialized type.** - -POC's `PyExecutionPlan.from_pycapsule` and the dual-input -`serialize_execution_plan` pattern (extract typed class *or* capsule) -should generalize. `PyLogicalPlan`, `PyExpr` (this branch), and any -future serialized types expose `__datafusion___(py) -> PyCapsule` -getters and accept capsule-protocol objects on input. POC's -`from_pycapsule!` / `try_from_pycapsule!` macros (POC -`src/utils.rs:182–223`) move into `datafusion-python-util` and become -the canonical extractor pattern. Current main already uses them for -table/catalog providers; extend to plans/exprs. - -**R6. Wire-format magic prefix registry.** - -`PythonLogicalCodec` prepends a magic byte sequence per payload kind. -Establish a convention table: - -| Codec layer + kind | Magic prefix | Owner | -| ----------------------------- | --------------- | ----------------- | -| `PythonLogicalCodec` (scalar) | `DFPYUDF1` | this branch | -| `PythonLogicalCodec` (agg) | `DFPYUDA1` | future | -| `PythonLogicalCodec` (window) | `DFPYUDW1` | future | -| `PythonPhysicalCodec` (expr) | `DFPYPE1` | future / POC merge | -| User FFI extension codec | user-supplied | downstream crates | -| Default codec (no magic) | (none) | upstream | - -`PythonLogicalCodec` decode dispatches by prefix; unknown prefixes (or -no prefix) delegate to the configured `inner` codec. User FFI codecs -must pick non-colliding magic prefixes (recommend a `DF` namespace plus -crate-specific suffix). - -**R7. Cross-language story made explicit.** - -The pre-existing "Out of scope" note that *"the bare `to_bytes` / -`from_bytes` proto path remains for cross-language use"* is no longer -accurate: with `PythonLogicalCodec` in the default stack, blobs -containing Python scalar UDFs are not portable to non-Python decoders. -Two remediations: - -a. **Optional codec override on `to_bytes`** — - `expr.to_bytes(cross_language=True)` builds bytes using only the - inner codec (typically `DefaultLogicalExtensionCodec` or a user - FFI codec), skipping Python-side encoding. Decoder must have UDFs - pre-registered or supply a matching FFI codec. -b. **Document the trade-off** in the user guide — the default codec - stack is Python-aware; the wire format is Python-receiver-only - unless the sender opts out. - -**R8. Move helpers into shared crate.** - -POC plumbing (`from_pycapsule!`, `try_from_pycapsule!`, -`task_context_from_pycapsule`, `physical_codec_from_pycapsule`, -`validate_pycapsule`) is duplicated material. Park them in -`crates/util` (already houses `create_logical_extension_capsule`, -`ffi_logical_codec_from_pycapsule`) so every consumer pulls from one -place. - -**R9. Pickle = `to_bytes` + worker ctx, for every serialized type.** - -`Expr.__reduce__` (this branch) is the template. When `LogicalPlan` / -`ExecutionPlan` pickle support is added, they use the same shape: -`__reduce__` returns `(cls._reconstruct, (self.to_bytes(),))`; -`_reconstruct` resolves the receiver context via -`datafusion.ipc._resolve_ctx(None)` and calls `cls.from_bytes`. No -per-type envelope dataclass. The worker-ctx mechanism in -`datafusion.ipc` is reused unchanged. - -### Phasing the consistency work - -Reordered (2026-05-14) after deciding to land codec consistency *before* -pickle work, on a fresh branch off `main` (`feat/proto-codecs`). The -pickle prototype on `feat/pickle-pyexpr` (local commit `aa08438`) -rebases onto the codec PR after it merges. Two PRs: - -**PR1 — Logical + physical codec consistency (`feat/proto-codecs`).** - -1. **R1 — Introduce `PythonLogicalCodec` and `PythonPhysicalCodec`.** - Both follow the composable-inner pattern (Python-aware dispatch - first, delegate to a user-supplied inner codec, then registry - fallback). Both implement scalar-UDF inline encoding via the - shared `DFPYUDF1` magic prefix ported from `aa08438` — the - `PhysicalExtensionCodec` trait exposes `try_encode_udf` / - `try_decode_udf` (and the UDAF/UDWF variants) the same way the - logical trait does, so an `ExecutionPlan` or `PhysicalExpr` that - references a Python `ScalarUDF` round-trips only if the physical - codec inlines it too. Factor the cloudpickle tuple - `(name, callable, input_schema, return_field, volatility)` and - the magic-prefix framing into a shared helper that both codecs - call. What stays deferred on the physical side is the - `DFPYPE*` namespace: encoding for Python-defined physical - extension nodes (`ExecutionPlan` impls) and Python-defined - `PhysicalExpr` impls. No concrete Python-side physical extension - type exists today, so no prefix assignment in PR1. -2. **R2 — Park codecs on `SessionContext`.** Replace the existing - `logical_codec: Arc` field with - `Arc`; add `physical_codec: Arc`. - `with_logical_extension_codec` and a new `with_physical_extension_codec` - rebuild the wrapper with the supplied inner. Every serializer reads - from these session fields. -3. **R3 — Rename `PyLogicalPlan::to_proto` → `to_bytes`** and - `from_proto` → `from_bytes`. Keep `to_proto` / `from_proto` as - deprecated thin wrappers that emit `DeprecationWarning` and call - the new names. Targeted for removal one release after PR1. -4. **R5 — PyCapsule protocol on serialized types.** Move - `from_pycapsule!` / `try_from_pycapsule!` from POC `src/utils.rs` - into `datafusion-python-util`. `PyExecutionPlan` and `PyPhysicalExpr` - expose `__datafusion___` capsule getters and accept - capsule-protocol input on `from_pycapsule`. `PyLogicalPlan` is - excluded: `datafusion-ffi` does not expose a `FFI_LogicalPlan` - representation, so there is no stable capsule shape to publish. - Round-tripping a `LogicalPlan` across a process boundary goes - through `to_bytes` / `from_bytes` only. -5. **R6 — Magic-prefix registry doc comment** in - `crates/core/src/codec.rs`. `DFPYUDF1` in use; `DFPYUDA1`, - `DFPYUDW1`, `DFPYPE1` reserved. -6. **R8 — Shared helpers into `crates/util`.** - `task_context_from_pycapsule`, `physical_codec_from_pycapsule`, - `validate_pycapsule`, and the capsule macros land here. Core and - downstream consumers pull from one place. -7. **POC equivalent surface area.** Pull - `PyExecutionPlan::to_bytes` / `from_bytes`, `PyPhysicalExpr::to_bytes` / - `from_bytes`, and `task_ctx_provider` capsule extraction off - `poc_ffi_query_planner`. Module-level `serialize_execution_plan` / - `deserialize_execution_plan` / `serialize_physical_expr` / - `deserialize_physical_expr` collapse into the class methods; the - FFI-bridging capsule-input variants stay as low-level helpers in - the `physical_plan` submodule (rename for clarity, e.g. - `bytes_from_capsule_plan`). - -**PR2 — Pickle and worker-scoped distribution (rebase of `aa08438`).** - -1. `PyExpr::to_bytes` / `from_bytes` reading `session.logical_codec` - (no hardcoded `PythonUDFCodec`). -2. `Expr.__reduce__` + `datafusion.ipc.set_worker_ctx` worker-scoped - fallback (Phase 1 of the original plan). -3. Multiprocessing tests across `fork`, `forkserver`, `spawn`. -4. **R9 — Pickle template reused on other serialized types.** Apply - `__reduce__` returning `(cls._reconstruct, (self.to_bytes(),))` to - `PyLogicalPlan` and `PyExecutionPlan` once the pickle infra exists. -5. **R7 — `to_bytes(cross_language=True)` opt-out** for blobs that - must round-trip through non-Python decoders. - -**Deferred (separate follow-ups after PR2).** - -- **UDAF / UDWF inline encoding.** Needs `RustAccumulator` / - `MultiColumnWindowUDF` refactored to expose a downcastable named - struct. Magic prefixes `DFPYUDA1` / `DFPYUDW1` reserved. -- **`PythonPhysicalCodec` extension-node / physical-expr inline - payloads.** Scalar-UDF (and the UDAF/UDWF variants, once those - ship logical-side) round-trip through `DFPYUDF1` / - `DFPYUDA1` / `DFPYUDW1` from PR1 onward. Distinct from those: - Python-defined physical extension nodes (`ExecutionPlan` impls) - and Python-defined `PhysicalExpr` impls. Once a concrete - Python-side physical extension type exists, assign a magic - prefix from the `DFPYPE*` namespace and add encode/decode. - ---- - -## Out of scope - -Things this plan deliberately does not address — flag as follow-ups if -needed: - -- **Cross-language serialization.** Blobs produced by the default codec - stack are not cross-language because they may contain cloudpickle - payloads. The plan addresses this in Phase 4 R7 (opt-out - `to_bytes(cross_language=True)`); full cross-language interop with a - Rust-only receiver is otherwise out of scope. -- **Distributed catalog / object store / table provider replication.** - Expressions don't typically reference tables directly (that's a `LogicalPlan` - concern). If pickle support is later extended to `LogicalPlan` or - `DataFrame`, those will have their own envelope concerns. -- **Authentication of envelopes.** Cloudpickle is arbitrary-code-execution - on deserialize. If untrusted-source deserialization becomes a real use - case, add a signed-envelope variant; not part of this work. -- **Replacing `datafusion-distributed`.** This is for local multi-process - fan-out and Python-orchestrated frameworks (Ray, Dask). True distributed - execution remains datafusion-distributed's domain. - ---- - -## References - -- PR #1517: -- Issue #1520 (motivating): -- Python 3.14 multiprocessing default change: - — `forkserver` is now the - POSIX default for `ProcessPoolExecutor` and `multiprocessing`. -- `datafusion-proto` `Serializeable` trait (the underlying wire format - the envelope wraps). -- `cloudpickle`: -- Ray custom serializer API: - `ray.util.register_serializer(cls, serializer, deserializer)` -- Relevant commits on `rerun-io:nick/pickle_expr`: - - `23543fc` — `OnceLock` → `RwLock` for global ctx (subject of decoupling) - - `5796b53` — proto-based `to_bytes`/`from_bytes` and pickle hooks - (the to_bytes/from_bytes plumbing is reusable; the `__setstate__` - global-ctx lookup is what changes) - - `bdeedd7` — pytest-importlib helper-module fix (keep) - - `106ea3c` — 30-minute job timeout (keep) From 9a4af4143b2109086a8ce1be8eab41f6099c1320 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 07:37:58 -0400 Subject: [PATCH 14/57] refactor: rename MultiColumnWindowUDF -> PythonFunctionWindowUDF The "multi-column" name was a relic of an earlier upstream limitation where `SimpleWindowUDF` only accepted a single input column. With the struct now also storing the Python evaluator factory directly for pickle support, the relevant distinction is no longer column count but "Python-defined". Rename to match `PythonFunctionScalarUDF` and `PythonFunctionAggregateUDF` for a consistent naming convention across all three Python UDF kinds. Also tighten visibility from `pub` to `pub(crate)`. No external consumer; the struct only needs to be reachable from `PyWindowUDF` and the codec. No functional change. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 18 +++++++++++------- crates/core/src/udwf.rs | 16 ++++++++-------- 2 files changed, 19 insertions(+), 15 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 5dc91f215..bed395b4d 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -97,7 +97,7 @@ use pyo3::types::{PyBytes, PyTuple}; use crate::udaf::PythonFunctionAggregateUDF; use crate::udf::PythonFunctionScalarUDF; -use crate::udwf::MultiColumnWindowUDF; +use crate::udwf::PythonFunctionWindowUDF; /// Wire-format prefix that tags a `fun_definition` payload as an /// inlined Python scalar UDF (cloudpickled tuple of name, callable, @@ -518,7 +518,11 @@ fn schema_from_ipc_bytes(bytes: &[u8]) -> arrow::error::Result { // ============================================================================= pub(crate) fn try_encode_python_window_udf(node: &WindowUDF, buf: &mut Vec) -> Result { - let Some(py_udf) = node.inner().as_any().downcast_ref::() else { + let Some(py_udf) = node + .inner() + .as_any() + .downcast_ref::() + else { return Ok(false); }; @@ -544,7 +548,7 @@ pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result, udf: &MultiColumnWindowUDF) -> PyResult> { +fn encode_python_window_udf(py: Python<'_>, udf: &PythonFunctionWindowUDF) -> PyResult> { let cloudpickle = py.import("cloudpickle")?; let signature = WindowUDFImpl::signature(udf); @@ -552,7 +556,7 @@ fn encode_python_window_udf(py: Python<'_>, udf: &MultiColumnWindowUDF) -> PyRes TypeSignature::Exact(types) => types.clone(), other => { return Err(pyo3::exceptions::PyValueError::new_err(format!( - "MultiColumnWindowUDF expected Signature::Exact, got {other:?}" + "PythonFunctionWindowUDF expected Signature::Exact, got {other:?}" ))); } }; @@ -586,7 +590,7 @@ fn encode_python_window_udf(py: Python<'_>, udf: &MultiColumnWindowUDF) -> PyRes blob.extract::>() } -fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult { +fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult { let cloudpickle = py.import("cloudpickle")?; let tuple = cloudpickle @@ -614,7 +618,7 @@ fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult, payload: &[u8]) -> PyResult = input_types.into_iter().map(|t| t.0).collect(); - let function = WindowUDF::from(MultiColumnWindowUDF::new( + let function = WindowUDF::from(PythonFunctionWindowUDF::new( name, evaluator, input_types, @@ -276,15 +276,15 @@ impl PyWindowUDF { } #[derive(Debug)] -pub struct MultiColumnWindowUDF { +pub(crate) struct PythonFunctionWindowUDF { name: String, evaluator: Py, signature: Signature, return_type: DataType, } -impl MultiColumnWindowUDF { - pub fn new( +impl PythonFunctionWindowUDF { + pub(crate) fn new( name: impl Into, evaluator: Py, input_types: Vec, @@ -323,8 +323,8 @@ impl MultiColumnWindowUDF { } } -impl Eq for MultiColumnWindowUDF {} -impl PartialEq for MultiColumnWindowUDF { +impl Eq for PythonFunctionWindowUDF {} +impl PartialEq for PythonFunctionWindowUDF { fn eq(&self, other: &Self) -> bool { self.name == other.name && self.signature == other.signature @@ -338,7 +338,7 @@ impl PartialEq for MultiColumnWindowUDF { } } -impl std::hash::Hash for MultiColumnWindowUDF { +impl std::hash::Hash for PythonFunctionWindowUDF { fn hash(&self, state: &mut H) { self.name.hash(state); self.signature.hash(state); @@ -350,7 +350,7 @@ impl std::hash::Hash for MultiColumnWindowUDF { } } -impl WindowUDFImpl for MultiColumnWindowUDF { +impl WindowUDFImpl for PythonFunctionWindowUDF { fn as_any(&self) -> &dyn Any { self } From 44df444e77e3fa54146bd4ec93f6581b80f6e89b Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 07:42:22 -0400 Subject: [PATCH 15/57] docs(distributing-work): drop __main__ guard from example to match site style Other code blocks in the user guide present snippets inline at module level; the worker-pool example was the only one using ``if __name__ == "__main__":``. Restructure as two blocks (worker function + driver code), both inline, with a prose note explaining when the guard is actually needed (saving to a .py file and running under ``spawn`` / ``forkserver``). Matches the look of the surrounding docs and keeps the snippet copy-pasteable for the interactive case. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../user-guide/io/distributing_work.rst | 43 ++++++++++++------- 1 file changed, 27 insertions(+), 16 deletions(-) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 0c5009d2a..0b927f685 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -55,12 +55,13 @@ expression; the receiver does not need to pre-register them. Basic worker-pool example ~~~~~~~~~~~~~~~~~~~~~~~~~ -.. code-block:: python +Define a worker function that takes the expression plus a batch and +returns the evaluated result: - import multiprocessing as mp +.. code-block:: python import pyarrow as pa - from datafusion import SessionContext, col, udf + from datafusion import SessionContext def evaluate(expr, batch): @@ -70,21 +71,31 @@ Basic worker-pool example df = ctx.from_pydict({"a": batch}) return df.with_column("result", expr).select("result").to_pydict()["result"] +Then build the expression in the driver and fan it out: - if __name__ == "__main__": - double = udf( - lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), - [pa.int64()], pa.int64(), volatility="immutable", name="double", +.. code-block:: python + + import multiprocessing as mp + from datafusion import col, udf + + double = udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], pa.int64(), volatility="immutable", name="double", + ) + expr = double(col("a")) + + mp_ctx = mp.get_context("forkserver") + with mp_ctx.Pool(processes=4) as pool: + results = pool.starmap( + evaluate, + [(expr, [1, 2, 3]), (expr, [10, 20, 30])], ) - expr = double(col("a")) - - mp_ctx = mp.get_context("forkserver") - with mp_ctx.Pool(processes=4) as pool: - results = pool.starmap( - evaluate, - [(expr, [1, 2, 3]), (expr, [10, 20, 30])], - ) - print(results) # [[2, 4, 6], [20, 40, 60]] + print(results) # [[2, 4, 6], [20, 40, 60]] + +When saved to a ``.py`` file and executed with the ``spawn`` or +``forkserver`` start method, wrap the driver block in +``if __name__ == "__main__":`` so worker processes can re-import the +module without re-running it. What travels with the expression From 977e88c370e0b40476106d9cdc04b387e062e662 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 07:56:38 -0400 Subject: [PATCH 16/57] feat: per-session toggle for Python UDF inline encoding MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adds `SessionContext.with_python_udf_inlining(enabled)` for two related use cases: * **Cross-language portability.** With inlining disabled, the codec no longer emits `DFPYUDF1` / `DFPYUDA1` / `DFPYUDW1` cloudpickle blobs. Python UDFs travel by name only, the same way FFI-capsule UDFs do. Bytes round-trip through a non-Python decoder. * **Untrusted-source decode.** `Expr.from_bytes` on bytes from a misbehaving sender no longer invokes `cloudpickle.loads`. Inline payloads received by a strict decoder raise a clear error. `PythonLogicalCodec` and `PythonPhysicalCodec` gain a `python_udf_inlining: bool` field (default `true`) and a builder method `with_python_udf_inlining(enabled)`. The six UDF encode/decode dispatchers consult the flag before calling the inline helpers. Strict decoders that see a magic-prefix payload return a clear `Plan` error rather than silently failing through to the inner codec (which would otherwise produce "LogicalExtensionCodec is not provided" — accurate but unhelpful). `PySessionContext::with_python_udf_inlining(enabled)` rebuilds both codecs with the new setting; Python wrapper at `SessionContext.with_python_udf_inlining` mirrors. Test coverage: encoder size delta, strict roundtrip via registry, clear-error-on-inline-payload-when-strict. `pickle.loads` on untrusted bytes remains unsafe regardless of this flag; the toggle only governs the `to_bytes` / `from_bytes` codec path. User guide documents both use cases plus the limitation. 1097 root tests pass (up from 1094 with 3 new strict-mode cases). Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 108 ++++++++++++++---- crates/core/src/context.rs | 22 ++++ .../user-guide/io/distributing_work.rst | 37 +++++- python/datafusion/context.py | 25 ++++ python/tests/test_pickle_expr.py | 56 +++++++++ 5 files changed, 226 insertions(+), 22 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index bed395b4d..5ad67411f 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -128,16 +128,34 @@ pub(crate) const PY_WINDOW_UDF_MAGIC: &[u8] = b"DFPYUDW1"; #[derive(Debug)] pub struct PythonLogicalCodec { inner: Arc, + python_udf_inlining: bool, } impl PythonLogicalCodec { pub fn new(inner: Arc) -> Self { - Self { inner } + Self { + inner, + python_udf_inlining: true, + } } pub fn inner(&self) -> &Arc { &self.inner } + + /// Whether Python-defined UDFs are encoded inline (and decoded + /// from cloudpickle blobs). Defaults to `true`. Set to `false` + /// when the codec sits on a session that must produce + /// cross-language wire bytes, or reject `cloudpickle.loads` on + /// untrusted `from_bytes` input. + pub fn with_python_udf_inlining(mut self, enabled: bool) -> Self { + self.python_udf_inlining = enabled; + self + } + + pub fn python_udf_inlining(&self) -> bool { + self.python_udf_inlining + } } impl Default for PythonLogicalCodec { @@ -197,48 +215,72 @@ impl LogicalExtensionCodec for PythonLogicalCodec { } fn try_encode_udf(&self, node: &ScalarUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_scalar_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_scalar_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udf(node, buf) } fn try_decode_udf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udf) = try_decode_python_scalar_udf(buf)? { - return Ok(udf); + if self.python_udf_inlining { + if let Some(udf) = try_decode_python_scalar_udf(buf)? { + return Ok(udf); + } + } else if buf.starts_with(PY_SCALAR_UDF_MAGIC) { + return Err(refuse_inline_payload("scalar UDF", name)); } self.inner.try_decode_udf(name, buf) } fn try_encode_udaf(&self, node: &AggregateUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_agg_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_agg_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udaf(node, buf) } fn try_decode_udaf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udaf) = try_decode_python_agg_udf(buf)? { - return Ok(udaf); + if self.python_udf_inlining { + if let Some(udaf) = try_decode_python_agg_udf(buf)? { + return Ok(udaf); + } + } else if buf.starts_with(PY_AGG_UDF_MAGIC) { + return Err(refuse_inline_payload("aggregate UDF", name)); } self.inner.try_decode_udaf(name, buf) } fn try_encode_udwf(&self, node: &WindowUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_window_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_window_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udwf(node, buf) } fn try_decode_udwf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udwf) = try_decode_python_window_udf(buf)? { - return Ok(udwf); + if self.python_udf_inlining { + if let Some(udwf) = try_decode_python_window_udf(buf)? { + return Ok(udwf); + } + } else if buf.starts_with(PY_WINDOW_UDF_MAGIC) { + return Err(refuse_inline_payload("window UDF", name)); } self.inner.try_decode_udwf(name, buf) } } +/// Build the error returned by a strict codec when it receives an +/// inline Python-UDF payload it has been told not to deserialize. +fn refuse_inline_payload(kind: &str, name: &str) -> datafusion::error::DataFusionError { + datafusion::error::DataFusionError::Plan(format!( + "Refusing to deserialize inline Python {kind} '{name}': Python UDF \ + inlining is disabled on this session. Re-encode the bytes with \ + inlining enabled, or register '{name}' on the sender's session \ + before encode (and on the receiver before decode) so the UDF \ + travels by name." + )) +} + /// `PhysicalExtensionCodec` mirror of [`PythonLogicalCodec`] parked /// on the same `SessionContext`. Carries the Python-aware encoding /// hooks for physical-layer types (`ExecutionPlan`, `PhysicalExpr`) @@ -254,16 +296,30 @@ impl LogicalExtensionCodec for PythonLogicalCodec { #[derive(Debug)] pub struct PythonPhysicalCodec { inner: Arc, + python_udf_inlining: bool, } impl PythonPhysicalCodec { pub fn new(inner: Arc) -> Self { - Self { inner } + Self { + inner, + python_udf_inlining: true, + } } pub fn inner(&self) -> &Arc { &self.inner } + + /// See [`PythonLogicalCodec::with_python_udf_inlining`]. + pub fn with_python_udf_inlining(mut self, enabled: bool) -> Self { + self.python_udf_inlining = enabled; + self + } + + pub fn python_udf_inlining(&self) -> bool { + self.python_udf_inlining + } } impl Default for PythonPhysicalCodec { @@ -287,15 +343,19 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { } fn try_encode_udf(&self, node: &ScalarUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_scalar_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_scalar_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udf(node, buf) } fn try_decode_udf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udf) = try_decode_python_scalar_udf(buf)? { - return Ok(udf); + if self.python_udf_inlining { + if let Some(udf) = try_decode_python_scalar_udf(buf)? { + return Ok(udf); + } + } else if buf.starts_with(PY_SCALAR_UDF_MAGIC) { + return Err(refuse_inline_payload("scalar UDF", name)); } self.inner.try_decode_udf(name, buf) } @@ -313,29 +373,37 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { } fn try_encode_udaf(&self, node: &AggregateUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_agg_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_agg_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udaf(node, buf) } fn try_decode_udaf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udaf) = try_decode_python_agg_udf(buf)? { - return Ok(udaf); + if self.python_udf_inlining { + if let Some(udaf) = try_decode_python_agg_udf(buf)? { + return Ok(udaf); + } + } else if buf.starts_with(PY_AGG_UDF_MAGIC) { + return Err(refuse_inline_payload("aggregate UDF", name)); } self.inner.try_decode_udaf(name, buf) } fn try_encode_udwf(&self, node: &WindowUDF, buf: &mut Vec) -> Result<()> { - if try_encode_python_window_udf(node, buf)? { + if self.python_udf_inlining && try_encode_python_window_udf(node, buf)? { return Ok(()); } self.inner.try_encode_udwf(node, buf) } fn try_decode_udwf(&self, name: &str, buf: &[u8]) -> Result> { - if let Some(udwf) = try_decode_python_window_udf(buf)? { - return Ok(udwf); + if self.python_udf_inlining { + if let Some(udwf) = try_decode_python_window_udf(buf)? { + return Ok(udwf); + } + } else if buf.starts_with(PY_WINDOW_UDF_MAGIC) { + return Err(refuse_inline_payload("window UDF", name)); } self.inner.try_decode_udwf(name, buf) } diff --git a/crates/core/src/context.rs b/crates/core/src/context.rs index 96de01889..258a8b610 100644 --- a/crates/core/src/context.rs +++ b/crates/core/src/context.rs @@ -1407,6 +1407,28 @@ impl PySessionContext { physical_codec, }) } + + /// Toggle inline encoding of Python-defined UDFs on this session's + /// codec stack. Disable when producing bytes that must round-trip + /// through a non-Python decoder, or when reconstructing bytes from + /// an untrusted source via `Expr.from_bytes` (cloudpickle.loads + /// will not be invoked on the receiver). Pickle remains unsafe on + /// untrusted input regardless of this flag. + pub fn with_python_udf_inlining(&self, enabled: bool) -> Self { + let logical_codec = Arc::new( + PythonLogicalCodec::new(Arc::clone(self.logical_codec.inner())) + .with_python_udf_inlining(enabled), + ); + let physical_codec = Arc::new( + PythonPhysicalCodec::new(Arc::clone(self.physical_codec.inner())) + .with_python_udf_inlining(enabled), + ); + Self { + ctx: Arc::clone(&self.ctx), + logical_codec, + physical_codec, + } + } } impl PySessionContext { diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 0b927f685..010db3186 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -173,6 +173,37 @@ Practical considerations the captured state is large, mutable, or not portable to the worker's environment. +Disabling Python UDF inlining +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For a stricter wire format, call +:py:meth:`SessionContext.with_python_udf_inlining(False) +` on the session +producing or consuming the bytes. With inlining disabled, Python +UDFs travel by name only — the same way FFI-capsule UDFs do — and +the receiver must have a matching registration. + +Two use cases: + +* **Cross-language portability.** A non-Python decoder cannot + reconstruct a cloudpickled payload. Senders aimed at Java, C++, + or another Rust binary disable inlining and rely on the receiver + having compatible UDF registrations. +* **Untrusted-source decode.** With inlining disabled, + :py:meth:`Expr.from_bytes` never calls ``cloudpickle.loads`` on + the incoming bytes — an inline payload from a misbehaving sender + raises a clear error instead of executing arbitrary Python code. + +Mismatched configurations raise a descriptive error: an inline blob +fed to a strict receiver fails fast rather than silently dropping +into ``cloudpickle.loads``. + +Note that :py:func:`pickle.loads` itself remains unsafe on untrusted +input regardless of this setting — an attacker producing the outer +pickle envelope can execute arbitrary code before the codec ever +sees the bytes. The toggle only protects the +:py:meth:`Expr.from_bytes` API surface. + Security ~~~~~~~~ @@ -182,8 +213,10 @@ Security arbitrary Python code on the receiver — pickle is doing the work under the hood and pickle is unsafe on untrusted input. Only accept expressions from trusted sources. For untrusted-source - workflows, restrict senders to built-in functions and - pre-registered Rust-side UDFs. + workflows, disable Python UDF inlining (see above), restrict + senders to built-in functions and pre-registered Rust-side UDFs, + and avoid :py:func:`pickle.loads` on externally supplied bytes + entirely. Query-level distribution via datafusion-distributed --------------------------------------------------- diff --git a/python/datafusion/context.py b/python/datafusion/context.py index 5c3501941..f637f1f73 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -1769,3 +1769,28 @@ def with_physical_extension_codec(self, codec: Any) -> SessionContext: new = SessionContext.__new__(SessionContext) new.ctx = new_internal return new + + def with_python_udf_inlining(self, enabled: bool) -> SessionContext: + """Toggle inline encoding of Python-defined UDFs on this session. + + When ``True`` (the default), Python scalar, aggregate, and window + UDFs travel inside the serialized expression and are + reconstructed on the receiver without pre-registration. + + Set ``False`` to: + + * Produce serialized bytes that round-trip through a non-Python + decoder (cross-language portability). UDFs are stored by name + only; the receiver must have matching registrations. + * Refuse to reconstruct Python UDFs from + :meth:`Expr.from_bytes` input that may come from an untrusted + source — ``cloudpickle.loads`` will not be invoked. + + ``pickle.loads`` on untrusted bytes remains unsafe regardless of + this setting; only the ``to_bytes`` / ``from_bytes`` API is + affected. + """ + new_internal = self.ctx.with_python_udf_inlining(enabled) + new = SessionContext.__new__(SessionContext) + new.ctx = new_internal + return new diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 8d3d033e1..7e3819512 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -229,6 +229,62 @@ def test_window_udf_decodes_via_pickle_with_no_worker_ctx(self): assert "count_up" in decoded.canonical_name() +class TestPythonUdfInliningToggle: + """`SessionContext.with_python_udf_inlining(False)` opts out of + inline Python UDF encoding for both encode and decode paths.""" + + def _build_double_udf(self): + return udf( + lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), + [pa.int64()], + pa.int64(), + volatility="immutable", + name="double", + ) + + def test_strict_encoder_emits_smaller_blob(self): + """Strict mode skips cloudpickle of the Python callable, so the + encoded bytes are dramatically smaller than the inline form.""" + ctx_inline = SessionContext() + ctx_strict = ctx_inline.with_python_udf_inlining(False) + u = self._build_double_udf() + e = u(col("a")) + + blob_inline = e.to_bytes(ctx_inline) + blob_strict = e.to_bytes(ctx_strict) + + assert len(blob_strict) < len(blob_inline) // 4 + + def test_strict_roundtrip_via_registry(self): + """When both sender and receiver disable inlining, the UDF + travels by name only and the receiver resolves it from its + registered functions.""" + from datafusion import Expr + + strict_sender = SessionContext().with_python_udf_inlining(False) + u = self._build_double_udf() + blob = u(col("a")).to_bytes(strict_sender) + + receiver = SessionContext().with_python_udf_inlining(False) + receiver.register_udf(u) + restored = Expr.from_bytes(blob, ctx=receiver) + assert "double" in restored.canonical_name() + + def test_strict_decoder_refuses_inline_payload(self): + """An inline-encoded blob fed to a strict receiver raises with a + clear error rather than silently invoking cloudpickle.loads.""" + from datafusion import Expr + + sender = SessionContext() + u = self._build_double_udf() + blob = u(col("a")).to_bytes(sender) + + strict_receiver = SessionContext().with_python_udf_inlining(False) + strict_receiver.register_udf(u) + with pytest.raises(Exception, match="inlining is disabled"): + Expr.from_bytes(blob, ctx=strict_receiver) + + class TestWorkerCtxLifecycle: def test_set_and_clear(self): assert get_worker_ctx() is None From d7a1ff47df7f251d9c72ac4a2e38a5acf7905411 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 08:05:05 -0400 Subject: [PATCH 17/57] docs: pickle module security warning link + move user-facing prose to Python wrapper Two changes: * Reference link to the pickle module's official security warning in `https://docs.python.org/3/library/pickle.html#module-pickle`. Added in the user guide ("Disabling Python UDF inlining" note and the Security warning block) and in the Python `SessionContext.with_python_udf_inlining` docstring. The unqualified phrase "pickle is unsafe on untrusted input" assumed reader background that not every datafusion-python user has. * Strip the user-facing prose docstring from the Rust `PySessionContext::with_python_udf_inlining` method. Python wrappers are what users see via `help()` and Sphinx; the Rust doc-comment duplicated the same text and risked drifting from the Python version. Matches the surrounding methods (`with_logical_extension_codec`, `with_physical_extension_codec`) which carry no Rust doc-comment for the same reason. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/context.rs | 6 ------ .../user-guide/io/distributing_work.rst | 19 ++++++++++++------- python/datafusion/context.py | 6 ++++-- 3 files changed, 16 insertions(+), 15 deletions(-) diff --git a/crates/core/src/context.rs b/crates/core/src/context.rs index 258a8b610..1de8644ad 100644 --- a/crates/core/src/context.rs +++ b/crates/core/src/context.rs @@ -1408,12 +1408,6 @@ impl PySessionContext { }) } - /// Toggle inline encoding of Python-defined UDFs on this session's - /// codec stack. Disable when producing bytes that must round-trip - /// through a non-Python decoder, or when reconstructing bytes from - /// an untrusted source via `Expr.from_bytes` (cloudpickle.loads - /// will not be invoked on the receiver). Pickle remains unsafe on - /// untrusted input regardless of this flag. pub fn with_python_udf_inlining(&self, enabled: bool) -> Self { let logical_codec = Arc::new( PythonLogicalCodec::new(Arc::clone(self.logical_codec.inner())) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 010db3186..cdcb267fb 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -201,7 +201,10 @@ into ``cloudpickle.loads``. Note that :py:func:`pickle.loads` itself remains unsafe on untrusted input regardless of this setting — an attacker producing the outer pickle envelope can execute arbitrary code before the codec ever -sees the bytes. The toggle only protects the +sees the bytes (see the +`pickle module security warning +`_ in +the Python standard library docs). The toggle only protects the :py:meth:`Expr.from_bytes` API surface. Security @@ -211,12 +214,14 @@ Security Reconstructing an expression containing a Python UDF executes arbitrary Python code on the receiver — pickle is doing the work - under the hood and pickle is unsafe on untrusted input. Only - accept expressions from trusted sources. For untrusted-source - workflows, disable Python UDF inlining (see above), restrict - senders to built-in functions and pre-registered Rust-side UDFs, - and avoid :py:func:`pickle.loads` on externally supplied bytes - entirely. + under the hood and pickle is unsafe on untrusted input (see the + `pickle module security warning + `_ + in the Python standard library docs). Only accept expressions + from trusted sources. For untrusted-source workflows, disable + Python UDF inlining (see above), restrict senders to built-in + functions and pre-registered Rust-side UDFs, and avoid + :py:func:`pickle.loads` on externally supplied bytes entirely. Query-level distribution via datafusion-distributed --------------------------------------------------- diff --git a/python/datafusion/context.py b/python/datafusion/context.py index f637f1f73..f8b5f7a18 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -1787,8 +1787,10 @@ def with_python_udf_inlining(self, enabled: bool) -> SessionContext: source — ``cloudpickle.loads`` will not be invoked. ``pickle.loads`` on untrusted bytes remains unsafe regardless of - this setting; only the ``to_bytes`` / ``from_bytes`` API is - affected. + this setting (see the `pickle module security warning + `_ + in the Python standard library docs). Only the + ``to_bytes`` / ``from_bytes`` API is affected. """ new_internal = self.ctx.with_python_udf_inlining(enabled) new = SessionContext.__new__(SessionContext) From ad0f956d05203d3885f2cbbc1ec47f8e9f3b4d1e Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 08:18:58 -0400 Subject: [PATCH 18/57] docs(ray-example): drop unnecessary UDF registration MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The actor was calling `ctx.register_udf(...)` and `set_worker_ctx(ctx)` to make the inbound expression's UDF resolvable on the worker. With Python scalar/aggregate/window UDFs now traveling inside the serialized expression, neither call is necessary — the actor just needs a `SessionContext` to evaluate against. Also drops the parallel `sender.register_udf(...)` in the driver; an expression built with a `udf(...)` callable carries its own reference and does not require the UDF to be registered on the driver session. Result: each actor is a few lines (one `SessionContext`, one `evaluate` method) — what the inline-UDF story is actually trying to demonstrate. Co-Authored-By: Claude Opus 4.7 (1M context) --- examples/ray_pickle_expr.py | 26 ++++++++------------------ 1 file changed, 8 insertions(+), 18 deletions(-) diff --git a/examples/ray_pickle_expr.py b/examples/ray_pickle_expr.py index 9123e4cfa..8ef6140b2 100644 --- a/examples/ray_pickle_expr.py +++ b/examples/ray_pickle_expr.py @@ -17,11 +17,9 @@ """Distribute DataFusion expressions to Ray actors. -Build an expression in the driver, ship it to a pool of Ray actors, and have -each actor evaluate it against its own slice of data. Each actor sets up -its own :class:`SessionContext` once in `__init__` and registers any UDFs -it needs to resolve by name. Python scalar UDFs travel with the shipped -expression and need no actor-side pre-registration. +Build an expression in the driver, ship it to a pool of Ray actors, and +have each actor evaluate it against its own slice of data. Python UDFs +travel with the shipped expression — no actor-side registration needed. Prerequisites: pip install ray @@ -33,11 +31,10 @@ import pyarrow as pa import ray from datafusion import Expr, SessionContext, col, lit, udf -from datafusion.ipc import set_worker_ctx def _build_double_udf(): - """Return the demo UDF used by the actors.""" + """Return the demo UDF used by the driver.""" return udf( lambda arr: pa.array([(v.as_py() or 0) * 2 for v in arr]), [pa.int64()], @@ -52,18 +49,13 @@ class DataFusionWorker: """A Ray actor with a private :class:`SessionContext`.""" def __init__(self) -> None: - ctx = SessionContext() - ctx.register_udf(_build_double_udf()) - # Install the actor's SessionContext as its worker context; - # expressions reconstructed in this actor will resolve their - # by-name references against it. - set_worker_ctx(ctx) - self._ctx = ctx + self._ctx = SessionContext() def evaluate(self, expr: Expr, batch_pylist: list[int]) -> list[int]: """Run the expression against an in-memory batch.""" - # `expr` arrived here via Ray's automatic argument serialization — - # no manual pickle handling needed in user code. + # `expr` arrived here via Ray's automatic argument serialization; + # the Python UDF inside it was reconstructed from the bytes — no + # pre-registration on this actor required. df = self._ctx.from_pydict({"a": batch_pylist}) out = df.with_column("result", expr).select("result") return out.to_pydict()["result"] @@ -72,8 +64,6 @@ def evaluate(self, expr: Expr, batch_pylist: list[int]) -> list[int]: def main() -> None: ray.init(ignore_reinit_error=True) - sender = SessionContext() - sender.register_udf(_build_double_udf()) expr = _build_double_udf()(col("a")) + lit(1) workers = [DataFusionWorker.remote() for _ in range(2)] From 8130ab2022f606ddaff900abae2cfd0e2201424b Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:06:59 -0400 Subject: [PATCH 19/57] feat: sender-side session context for outbound pickles `SessionContext.with_python_udf_inlining(False)` previously had no effect on `pickle.dumps(expr)` because `__reduce__` called `to_bytes()` without a context. Add a thread-local sender slot (`datafusion.ipc.set_sender_ctx`) that `__reduce__` consults, so the inlining toggle flows through pickle. Symmetric to the existing worker slot. Also switch the worker-side decode fallback from a freshly constructed `SessionContext` to `SessionContext.global_ctx()` so expressions arriving with no explicit/worker context can still resolve names registered on the global singleton. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../user-guide/io/distributing_work.rst | 107 +++++++++++++++- python/datafusion/context.py | 10 ++ python/datafusion/expr.py | 16 ++- python/datafusion/ipc.py | 69 +++++++++- python/tests/test_pickle_expr.py | 120 +++++++++++++++++- 5 files changed, 307 insertions(+), 15 deletions(-) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index cdcb267fb..5eaf2b4ca 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -116,6 +116,85 @@ What travels with the expression :py:class:`SessionContext`. Without that registration, evaluation raises an error. +Session contexts at a glance +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +There is only one type — :py:class:`SessionContext`. It can occupy +up to four *slots* in a running program: + +.. list-table:: + :header-rows: 1 + :widths: 12 18 40 30 + + * - Slot + - Lifetime + - Purpose + - Set how + * - User-held + - Local variable / attribute + - Build and run queries + - ``ctx = SessionContext(...)`` + * - Global + - Process singleton (lazy-init) + - Backs module-level + :py:func:`~datafusion.io.read_parquet`, + :py:func:`~datafusion.io.read_csv`, + :py:func:`~datafusion.io.read_json`, + :py:func:`~datafusion.io.read_avro`; final fallback for + :py:meth:`Expr.from_bytes` + - Implicit; access via + :py:meth:`SessionContext.global_ctx` + * - Sender + - Thread-local on the driver + - Codec settings for outbound :py:func:`pickle.dumps` / + :py:meth:`Expr.to_bytes` without ``ctx`` + - :py:func:`~datafusion.ipc.set_sender_ctx` + * - Worker + - Thread-local on the worker + - Function registry for inbound :py:func:`pickle.loads` / + :py:meth:`Expr.from_bytes` without ``ctx`` + - :py:func:`~datafusion.ipc.set_worker_ctx` + +The same :py:class:`SessionContext` object may occupy more than one +slot simultaneously — installing it into a slot is a reference, not +a copy. + +**Non-distributed user.** One user-held context. The global slot is +invisible unless you call top-level ``read_*`` helpers. Sender and +worker slots are unused. + +**Distributed user.** Two questions to answer: + +1. *Driver side — what wire format do I want?* The default (Python UDF + inlining on) is self-contained; you do not need a sender context. + To opt into the strict format, + :py:func:`~datafusion.ipc.set_sender_ctx` + with a session built via + :py:meth:`SessionContext.with_python_udf_inlining(False) + `. + +2. *Worker side — what registrations does decode need?* For built-ins + and inline Python UDFs, nothing. For FFI-capsule UDFs (or + strict-mode round-trips that travel by name), call + :py:func:`~datafusion.ipc.set_worker_ctx` once per worker with a + context that has the relevant registrations. + +Resolution order on the worker side is *explicit argument → +worker context → global context.* Explicit ``ctx=`` on +:py:meth:`Expr.from_bytes` always wins; the sender slot is ignored +on decode and the worker slot is ignored on encode. + +Sharp edges: + +* Sender and worker slots are **thread-local**. Background threads + on either side see ``None`` until they install their own. +* The global slot persists across ``fork`` workers (copy-on-write + memory inherit) but not across ``spawn`` / ``forkserver`` workers + (fresh process — register or install a worker context on + start-up). +* The inlining toggle is per-context state, not a global switch. + Two contexts with different toggles can coexist in one process. + Registering shared UDFs on workers ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -143,9 +222,10 @@ as the *worker context*: Inside a worker, expressions arriving from the driver resolve their by-name references against the installed worker context. If no worker -context is installed, a fresh empty :py:class:`SessionContext` is -used — fine for expressions that only reference built-ins and Python -UDFs, but FFI-capsule-backed registrations will fail to resolve. +context is installed, the global :py:class:`SessionContext` is used — +fine for expressions that only reference built-ins and Python UDFs, +but FFI-capsule-backed registrations must be installed on the global +context to resolve. Python 3.14 default change ~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -198,6 +278,27 @@ Mismatched configurations raise a descriptive error: an inline blob fed to a strict receiver fails fast rather than silently dropping into ``cloudpickle.loads``. +To make the toggle apply through :py:func:`pickle.dumps` (which +calls :py:meth:`Expr.to_bytes` with no context), install the strict +session as the driver's *sender context*: + +.. code-block:: python + + from datafusion import SessionContext + from datafusion.ipc import set_sender_ctx + + set_sender_ctx(SessionContext().with_python_udf_inlining(False)) + # Every subsequent pickle.dumps(expr) on this thread encodes + # without inlining the Python callable. + +Pair with a matching strict worker context +(:py:func:`~datafusion.ipc.set_worker_ctx`) so the ``pickle.loads`` +side also refuses inline payloads. Explicit +:py:meth:`Expr.to_bytes(ctx) ` and +:py:meth:`Expr.from_bytes(blob, ctx=ctx) ` calls +honor the supplied ``ctx`` directly and ignore the sender / worker +contexts. + Note that :py:func:`pickle.loads` itself remains unsafe on untrusted input regardless of this setting — an attacker producing the outer pickle envelope can execute arbitrary code before the codec ever diff --git a/python/datafusion/context.py b/python/datafusion/context.py index f8b5f7a18..9019eebb9 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -1786,6 +1786,16 @@ def with_python_udf_inlining(self, enabled: bool) -> SessionContext: :meth:`Expr.from_bytes` input that may come from an untrusted source — ``cloudpickle.loads`` will not be invoked. + The toggle applies directly to :meth:`Expr.to_bytes` / + :meth:`Expr.from_bytes` calls that pass this session as their + ``ctx`` argument. To make the toggle apply through + :func:`pickle.dumps` (which calls :meth:`Expr.to_bytes` with no + context), install this session as the driver's sender context + via :func:`datafusion.ipc.set_sender_ctx` — and install it as + the worker's context via + :func:`datafusion.ipc.set_worker_ctx` for the corresponding + :func:`pickle.loads`. + ``pickle.loads`` on untrusted bytes remains unsafe regardless of this setting (see the `pickle module security warning `_ diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 5a0b8e5fb..7eb1cd0df 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -457,9 +457,10 @@ def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: function references that travel by name — aggregate UDFs, window UDFs, FFI UDFs. When ``ctx`` is ``None`` the worker context installed via :func:`datafusion.ipc.set_worker_ctx` is consulted; - if no worker context is installed, a fresh + if no worker context is installed, the global :class:`SessionContext` is used (sufficient for built-ins and - Python scalar UDFs). + Python scalar UDFs, plus any UDFs registered on the global + context). """ from datafusion.ipc import _resolve_ctx @@ -475,10 +476,17 @@ def __reduce__(self) -> tuple: UDFs require pre-registration on the worker. The worker's :class:`SessionContext` for resolving those references is looked up via :func:`datafusion.ipc.set_worker_ctx`, falling - back to a fresh empty :class:`SessionContext` if none has been + back to the global :class:`SessionContext` if none has been installed on the worker. + + The encoding side honors a driver-side sender context installed + via :func:`datafusion.ipc.set_sender_ctx` — that is how + :meth:`SessionContext.with_python_udf_inlining` propagates + through ``pickle.dumps``. """ - return (Expr._reconstruct, (self.to_bytes(),)) + from datafusion.ipc import get_sender_ctx + + return (Expr._reconstruct, (self.to_bytes(get_sender_ctx()),)) @classmethod def _reconstruct(cls, proto_bytes: bytes) -> Expr: diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 33869da34..ffcb3d1be 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -15,7 +15,7 @@ # specific language governing permissions and limitations # under the License. -"""Worker-side setup for distributing DataFusion expressions. +"""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 @@ -38,6 +38,24 @@ inside the shipped expression itself and do not need pre-registration on the worker. +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: + +>>> # doctest: +SKIP +>>> from datafusion import SessionContext +>>> from datafusion.ipc import set_sender_ctx +>>> +>>> driver_ctx = SessionContext().with_python_udf_inlining(False) +>>> set_sender_ctx(driver_ctx) +>>> pickle.dumps(expr) # encoded with inlining disabled + +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``. + See :doc:`/user-guide/io/distributing_work` for the full pattern. """ @@ -51,8 +69,11 @@ __all__ = [ + "clear_sender_ctx", "clear_worker_ctx", + "get_sender_ctx", "get_worker_ctx", + "set_sender_ctx", "set_worker_ctx", ] @@ -74,10 +95,10 @@ def set_worker_ctx(ctx: SessionContext) -> None: def clear_worker_ctx() -> None: """Remove this worker's installed :class:`SessionContext`. - After clearing, expressions reconstructed in this worker fall back to a - fresh empty :class:`SessionContext` — adequate for built-ins and Python + 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 will fail to resolve. + capsule protocol must be registered on the global context to resolve. """ if hasattr(_local, "ctx"): del _local.ctx @@ -88,12 +109,48 @@ def get_worker_ctx() -> SessionContext | None: 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. + """ + _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). + """ + 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``.""" + 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 > fresh context. + 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. """ if explicit_ctx is not None: return explicit_ctx @@ -102,4 +159,4 @@ def _resolve_ctx( return worker from datafusion.context import SessionContext # noqa: PLC0415 - return SessionContext() + return SessionContext.global_ctx() diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 7e3819512..c2dd42db9 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -33,15 +33,24 @@ import pyarrow as pa import pytest from datafusion import Expr, SessionContext, col, lit, udf -from datafusion.ipc import clear_worker_ctx, get_worker_ctx, set_worker_ctx +from datafusion.ipc import ( + clear_sender_ctx, + clear_worker_ctx, + get_sender_ctx, + get_worker_ctx, + set_sender_ctx, + set_worker_ctx, +) @pytest.fixture(autouse=True) def _reset_worker_ctx(): - """Ensure every test starts with no worker context installed.""" + """Ensure every test starts with no worker or sender context installed.""" clear_worker_ctx() + clear_sender_ctx() yield clear_worker_ctx() + clear_sender_ctx() def _double_udf(): @@ -284,6 +293,80 @@ def test_strict_decoder_refuses_inline_payload(self): with pytest.raises(Exception, match="inlining is disabled"): Expr.from_bytes(blob, ctx=strict_receiver) + def test_sender_ctx_propagates_through_pickle(self): + """`set_sender_ctx` makes `pickle.dumps` use a strict codec. + + Without a sender context, pickle defaults to the inline codec and + the blob is large. With a strict sender context installed, the + blob shrinks because the Python callable is encoded by name + instead of cloudpickled. + """ + u = self._build_double_udf() + e = u(col("a")) + + blob_default = pickle.dumps(e) + + strict_sender = SessionContext().with_python_udf_inlining(False) + set_sender_ctx(strict_sender) + try: + blob_strict = pickle.dumps(e) + finally: + clear_sender_ctx() + + assert len(blob_strict) < len(blob_default) // 4 + + def test_sender_ctx_strict_roundtrip_via_pickle(self): + """End-to-end pickle round-trip with strict mode on both sides. + + Driver installs a strict sender context. Worker installs a + matching strict context with the UDF registered. The UDF + travels by name through `pickle.dumps` / `pickle.loads`. + """ + u = self._build_double_udf() + e = u(col("a")) + + strict_sender = SessionContext().with_python_udf_inlining(False) + set_sender_ctx(strict_sender) + try: + blob = pickle.dumps(e) + finally: + clear_sender_ctx() + + worker = SessionContext().with_python_udf_inlining(False) + worker.register_udf(u) + set_worker_ctx(worker) + try: + decoded = pickle.loads(blob) + finally: + clear_worker_ctx() + + assert "double" in decoded.canonical_name() + + def test_sender_ctx_strict_pickle_refused_by_inline_worker(self): + """A strict-encoded blob still decodes fine on an inline worker + because the wire format is the same default-codec by-name form. + Sanity check: cross-config works as long as the receiver can + resolve the name.""" + u = self._build_double_udf() + e = u(col("a")) + + strict_sender = SessionContext().with_python_udf_inlining(False) + set_sender_ctx(strict_sender) + try: + blob = pickle.dumps(e) + finally: + clear_sender_ctx() + + worker = SessionContext() + worker.register_udf(u) + set_worker_ctx(worker) + try: + decoded = pickle.loads(blob) + finally: + clear_worker_ctx() + + assert "double" in decoded.canonical_name() + class TestWorkerCtxLifecycle: def test_set_and_clear(self): @@ -318,3 +401,36 @@ def worker(): assert seen_in_thread[1] is not main_ctx # Main thread's ctx is unchanged by the thread's actions. assert get_worker_ctx() is main_ctx + + +class TestSenderCtxLifecycle: + def test_set_and_clear(self): + assert get_sender_ctx() is None + ctx = SessionContext() + set_sender_ctx(ctx) + assert get_sender_ctx() is ctx + clear_sender_ctx() + assert get_sender_ctx() is None + + def test_clear_when_unset_is_noop(self): + clear_sender_ctx() # no error + assert get_sender_ctx() is None + + def test_thread_local_isolation(self): + main_ctx = SessionContext() + set_sender_ctx(main_ctx) + + seen_in_thread: list = [] + + def worker(): + seen_in_thread.append(get_sender_ctx()) + set_sender_ctx(SessionContext()) + seen_in_thread.append(get_sender_ctx()) + + t = threading.Thread(target=worker) + t.start() + t.join() + + assert seen_in_thread[0] is None + assert seen_in_thread[1] is not main_ctx + assert get_sender_ctx() is main_ctx From 1300076bce5a2aefcaad29f2536bc0aa8f799f19 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:08:35 -0400 Subject: [PATCH 20/57] fix(ipc): doctest collection failure on module docstring MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `>>> # doctest: +SKIP` on its own line is not valid — doctest directives must attach to an example line. CI runs `pytest --doctest-modules` and `pyproject.toml` includes `python/datafusion` in testpaths, so the bad directive aborted collection across every Python version. Replace the two illustrative `>>>` blocks with `.. code-block:: python` since they were never meant to execute under doctest. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/datafusion/ipc.py | 32 +++++++++++++++++--------------- 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index ffcb3d1be..48b92a2fd 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -25,14 +25,15 @@ the shipped expression — install a configured :class:`SessionContext` once per worker: ->>> # doctest: +SKIP ->>> 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) +.. 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 @@ -43,13 +44,14 @@ :meth:`SessionContext.with_python_udf_inlining` to every pickled expression on this thread: ->>> # doctest: +SKIP ->>> from datafusion import SessionContext ->>> from datafusion.ipc import set_sender_ctx ->>> ->>> driver_ctx = SessionContext().with_python_udf_inlining(False) ->>> set_sender_ctx(driver_ctx) ->>> pickle.dumps(expr) # encoded with inlining disabled +.. code-block:: python + + from datafusion import SessionContext + from datafusion.ipc import set_sender_ctx + + driver_ctx = SessionContext().with_python_udf_inlining(False) + set_sender_ctx(driver_ctx) + pickle.dumps(expr) # encoded with inlining disabled Without a sender context the default codec is used (Python UDF inlining on). The sender context only affects pickle / ``to_bytes`` From d5055e1fe91ee937d67d7258ee500cf0abe73019 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:13:12 -0400 Subject: [PATCH 21/57] docs(example): multiprocessing pool with distinct expressions per worker MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The Ray example fans the same expression out to every actor — it demonstrates pickling works but doesn't show why expression pickling is necessary (a by-name registration on each worker would suffice for that pattern). This example builds a list of parametric expressions in the driver, each closing over a different threshold value, and ships one per worker via `multiprocessing.Pool`. The closure state forces the cloudpickle path: a by-name registration on the worker would collapse every threshold into the same callable and lose the per-task value. Workers return results plus their PID so the driver output makes the cross-process distribution visible. Also mixes a scalar UDF in an aggregate and a pure UDAF so both kinds round-trip through pickle. README and the distributing-work "See also" section now link the example. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../user-guide/io/distributing_work.rst | 3 + examples/README.md | 1 + examples/multiprocessing_pickle_expr.py | 165 ++++++++++++++++++ 3 files changed, 169 insertions(+) create mode 100644 examples/multiprocessing_pickle_expr.py diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 5eaf2b4ca..d88457b4d 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -354,4 +354,7 @@ See also -------- * :py:mod:`datafusion.ipc` — worker context API. +* ``examples/multiprocessing_pickle_expr.py`` — runnable + ``multiprocessing.Pool`` example that ships a different parametric + expression to each worker and collects results back. * ``examples/ray_pickle_expr.py`` — runnable Ray actor example. diff --git a/examples/README.md b/examples/README.md index df082506c..e0e3056d9 100644 --- a/examples/README.md +++ b/examples/README.md @@ -46,6 +46,7 @@ Here is a direct link to the file used in the examples: ### Distributing DataFusion expressions +- [Fan out distinct expressions to a multiprocessing pool](./multiprocessing_pickle_expr.py) - [Distribute expression evaluation across Ray actors](./ray_pickle_expr.py) ### Substrait Support diff --git a/examples/multiprocessing_pickle_expr.py b/examples/multiprocessing_pickle_expr.py new file mode 100644 index 000000000..3832f96de --- /dev/null +++ b/examples/multiprocessing_pickle_expr.py @@ -0,0 +1,165 @@ +# 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. + +"""Distribute different DataFusion expressions to worker processes. + +Builds a list of parametric expressions in the driver — each closing +over a different threshold value — ships one per worker via +``multiprocessing.Pool``, and collects the results back. The closure +state forces the cloudpickle path (a by-name registration would lose +the captured threshold), so this is a real test of the expression- +pickling story rather than a same-expression fan-out. + +Worker layout: + +* Each worker receives a different ``(expr, label)`` task. +* Each worker materializes the shared dataset locally and runs its + own expression against it. +* The result and the worker's PID travel back to the driver, so the + output makes it visible that the work was spread across processes. + +Run: + python examples/multiprocessing_pickle_expr.py +""" + +from __future__ import annotations + +import multiprocessing as mp +import os + +import pyarrow as pa +from datafusion import Expr, SessionContext, col, udaf, udf +from datafusion import functions as F +from datafusion.user_defined import Accumulator, AggregateUDF, ScalarUDF + +# A shared input dataset. In a production pipeline this would live on +# object storage; here we hand-roll a small batch so the example runs +# without any I/O setup. +DATASET = { + "value": [3, 17, 42, 5, 88, 21, 9, 56, 4, 73, 12, 31], +} + + +def make_above_threshold_udf(threshold: int) -> ScalarUDF: + """Build a scalar UDF that returns 1 where ``value > threshold`` else 0. + + The threshold is captured in the closure, so cloudpickle has to + walk into the function body to ship the value across processes — + a by-name registration on the worker would collapse every + threshold into the same callable and lose the per-task state. + """ + + def above(arr: pa.Array) -> pa.Array: + return pa.array([1 if (v.as_py() or 0) > threshold else 0 for v in arr]) + + return udf( + above, + [pa.int64()], + pa.int64(), + volatility="immutable", + name=f"above_{threshold}", + ) + + +class _SumAccumulator(Accumulator): + """Tiny aggregate UDF state used to demonstrate UDAFs travel too.""" + + def __init__(self) -> None: + self._total = 0 + + def state(self) -> list[pa.Scalar]: + return [pa.scalar(self._total, type=pa.int64())] + + def update(self, values: pa.Array) -> None: + for v in values: + self._total += v.as_py() or 0 + + def merge(self, states: list[pa.Array]) -> None: + for s in states: + self._total += s[0].as_py() + + def evaluate(self) -> pa.Scalar: + return pa.scalar(self._total, type=pa.int64()) + + +def _build_sum_udaf() -> AggregateUDF: + return udaf( + _SumAccumulator, + [pa.int64()], + pa.int64(), + [pa.int64()], + "immutable", + name="my_sum", + ) + + +def evaluate_in_worker(task: tuple[str, Expr]) -> tuple[str, int, int]: + """Run one expression against the shared dataset. + + ``task`` arrived here via the pool's automatic pickling. The Python + callable inside the expression (including its captured threshold) + was reconstructed by the codec — the worker did not have to + register anything before this call. + """ + label, expr = task + ctx = SessionContext() + df = ctx.from_pydict(DATASET) + # ``expr`` is an aggregate over the whole batch; ``aggregate`` keeps + # a single row of output, which we read as a Python int. + result_df = df.aggregate([], [expr.alias("result")]) + result = result_df.to_pydict()["result"][0] + return label, result, os.getpid() + + +def build_tasks() -> list[tuple[str, Expr]]: + """Return ``(label, expr)`` pairs — one task per worker invocation. + + Mixes scalar-UDF-in-aggregate and pure-aggregate work to show both + UDF kinds round-tripping through pickle. + """ + sum_udaf = _build_sum_udaf() + tasks: list[tuple[str, Expr]] = [] + + # Three "count values strictly above threshold T" tasks built from + # closure-capturing scalar UDFs. + for threshold in (10, 30, 60): + above_udf = make_above_threshold_udf(threshold) + tasks.append((f"count_above_{threshold}", F.sum(above_udf(col("value"))))) + + # One pure aggregate UDF task. + tasks.append(("custom_sum", sum_udaf(col("value")))) + + return tasks + + +def main() -> None: + tasks = build_tasks() + + # ``forkserver`` works on every POSIX platform and is the Python 3.14 + # default for POSIX. ``spawn`` would also work; ``fork`` is unsafe + # with pyarrow/tokio on macOS. + mp_ctx = mp.get_context("forkserver") + with mp_ctx.Pool(processes=min(4, len(tasks))) as pool: + results = pool.map(evaluate_in_worker, tasks) + + print(f"driver pid: {os.getpid()}") + for label, value, worker_pid in results: + print(f" [{label:>16}] = {value:>6} (worker pid: {worker_pid})") + + +if __name__ == "__main__": + main() From c8c9306ab2da553fde1b4320af0b04acdcb96d7a Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:19:40 -0400 Subject: [PATCH 22/57] fix(codec): preserve UDAF state field metadata across pickle round-trip MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `encode_python_agg_udf` wrote the full state schema (names, nullability, key/value metadata) to its IPC payload, but `decode_python_agg_udf` collapsed it to `Vec` and `PythonFunctionAggregateUDF::from_parts` re-synthesized field names as `"{i}"`. An aggregate UDF arriving via FFI capsule with named state fields would lose those names on round-trip; a downstream `StateFieldsArgs` consumer keyed off `field.name()` or `field.nullable()` would see different output before vs. after serialization. Decode the IPC schema into `Vec` and feed it directly to `from_parts`, which now takes fields instead of types. The Python construction path still synthesizes names through `new` — that codepath only receives `Vec` from PyArrow, so there is nothing better to preserve. Add an end-to-end pickle round-trip test that runs the decoded UDAF and merges across partitions, exercising the rebuilt state schema. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 14 ++++++++------ crates/core/src/udaf.rs | 18 ++++++++++++------ python/tests/test_pickle_expr.py | 12 ++++++++++++ 3 files changed, 32 insertions(+), 12 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 5ad67411f..e7431511f 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -831,11 +831,13 @@ fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult = state_schema - .fields() - .iter() - .map(|f| f.data_type().clone()) - .collect(); + // Preserve the encoded state field metadata (names, nullability, + // arbitrary key/value attributes) so the post-decode UDF reports + // the same state schema as the sender's instance — important for + // accumulators whose `StateFieldsArgs` consumers key off names or + // nullability rather than positional `DataType`. + let state_fields: Vec = + state_schema.fields().iter().cloned().collect(); let volatility = datafusion_python_util::parse_volatility(&volatility_str) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; @@ -845,7 +847,7 @@ fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult` is available (e.g. the Python constructor path, + /// where field names are synthesized). pub(crate) fn from_parts( name: String, accumulator: Py, input_types: Vec, return_type: DataType, - state_types: Vec, + state_fields: Vec, volatility: Volatility, ) -> Self { - Self::new( + Self { name, accumulator, - input_types, + signature: Signature::exact(input_types, volatility), return_type, - state_types, - volatility, - ) + state_fields, + } } } diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index c2dd42db9..853c77e1e 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -194,6 +194,18 @@ def test_agg_udf_decodes_via_pickle_with_no_worker_ctx(self): decoded = pickle.loads(blob) assert "count_all" in decoded.canonical_name() + def test_agg_udf_evaluates_after_roundtrip(self): + """End-to-end: the decoded aggregate UDF runs and merges across + partitions, exercising the round-tripped state-field schema.""" + u = self._build_aggregate_udf() + e = u(col("a")) + decoded = pickle.loads(pickle.dumps(e)) + + ctx = SessionContext() + df = ctx.from_pydict({"a": [1, 2, 3, 4, 5]}) + out = df.aggregate([], [decoded.alias("n")]).to_pydict() + assert out["n"] == [5] + class TestWindowUDFCodec: """Python window UDFs travel inline like scalar UDFs.""" From 0b8672e9071eca10c640a82fc5502370b017212a Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:21:27 -0400 Subject: [PATCH 23/57] refactor(codec): pin Volatility wire format to explicit match MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `format!("{:?}", signature.volatility).to_lowercase()` coupled the Python UDF wire format to upstream `Volatility`'s `Debug` repr. If DataFusion ever changes that repr (renames a variant, adds a field, adopts non-lowercase casing), encoded bytes would silently fail to decode — `parse_volatility` only accepts "immutable" / "stable" / "volatile". Replace all three sites (scalar / window / aggregate encoders) with a `volatility_wire_str(Volatility) -> &'static str` helper that exhaustively matches the three variants. The exhaustive match also turns a future upstream `Volatility` variant addition into a compile error instead of a runtime decode failure. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index e7431511f..435f90b86 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -86,7 +86,7 @@ use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; use datafusion::logical_expr::{ AggregateUDF, AggregateUDFImpl, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, - TypeSignature, WindowUDF, WindowUDFImpl, + TypeSignature, Volatility, WindowUDF, WindowUDFImpl, }; use datafusion::physical_expr::PhysicalExpr; use datafusion::physical_plan::ExecutionPlan; @@ -494,7 +494,7 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py let return_schema_bytes = schema_to_ipc_bytes(&return_schema) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let volatility = format!("{:?}", signature.volatility).to_lowercase(); + let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( py, @@ -577,6 +577,18 @@ fn schema_from_ipc_bytes(bytes: &[u8]) -> arrow::error::Result { Ok(reader.schema().as_ref().clone()) } +/// Stable wire-format string for a `Volatility`. Pinned to the three +/// tokens [`datafusion_python_util::parse_volatility`] accepts, so an +/// upstream change to `Volatility`'s `Debug` repr cannot silently +/// produce bytes the decoder rejects. +fn volatility_wire_str(v: Volatility) -> &'static str { + match v { + Volatility::Immutable => "immutable", + Volatility::Stable => "stable", + Volatility::Volatile => "volatile", + } +} + // ============================================================================= // Shared Python window UDF encode / decode helpers // @@ -641,7 +653,7 @@ fn encode_python_window_udf(py: Python<'_>, udf: &PythonFunctionWindowUDF) -> Py let return_schema_bytes = schema_to_ipc_bytes(&return_schema) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let volatility = format!("{:?}", signature.volatility).to_lowercase(); + let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( py, @@ -776,7 +788,7 @@ fn encode_python_agg_udf(py: Python<'_>, udf: &PythonFunctionAggregateUDF) -> Py let state_schema_bytes = schema_to_ipc_bytes(&Schema::new(state_fields)) .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let volatility = format!("{:?}", signature.volatility).to_lowercase(); + let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( py, From d722bdcf31535af0d37b8b550d5ee7913d6140c9 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:35:52 -0400 Subject: [PATCH 24/57] fix: drop Python callable from UDF Hash impls MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `Python::attach(|py| self.func.bind(py).hash().unwrap_or(0))` silently collapsed every unhashable closure (e.g. a lambda with an unhashable captured value) to the same hash bucket — the worst case for hashmap lookups, and arrived at via `unwrap_or` rather than a deliberate design choice. Hash on the identifying header (name + signature + return field / state fields) only. The Rust `Hash` contract requires `a == b ⇒ hash(a) == hash(b)`, not the converse, so a coarser hash is still sound — `PartialEq` (which still calls Python `eq` on the callable) disambiguates two UDFs that share a header but differ in their callable. Applied symmetrically to scalar, aggregate, and window UDFs. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/udaf.rs | 7 +++---- crates/core/src/udf.rs | 15 +++++++++------ crates/core/src/udwf.rs | 7 +++---- 3 files changed, 15 insertions(+), 14 deletions(-) diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index ca8817563..012583928 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -249,16 +249,15 @@ impl PartialEq for PythonFunctionAggregateUDF { impl std::hash::Hash for PythonFunctionAggregateUDF { fn hash(&self, state: &mut H) { + // See `PythonFunctionScalarUDF`'s `Hash` impl for the + // rationale: hash the identifying header only and let + // `PartialEq` disambiguate callables. self.name.hash(state); self.signature.hash(state); self.return_type.hash(state); for f in &self.state_fields { f.hash(state); } - Python::attach(|py| { - let py_hash = self.accumulator.bind(py).hash().unwrap_or(0); - state.write_isize(py_hash); - }); } } diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index 875a7352c..63fccd906 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -106,15 +106,18 @@ impl PartialEq for PythonFunctionScalarUDF { impl Hash for PythonFunctionScalarUDF { fn hash(&self, state: &mut H) { + // Hash only the identifying header (name + signature + return + // field). Skipping `func` is intentional: the Rust `Hash` + // contract requires `a == b ⇒ hash(a) == hash(b)`, not the + // converse, so a coarser hash is sound — `PartialEq` still + // disambiguates two UDFs with the same header but distinct + // callables. Falling back to a sentinel on `py_hash` failure + // (as a prior revision did) silently mapped every unhashable + // closure to the same bucket; that is the worst case for a + // hashmap and is what this rewrite avoids. self.name.hash(state); self.signature.hash(state); self.return_field.hash(state); - - Python::attach(|py| { - let py_hash = self.func.bind(py).hash().unwrap_or(0); // Handle unhashable objects - - state.write_isize(py_hash); - }); } } diff --git a/crates/core/src/udwf.rs b/crates/core/src/udwf.rs index 33349247c..d27d36e3c 100644 --- a/crates/core/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -340,13 +340,12 @@ impl PartialEq for PythonFunctionWindowUDF { impl std::hash::Hash for PythonFunctionWindowUDF { fn hash(&self, state: &mut H) { + // See `PythonFunctionScalarUDF`'s `Hash` impl for the + // rationale: hash the identifying header only and let + // `PartialEq` disambiguate evaluators. self.name.hash(state); self.signature.hash(state); self.return_type.hash(state); - Python::attach(|py| { - let py_hash = self.evaluator.bind(py).hash().unwrap_or(0); - state.write_isize(py_hash); - }); } } From b5684bcf70777b4f633214ca0d1766644ff8333c Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:37:39 -0400 Subject: [PATCH 25/57] perf(codec): cache cloudpickle module handle in a OnceLock The six encode/decode helpers re-resolved `cloudpickle` on every call. `py.import` is backed by `sys.modules` so each lookup is cheap, but a plan with many Python UDFs pays the dict-walk + bind cost per UDF for no reason. Cache the module once in a `OnceLock>` and hand out a fresh `Bound` against the caller's GIL token. Race-safe: two threads can both miss and import, but CPython already deduplicates the import via `sys.modules` and the losing `set` still leaves the winning value in the slot, so both threads return the same module. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 41 +++++++++++++++++++++++++++++++++------- 1 file changed, 34 insertions(+), 7 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 435f90b86..2e03b2a83 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -75,7 +75,7 @@ //! `inner`; the encoder/decoder hooks for each kind are added as the //! corresponding Python-side type becomes serializable. -use std::sync::Arc; +use std::sync::{Arc, OnceLock}; use arrow::datatypes::{Field, Schema, SchemaRef}; use arrow::ipc::reader::StreamReader; @@ -470,7 +470,7 @@ pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result, udf: &PythonFunctionScalarUDF) -> PyResult> { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let signature = udf.signature(); let input_dtypes: Vec = match &signature.type_signature { @@ -513,7 +513,7 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py /// Inverse of [`encode_python_scalar_udf`]. fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let tuple = cloudpickle .call_method1("loads", (PyBytes::new(py, payload),))? @@ -589,6 +589,33 @@ fn volatility_wire_str(v: Volatility) -> &'static str { } } +/// Cached handle to the `cloudpickle` module. +/// +/// Six encode/decode helpers below would otherwise re-resolve the +/// module on every call. `py.import` is backed by `sys.modules` and +/// therefore cheap, but each call still walks a dict and re-binds the +/// result; a plan with many Python UDFs pays that cost per UDF. The +/// `OnceLock` collapses it to a single import per process while the +/// `Py` lets us hand out a fresh `Bound` against the current +/// GIL token without holding one in the static slot. +fn cloudpickle<'py>(py: Python<'py>) -> PyResult> { + static CLOUDPICKLE: OnceLock> = OnceLock::new(); + if let Some(cached) = CLOUDPICKLE.get() { + return Ok(cached.bind(py).clone()); + } + // Race: two threads can both miss and import. CPython's + // `sys.modules` makes the second import essentially free, and + // `set` losing the race still leaves the winning value in the + // slot — both threads end up returning the same module. + let module = py.import("cloudpickle")?; + let _ = CLOUDPICKLE.set(module.clone().unbind().into_any()); + Ok(CLOUDPICKLE + .get() + .expect("cloudpickle slot populated above") + .bind(py) + .clone()) +} + // ============================================================================= // Shared Python window UDF encode / decode helpers // @@ -629,7 +656,7 @@ pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result, udf: &PythonFunctionWindowUDF) -> PyResult> { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let signature = WindowUDFImpl::signature(udf); let input_dtypes: Vec = match &signature.type_signature { @@ -671,7 +698,7 @@ fn encode_python_window_udf(py: Python<'_>, udf: &PythonFunctionWindowUDF) -> Py } fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let tuple = cloudpickle .call_method1("loads", (PyBytes::new(py, payload),))? @@ -757,7 +784,7 @@ pub(crate) fn try_decode_python_agg_udf(buf: &[u8]) -> Result, udf: &PythonFunctionAggregateUDF) -> PyResult> { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let signature = AggregateUDFImpl::signature(udf); let input_dtypes: Vec = match &signature.type_signature { @@ -807,7 +834,7 @@ fn encode_python_agg_udf(py: Python<'_>, udf: &PythonFunctionAggregateUDF) -> Py } fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = py.import("cloudpickle")?; + let cloudpickle = cloudpickle(py)?; let tuple = cloudpickle .call_method1("loads", (PyBytes::new(py, payload),))? From 8af9c539408159934a35ab124c5f5bb851a2dd02 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:42:20 -0400 Subject: [PATCH 26/57] refactor(udf): scalar from_parts now takes Vec, matching udaf/udwf MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `PythonFunctionScalarUDF::from_parts` previously took `Vec` for inputs only to call `.data_type()` on every entry inside `new` — nullability and metadata went straight in the bin because `Signature::exact` cannot represent them. The codec carried full Field info through IPC but threw it away one frame later. Take `Vec` instead. The codec extracts data types from the decoded IPC schema and feeds them in directly. `return_field` is still a `Field` so per-output nullability/metadata round-trips intact, the same way `state_fields` is preserved on the aggregate side. The Python constructor path (`PyScalarUDF::new`) still goes through `new` with `Vec` since PyArrow already hands it the full Field list. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 6 +++--- crates/core/src/udf.rs | 18 ++++++++++++------ 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 2e03b2a83..6d3e99455 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -527,10 +527,10 @@ fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult = input_schema + let input_types: Vec = input_schema .fields() .iter() - .map(|f| f.as_ref().clone()) + .map(|f| f.data_type().clone()) .collect(); let return_schema = schema_from_ipc_bytes(&return_schema_bytes) @@ -552,7 +552,7 @@ fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult`. + /// by the codec. Inputs collapse to `Vec` because + /// `Signature::exact` cannot carry per-input nullability or + /// metadata — the encoder is free to discard that side of the + /// schema. `return_field` is kept as a `Field` so the post-decode + /// nullability and metadata match the sender's instance. pub(crate) fn from_parts( name: String, func: Py, - input_fields: Vec, + input_types: Vec, return_field: Field, volatility: Volatility, ) -> Self { - Self::new(name, func, input_fields, return_field, volatility) + Self { + name, + func, + signature: Signature::exact(input_types, volatility), + return_field: Arc::new(return_field), + } } } From ddd76f8a32d3dfe3012f8e75cfd4cdca173b527d Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:44:43 -0400 Subject: [PATCH 27/57] docs(pyproject): cloudpickle covers aggregate and window UDFs too The dependency comment said "scalar UDF callables" but cloudpickle has covered aggregate and window UDFs since 4b51402 / 89d119f. Also note the OnceLock import cache landed in b5684bc so callers pay one import per process, not one per UDF. Co-Authored-By: Claude Opus 4.7 (1M context) --- pyproject.toml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7df7e4760..f7d106836 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -45,9 +45,10 @@ classifiers = [ ] dependencies = [ # cloudpickle is invoked by the Rust-side PythonLogicalCodec / - # PythonPhysicalCodec via pyo3 to serialize Python scalar UDF - # callables into the proto wire format. Lazy-imported on the encode - # / decode hot paths, so users who never serialize a plan or + # PythonPhysicalCodec via pyo3 to serialize Python UDF callables — + # scalar, aggregate, and window — into the proto wire format. + # Lazy-imported on the encode / decode hot paths (and cached after + # the first import), so users who never serialize a plan or # expression incur no runtime cost beyond the install footprint. "cloudpickle>=2.0", "pyarrow>=16.0.0;python_version<'3.14'", From ca2f0ff6f929aca2c0b9a4a187c3243f18682698 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:50:03 -0400 Subject: [PATCH 28/57] refactor(codec): drop redundant buf.is_empty() guards `<[u8]>::starts_with` on an empty buffer with a non-empty prefix already returns false, so the `buf.is_empty() || !buf.starts_with(MAGIC)` guard in each `try_decode_python_*_udf` function reduces to just `!buf.starts_with(MAGIC)`. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 6d3e99455..7190b1b45 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -446,7 +446,7 @@ pub(crate) fn try_encode_python_scalar_udf(node: &ScalarUDF, buf: &mut Vec) /// caller to delegate to its `inner` codec (and eventually the /// `FunctionRegistry`). pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result>> { - if buf.is_empty() || !buf.starts_with(PY_SCALAR_UDF_MAGIC) { + if !buf.starts_with(PY_SCALAR_UDF_MAGIC) { return Ok(None); } let payload = &buf[PY_SCALAR_UDF_MAGIC.len()..]; @@ -643,7 +643,7 @@ pub(crate) fn try_encode_python_window_udf(node: &WindowUDF, buf: &mut Vec) } pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result>> { - if buf.is_empty() || !buf.starts_with(PY_WINDOW_UDF_MAGIC) { + if !buf.starts_with(PY_WINDOW_UDF_MAGIC) { return Ok(None); } let payload = &buf[PY_WINDOW_UDF_MAGIC.len()..]; @@ -771,7 +771,7 @@ pub(crate) fn try_encode_python_agg_udf(node: &AggregateUDF, buf: &mut Vec) } pub(crate) fn try_decode_python_agg_udf(buf: &[u8]) -> Result>> { - if buf.is_empty() || !buf.starts_with(PY_AGG_UDF_MAGIC) { + if !buf.starts_with(PY_AGG_UDF_MAGIC) { return Ok(None); } let payload = &buf[PY_AGG_UDF_MAGIC.len()..]; From 8a22df11a38a147270bb94d15db3f4f06e866c79 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:51:09 -0400 Subject: [PATCH 29/57] refactor(expr): drop redundant bytes(...) wrap in to_bytes `PyExpr::to_bytes` returns `Bound`, which crosses the FFI boundary as Python `bytes`. The defensive `bytes(...)` wrap was a no-op. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/datafusion/expr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index 7eb1cd0df..a6dc016c9 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -446,7 +446,7 @@ def to_bytes(self, ctx: SessionContext | None = None) -> bytes: :doc:`/user-guide/io/distributing_work`. """ ctx_arg = ctx.ctx if ctx is not None else None - return bytes(self.expr.to_bytes(ctx_arg)) + return self.expr.to_bytes(ctx_arg) @classmethod def from_bytes(cls, buf: bytes, ctx: SessionContext | None = None) -> Expr: From d578c85e706f911adf9b7e914d40975e18ffa065 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:52:24 -0400 Subject: [PATCH 30/57] refactor(tests): drop redundant inline Expr reimports Four test methods re-imported `Expr` from `datafusion` despite the module top already importing it on line 35. Removed. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_expr.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 853c77e1e..066e605fa 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -182,8 +182,6 @@ def test_agg_udf_decodes_into_fresh_ctx(self): e = u(col("a")) blob = e.to_bytes() fresh = SessionContext() - from datafusion import Expr - decoded = Expr.from_bytes(blob, ctx=fresh) assert "count_all" in decoded.canonical_name() @@ -237,8 +235,6 @@ def test_window_udf_decodes_into_fresh_ctx(self): e = u(col("a")) blob = e.to_bytes() fresh = SessionContext() - from datafusion import Expr - decoded = Expr.from_bytes(blob, ctx=fresh) assert "count_up" in decoded.canonical_name() @@ -280,8 +276,6 @@ def test_strict_roundtrip_via_registry(self): """When both sender and receiver disable inlining, the UDF travels by name only and the receiver resolves it from its registered functions.""" - from datafusion import Expr - strict_sender = SessionContext().with_python_udf_inlining(False) u = self._build_double_udf() blob = u(col("a")).to_bytes(strict_sender) @@ -294,8 +288,6 @@ def test_strict_roundtrip_via_registry(self): def test_strict_decoder_refuses_inline_payload(self): """An inline-encoded blob fed to a strict receiver raises with a clear error rather than silently invoking cloudpickle.loads.""" - from datafusion import Expr - sender = SessionContext() u = self._build_double_udf() blob = u(col("a")).to_bytes(sender) From 5ae44f2ebd791c00776ac5a6bdb77f370822a0aa Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 10:53:14 -0400 Subject: [PATCH 31/57] docs(codec): justify synthetic input field names and nullability MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Encoders for scalar, aggregate, and window UDFs build IPC input schemas from `Field::new(format!("arg_{i}"), dt, true)` — synthetic names, unconditional nullability. Add a comment at each site explaining that the field wrapper is only a transport for the `DataType`: the receiver immediately collapses these fields back to `Vec` when reconstructing `Signature::Exact`, which cannot encode names or nullability. Setting realistic values here would be discarded on decode. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 7190b1b45..8a6da89be 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -481,6 +481,13 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py ))); } }; + // Input fields exist only as a transport for the per-arg + // `DataType`. Names default to `arg_{i}` and nullability to + // `true` because the underlying `TypeSignature::Exact` cannot + // express either — the receiver immediately collapses these + // fields back to `Vec` when reconstructing the + // `Signature`, so any nullability or metadata set here would be + // discarded. let input_fields: Vec = input_dtypes .into_iter() .enumerate() @@ -667,6 +674,13 @@ fn encode_python_window_udf(py: Python<'_>, udf: &PythonFunctionWindowUDF) -> Py ))); } }; + // Input fields exist only as a transport for the per-arg + // `DataType`. Names default to `arg_{i}` and nullability to + // `true` because the underlying `TypeSignature::Exact` cannot + // express either — the receiver immediately collapses these + // fields back to `Vec` when reconstructing the + // `Signature`, so any nullability or metadata set here would be + // discarded. let input_fields: Vec = input_dtypes .into_iter() .enumerate() @@ -795,6 +809,13 @@ fn encode_python_agg_udf(py: Python<'_>, udf: &PythonFunctionAggregateUDF) -> Py ))); } }; + // Input fields exist only as a transport for the per-arg + // `DataType`. Names default to `arg_{i}` and nullability to + // `true` because the underlying `TypeSignature::Exact` cannot + // express either — the receiver immediately collapses these + // fields back to `Vec` when reconstructing the + // `Signature`, so any nullability or metadata set here would be + // discarded. let input_fields: Vec = input_dtypes .into_iter() .enumerate() From 8a321d65ef401ebbb26321bf2bf7fe8b01a6daca Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:24:57 -0400 Subject: [PATCH 32/57] fix(codec): use Execution error for inline-payload refusal `refuse_inline_payload` was returning `DataFusionError::Plan`, but the refusal happens at wire-format decode time, not during planning. Downstream error classification keys off the variant; surfacing this as a planner error mis-routes it into "fix your SQL" buckets when the real problem is that the receiver opted out of inline Python UDF decoding. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 8a6da89be..374b95411 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -272,7 +272,11 @@ impl LogicalExtensionCodec for PythonLogicalCodec { /// Build the error returned by a strict codec when it receives an /// inline Python-UDF payload it has been told not to deserialize. fn refuse_inline_payload(kind: &str, name: &str) -> datafusion::error::DataFusionError { - datafusion::error::DataFusionError::Plan(format!( + // `Execution`, not `Plan`: this is a wire-format decode refusal at + // codec time, not a planner-stage failure. Downstream error + // classification keys off the variant — surfacing this as a planner + // error would mis-route it into "fix your SQL" buckets. + datafusion::error::DataFusionError::Execution(format!( "Refusing to deserialize inline Python {kind} '{name}': Python UDF \ inlining is disabled on this session. Re-encode the bytes with \ inlining enabled, or register '{name}' on the sender's session \ From 508f8f603ef8f6e34ec0d0684b0d09c53d03098d Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:25:14 -0400 Subject: [PATCH 33/57] refactor(codec): cache cloudpickle via per-interpreter PyOnceLock MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The cloudpickle module handle was cached in `OnceLock>`, a process-lifetime slot. The cached `Py` was rooted in the interpreter that won the first-import race; under CPython subinterpreters (PEP 684), a teardown of that interpreter would have left the slot pointing at a dangling reference for the next call. Swap to `pyo3::sync::PyOnceLock`, which scopes the cached `Py` to the current interpreter and drops it cleanly on interpreter teardown. The body collapses to a single `get_or_try_init` call — no more manual race-handling between `get` and `set`. Same call sites, same cost profile, no downstream churn. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 32 ++++++++++++-------------------- 1 file changed, 12 insertions(+), 20 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 374b95411..c5ee9a044 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -75,7 +75,7 @@ //! `inner`; the encoder/decoder hooks for each kind are added as the //! corresponding Python-side type becomes serializable. -use std::sync::{Arc, OnceLock}; +use std::sync::Arc; use arrow::datatypes::{Field, Schema, SchemaRef}; use arrow::ipc::reader::StreamReader; @@ -93,6 +93,7 @@ use datafusion::physical_plan::ExecutionPlan; use datafusion_proto::logical_plan::{DefaultLogicalExtensionCodec, LogicalExtensionCodec}; use datafusion_proto::physical_plan::{DefaultPhysicalExtensionCodec, PhysicalExtensionCodec}; use pyo3::prelude::*; +use pyo3::sync::PyOnceLock; use pyo3::types::{PyBytes, PyTuple}; use crate::udaf::PythonFunctionAggregateUDF; @@ -605,26 +606,17 @@ fn volatility_wire_str(v: Volatility) -> &'static str { /// Six encode/decode helpers below would otherwise re-resolve the /// module on every call. `py.import` is backed by `sys.modules` and /// therefore cheap, but each call still walks a dict and re-binds the -/// result; a plan with many Python UDFs pays that cost per UDF. The -/// `OnceLock` collapses it to a single import per process while the -/// `Py` lets us hand out a fresh `Bound` against the current -/// GIL token without holding one in the static slot. +/// result; a plan with many Python UDFs pays that cost per UDF. +/// +/// `PyOnceLock` scopes the cached `Py` to the current +/// interpreter, so the slot drops cleanly on interpreter teardown +/// (relevant under CPython subinterpreters, PEP 684) instead of +/// resurrecting a `Py` rooted in a dead interpreter on the next call. fn cloudpickle<'py>(py: Python<'py>) -> PyResult> { - static CLOUDPICKLE: OnceLock> = OnceLock::new(); - if let Some(cached) = CLOUDPICKLE.get() { - return Ok(cached.bind(py).clone()); - } - // Race: two threads can both miss and import. CPython's - // `sys.modules` makes the second import essentially free, and - // `set` losing the race still leaves the winning value in the - // slot — both threads end up returning the same module. - let module = py.import("cloudpickle")?; - let _ = CLOUDPICKLE.set(module.clone().unbind().into_any()); - Ok(CLOUDPICKLE - .get() - .expect("cloudpickle slot populated above") - .bind(py) - .clone()) + static CLOUDPICKLE: PyOnceLock> = PyOnceLock::new(); + CLOUDPICKLE + .get_or_try_init(py, || Ok(py.import("cloudpickle")?.unbind().into_any())) + .map(|cached| cached.bind(py).clone()) } // ============================================================================= From 576243537b58d621a7dc91a4b3b7aac15a39f788 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:25:24 -0400 Subject: [PATCH 34/57] test(pickle): probe multiprocessing lazily via cached fixture MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `_multiprocessing_available()` was called at module import time, which meant every pytest collection — including unrelated runs that never execute this module — paid the cost of spawning a probe Pool. In sandboxed environments that deny semaphore creation, this also surfaced an irrelevant traceback during collection. Wrap the probe in `functools.cache` and call it from an autouse fixture that emits `pytest.skip` when the Pool can't start. The probe now runs at most once per session, and only when a test in this module is about to execute; `--collect-only` no longer triggers it. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_multiprocessing.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/python/tests/test_pickle_multiprocessing.py b/python/tests/test_pickle_multiprocessing.py index 043634c25..6eabaff9e 100644 --- a/python/tests/test_pickle_multiprocessing.py +++ b/python/tests/test_pickle_multiprocessing.py @@ -27,6 +27,7 @@ from __future__ import annotations +import functools import multiprocessing as mp import pickle import sys @@ -37,24 +38,29 @@ from . import _pickle_multiprocessing_helpers as helpers +@functools.cache def _multiprocessing_available() -> tuple[bool, str]: """Return (available, reason). Some sandboxed environments deny semaphore creation; without semaphores, ``multiprocessing.Pool`` cannot start. + + Cached so the probe Pool only spawns once per session, and only when a + test in this module is actually about to run — collection-only runs + (e.g. ``pytest --collect-only`` on the full suite) skip the probe. """ try: ctx = mp.get_context("spawn") with ctx.Pool(processes=1) as pool: pool.map(int, [0]) - except PermissionError as exc: - return False, f"multiprocessing.Pool unavailable: {exc}" - except OSError as exc: + except (PermissionError, OSError) as exc: return False, f"multiprocessing.Pool unavailable: {exc}" return True, "" -_MP_AVAILABLE, _MP_SKIP_REASON = _multiprocessing_available() - -pytestmark = pytest.mark.skipif(not _MP_AVAILABLE, reason=_MP_SKIP_REASON) +@pytest.fixture(autouse=True) +def _skip_if_multiprocessing_unavailable(): + available, reason = _multiprocessing_available() + if not available: + pytest.skip(reason) START_METHODS = [ From 54bbdf899e7a8145c27c3ee0ecd2fb54ff3c2889 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:30:18 -0400 Subject: [PATCH 35/57] refactor(udwf): drop redundant PythonFunctionWindowUDF::from_parts `from_parts` was a verbatim wrapper over `new` with an identical signature, kept only for parity with the scalar / aggregate variants where `from_parts` accepts richer state (return `Field`, state `Vec`) than `new`. Window UDFs have no such asymmetry, so the indirection added no value. Call `PythonFunctionWindowUDF::new` from the codec directly. Same behavior, one fewer hop. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 2 +- crates/core/src/udwf.rs | 10 ---------- 2 files changed, 1 insertion(+), 11 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index c5ee9a044..e84a9093c 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -744,7 +744,7 @@ fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult &DataType { &self.return_type } - - pub(crate) fn from_parts( - name: String, - evaluator: Py, - input_types: Vec, - return_type: DataType, - volatility: Volatility, - ) -> Self { - Self::new(name, evaluator, input_types, return_type, volatility) - } } impl Eq for PythonFunctionWindowUDF {} From cea92541c5cdd7577c7fed15831935b176058d16 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:30:34 -0400 Subject: [PATCH 36/57] refactor(udaf): name synthesized state fields state_{i} MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The Python constructor path collapses input `Vec` into synthesized `FieldRef`s; bare integer names (`"0"`, `"1"`) made EXPLAIN and debug output for the state schema opaque. Use `state_{i}` to match the `arg_{i}` convention the codec applies on the input-field side. The post-decode path is unaffected — the codec ships full `FieldRef`s (with the names the sender chose) through IPC and bypasses this synthesis entirely. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/udaf.rs | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index 012583928..311e21a88 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -180,7 +180,7 @@ impl PythonFunctionAggregateUDF { let state_fields = state_types .into_iter() .enumerate() - .map(|(i, t)| Arc::new(Field::new(format!("{i}"), t, true))) + .map(|(i, t)| Arc::new(Field::new(format!("state_{i}"), t, true))) .collect(); Self { name, From 2f241be46244aed059da4486d23337147f3ef8a6 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:31:06 -0400 Subject: [PATCH 37/57] perf(udf): pointer-identity fast path in PartialEq for Python UDFs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `PartialEq` was acquiring the GIL on every comparison via `Python::attach`, just to call `__eq__` on the stored callable. Most equality checks compare `Arc`-shared clones of the same UDF (expression rewriting, plan diffing), where pointer identity already settles the question — no Python call needed. Add `Py::as_ptr` pointer comparison before the `Python::attach` branch on all three impls (scalar / aggregate / window). Falls through to the GIL-bound `__eq__` only when the two callables are distinct objects. No behavior change for unequal-pointer cases; the equal-pointer case now skips the GIL roundtrip entirely. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/udaf.rs | 16 ++++++++++------ crates/core/src/udf.rs | 9 ++++++++- crates/core/src/udwf.rs | 16 ++++++++++------ 3 files changed, 28 insertions(+), 13 deletions(-) diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index 311e21a88..55c3829bf 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -238,12 +238,16 @@ impl PartialEq for PythonFunctionAggregateUDF { && self.signature == other.signature && self.return_type == other.return_type && self.state_fields == other.state_fields - && Python::attach(|py| { - self.accumulator - .bind(py) - .eq(other.accumulator.bind(py)) - .unwrap_or(false) - }) + // Pointer-identity fast path: `Arc`-shared clones of the + // same UDF skip the GIL roundtrip. Falls through to Python + // `__eq__` only for two distinct callables. + && (self.accumulator.as_ptr() == other.accumulator.as_ptr() + || Python::attach(|py| { + self.accumulator + .bind(py) + .eq(other.accumulator.bind(py)) + .unwrap_or(false) + })) } } diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index b1d664b1e..b5ea8f55f 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -106,7 +106,14 @@ impl PartialEq for PythonFunctionScalarUDF { self.name == other.name && self.signature == other.signature && self.return_field == other.return_field - && Python::attach(|py| self.func.bind(py).eq(other.func.bind(py)).unwrap_or(false)) + // Identical pointers ⇒ same Python object. Most equality + // checks compare `Arc`-shared clones of the same UDF + // (e.g. expression rewriting), so the pointer match short- + // circuits before touching the GIL. + && (self.func.as_ptr() == other.func.as_ptr() + || Python::attach(|py| { + self.func.bind(py).eq(other.func.bind(py)).unwrap_or(false) + })) } } diff --git a/crates/core/src/udwf.rs b/crates/core/src/udwf.rs index a1d356853..e6dfb8dc9 100644 --- a/crates/core/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -319,12 +319,16 @@ impl PartialEq for PythonFunctionWindowUDF { self.name == other.name && self.signature == other.signature && self.return_type == other.return_type - && Python::attach(|py| { - self.evaluator - .bind(py) - .eq(other.evaluator.bind(py)) - .unwrap_or(false) - }) + // Pointer-identity fast path: `Arc`-shared clones of the + // same UDF skip the GIL roundtrip. Falls through to Python + // `__eq__` only for two distinct callables. + && (self.evaluator.as_ptr() == other.evaluator.as_ptr() + || Python::attach(|py| { + self.evaluator + .bind(py) + .eq(other.evaluator.bind(py)) + .unwrap_or(false) + })) } } From 4a8fadc09d9ae8336db5d0f86d662a94330f4fc2 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:31:14 -0400 Subject: [PATCH 38/57] =?UTF-8?q?docs(example):=20note=20null=E2=86=920=20?= =?UTF-8?q?coercion=20in=20above-threshold=20UDF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The `(v.as_py() or 0) > threshold` line was reading as canonical Python UDF style. Add a one-line comment flagging that nulls silently coerce to 0 — fine for the demo dataset (no nulls), but real code should pick the semantics explicitly. Co-Authored-By: Claude Opus 4.7 (1M context) --- examples/multiprocessing_pickle_expr.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/multiprocessing_pickle_expr.py b/examples/multiprocessing_pickle_expr.py index 3832f96de..68ef55b5c 100644 --- a/examples/multiprocessing_pickle_expr.py +++ b/examples/multiprocessing_pickle_expr.py @@ -64,6 +64,9 @@ def make_above_threshold_udf(threshold: int) -> ScalarUDF: """ def above(arr: pa.Array) -> pa.Array: + # `v.as_py() or 0` coerces nulls to 0 — the demo dataset has no + # nulls, but real-world code should decide explicitly how nulls + # compare against the threshold. return pa.array([1 if (v.as_py() or 0) > threshold else 0 for v in arr]) return udf( From 9a265e462c477088991b686f437bc135a6a59a5e Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:31:17 -0400 Subject: [PATCH 39/57] test(pickle): tighten refusal assertion from Exception to RuntimeError MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `pytest.raises(Exception, ...)` matched too loosely — a regression that swallowed the codec refusal as some other error type would have passed the test. The actual surface error is `RuntimeError` (the `parse_expr` → `PyRuntimeError` conversion in `expr.rs::from_bytes`). Switch to the concrete type and add a brief comment explaining why. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_expr.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 066e605fa..281108605 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -294,7 +294,11 @@ def test_strict_decoder_refuses_inline_payload(self): strict_receiver = SessionContext().with_python_udf_inlining(False) strict_receiver.register_udf(u) - with pytest.raises(Exception, match="inlining is disabled"): + # `RuntimeError` (not bare `Exception`): the codec refusal is + # surfaced through `parse_expr` → `PyRuntimeError`. Tightening + # the assertion catches a regression that swallows the refusal + # as a different error type. + with pytest.raises(RuntimeError, match="inlining is disabled"): Expr.from_bytes(blob, ctx=strict_receiver) def test_sender_ctx_propagates_through_pickle(self): From 06421600ba6649df9095dd3393a83ec3e97d5d98 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:33:26 -0400 Subject: [PATCH 40/57] docs(codec): clarify strict-mode security scope MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The doc on `with_python_udf_inlining(false)` said strict mode "rejects cloudpickle.loads on untrusted from_bytes input", which could be misread as making `pickle.loads(untrusted)` safe. It does not. Strict mode only narrows the codec layer: it stops `Expr::from_bytes` from invoking `cloudpickle.loads` on the inline `DFPY*` payload. The outer pickle stream is still arbitrary code — `pickle.loads` honors any `__reduce__` the bytes name, and an attacker is free to choose one. Spell that out in the doc so callers don't treat the toggle as a substitute for "never pickle.loads untrusted input." The Python-side docstring (`SessionContext.with_python_udf_inlining`) already carries the equivalent caveat; this brings the Rust side in line. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index e84a9093c..6382e80e3 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -149,6 +149,18 @@ impl PythonLogicalCodec { /// when the codec sits on a session that must produce /// cross-language wire bytes, or reject `cloudpickle.loads` on /// untrusted `from_bytes` input. + /// + /// Security scope: strict mode (`false`) protects only the codec + /// layer — it stops `Expr::from_bytes` from invoking + /// `cloudpickle.loads` on the inline `DFPY*` payload. It does + /// **not** make `pickle.loads(untrusted_bytes)` safe. Python's + /// pickle protocol permits arbitrary code execution via + /// `__reduce__` (and `Expr.__reduce__` returns + /// `Expr._reconstruct(bytes)` — an honest reducer here, but the + /// outer pickle stream can contain any reducer). Treat every + /// `pickle.loads` on untrusted input as unsafe regardless of this + /// setting; the toggle only narrows the surface inside + /// `from_bytes`. pub fn with_python_udf_inlining(mut self, enabled: bool) -> Self { self.python_udf_inlining = enabled; self From 9d7848571dfa081e7e67acf67dc8bb2c87dd7747 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:40:32 -0400 Subject: [PATCH 41/57] build(uv): regenerate uv.lock with current uv MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Prior regeneration used an older uv that stripped revision = 2 and every upload-time field — 800+ lines of noise unrelated to the feature. Lockfile is now back to a minimal +12/-1 delta covering only the cloudpickle dependency addition. 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outcome and would mask a real refusal regression in code review. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_expr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 281108605..993a93f26 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -350,7 +350,7 @@ def test_sender_ctx_strict_roundtrip_via_pickle(self): assert "double" in decoded.canonical_name() - def test_sender_ctx_strict_pickle_refused_by_inline_worker(self): + def test_sender_ctx_strict_pickle_accepted_by_inline_worker_with_registry(self): """A strict-encoded blob still decodes fine on an inline worker because the wire format is the same default-codec by-name form. Sanity check: cross-config works as long as the receiver can From 310080c06c8d354a04c1ef3459adcf9169e71a71 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:46:21 -0400 Subject: [PATCH 43/57] docs(distributing): note fork-worker threading.local() inheritance Forked workers inherit the parent's `threading.local()` slots via copy-on-write until first write, so a child can silently observe a sender or worker context the parent set. Spell that out next to the existing thread-local caveat and tell readers to (re)initialize the slot in the worker rather than rely on inherited state. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/source/user-guide/io/distributing_work.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index d88457b4d..40b5dd965 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -188,6 +188,14 @@ Sharp edges: * Sender and worker slots are **thread-local**. Background threads on either side see ``None`` until they install their own. +* Under the ``fork`` start method, the parent's ``threading.local()`` + values are copied into the child by copy-on-write — a forked + worker initially observes whatever sender / worker slot the parent + had set, until the worker writes its own value (or calls the + matching ``clear_*_ctx``). ``spawn`` and ``forkserver`` workers + start with empty thread-local slots. Treat the slot as + uninitialized on worker entry and install (or clear) it explicitly + in the worker initializer; do not rely on inherited state. * The global slot persists across ``fork`` workers (copy-on-write memory inherit) but not across ``spawn`` / ``forkserver`` workers (fresh process — register or install a worker context on From f25aa879f1e50af095b94cc846ed46d2781c4065 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:46:29 -0400 Subject: [PATCH 44/57] test(ffi): strict-mode Expr round-trip with an FFI-capsule scalar UDF MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The strict-mode wire format ships Python-defined UDFs by name; the existing coverage only exercised that path with `PythonFunctionScalarUDF` instances. This test uses the FFI `IsNullUDF` so the codec falls through `try_encode_python_scalar_udf` (downcast fails) and the default by-name encoding actually runs. Covers explicit `Expr.to_bytes` / `from_bytes` plus the `pickle.dumps` / `pickle.loads` route via the sender / worker thread-local slots, and asserts the strict blob is materially smaller than an inline Python-UDF blob — catching a regression that silently falls back to inlining for a non-Python UDF. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../python/tests/_test_pickle_strict_ffi.py | 127 ++++++++++++++++++ 1 file changed, 127 insertions(+) create mode 100644 examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py diff --git a/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py b/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py new file mode 100644 index 000000000..ac5e972e2 --- /dev/null +++ b/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py @@ -0,0 +1,127 @@ +# 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. + +"""Strict-mode Expr round-trip with an FFI-capsule scalar UDF. + +Verifies the by-name path: an FFI-imported UDF (no +``PythonFunctionScalarUDF`` downcast on the codec) serializes by name +and resolves from the receiver's function registry on decode. Covers +both the explicit ``Expr.to_bytes(ctx)`` / ``Expr.from_bytes(ctx=...)`` +API and the ``pickle.dumps`` / ``pickle.loads`` route through the +sender / worker context slots. +""" + +from __future__ import annotations + +import pickle + +import pyarrow as pa +import pytest +from datafusion import Expr, SessionContext, col, udf +from datafusion.ipc import ( + clear_sender_ctx, + clear_worker_ctx, + set_sender_ctx, + set_worker_ctx, +) +from datafusion_ffi_example import IsNullUDF + + +@pytest.fixture(autouse=True) +def _reset_thread_locals(): + """Ensure no sender / worker context leaks across tests.""" + clear_worker_ctx() + clear_sender_ctx() + yield + clear_worker_ctx() + clear_sender_ctx() + + +def _strict_session_with_ffi_udf(): + """Build a strict-mode session with the FFI ``IsNullUDF`` registered.""" + ctx = SessionContext().with_python_udf_inlining(enabled=False) + my_udf = udf(IsNullUDF()) + ctx.register_udf(my_udf) + return ctx, my_udf + + +def test_strict_ffi_udf_expr_roundtrip_via_to_bytes(): + """Strict-mode encode emits a by-name payload; receiver resolves + ``my_custom_is_null`` from its registered functions and the decoded + expression evaluates to the same result as the original.""" + sender, my_udf = _strict_session_with_ffi_udf() + receiver, _ = _strict_session_with_ffi_udf() + + expr = my_udf(col("a")) + blob = expr.to_bytes(sender) + restored = Expr.from_bytes(blob, ctx=receiver) + + assert "my_custom_is_null" in restored.canonical_name() + + batch = pa.RecordBatch.from_arrays( + [pa.array([1, 2, None, 4], type=pa.int64())], names=["a"] + ) + receiver.register_record_batches("t", [[batch]]) + out = receiver.table("t").select(restored.alias("r")).collect() + expected = pa.array([False, False, True, False], type=pa.bool_()) + assert out[0].column(0) == expected + + +def test_strict_ffi_udf_pickle_roundtrip_via_thread_locals(): + """Driver installs a strict sender context; worker installs a + matching strict receiver. ``pickle.dumps`` / ``pickle.loads`` route + through them and the FFI UDF resolves by name on decode.""" + sender, my_udf = _strict_session_with_ffi_udf() + receiver, _ = _strict_session_with_ffi_udf() + + expr = my_udf(col("a")) + + set_sender_ctx(sender) + try: + blob = pickle.dumps(expr) + finally: + clear_sender_ctx() + + set_worker_ctx(receiver) + try: + restored = pickle.loads(blob) + finally: + clear_worker_ctx() + + assert "my_custom_is_null" in restored.canonical_name() + + +def test_strict_ffi_udf_smaller_than_inline_python_udf(): + """Sanity-check the wire size claim: strict-mode FFI UDF bytes are + a small by-name payload, dramatically smaller than the inline form + of a Python UDF with the same arity. Confirms the encode path + actually took the by-name branch instead of falling through to an + inline path.""" + sender, my_udf = _strict_session_with_ffi_udf() + ffi_blob = my_udf(col("a")).to_bytes(sender) + + inline_ctx = SessionContext() + py_udf = udf( + lambda arr: pa.array([v.as_py() is None for v in arr]), + [pa.int64()], + pa.bool_(), + volatility="immutable", + name="py_is_null", + ) + py_blob = py_udf(col("a")).to_bytes(inline_ctx) + + assert len(ffi_blob) < len(py_blob) // 4 From 69b0ddb6070a9d996fdbfc2cb7e2e1b535b1aff1 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:55:33 -0400 Subject: [PATCH 45/57] fix(udf): name property reports FFI capsule name, not the ignored ctor arg MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Previously `ScalarUDF.name`, `AggregateUDF.name`, and `WindowUDF.name` cached the `name` argument passed to `__init__`. The FFI-capsule branch discards that argument and constructs from the capsule directly, so the cached value was wrong — e.g. `udf(IsNullUDF()).name` returned `""` (the factory-synthesized string from `from_pycapsule`) rather than the capsule's own `my_custom_is_null`. Expose a `name` getter on `PyScalarUDF`, `PyAggregateUDF`, `PyWindowUDF` that forwards to the underlying `UDF::name()`, and have each Python property delegate to it. Same path for Python-defined and FFI-imported UDFs, no stale cache. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/udaf.rs | 5 +++++ crates/core/src/udf.rs | 5 +++++ crates/core/src/udwf.rs | 5 +++++ python/datafusion/user_defined.py | 30 +++++++++++++++++++++--------- 4 files changed, 36 insertions(+), 9 deletions(-) diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index 55c3829bf..d77793506 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -368,4 +368,9 @@ impl PyAggregateUDF { fn __repr__(&self) -> PyResult { Ok(format!("AggregateUDF({})", self.function.name())) } + + #[getter] + fn name(&self) -> &str { + self.function.name() + } } diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index b5ea8f55f..0fdb16e44 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -261,4 +261,9 @@ impl PyScalarUDF { fn __repr__(&self) -> PyResult { Ok(format!("ScalarUDF({})", self.function.name())) } + + #[getter] + fn name(&self) -> &str { + self.function.name() + } } diff --git a/crates/core/src/udwf.rs b/crates/core/src/udwf.rs index e6dfb8dc9..ed6932fe8 100644 --- a/crates/core/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -273,6 +273,11 @@ impl PyWindowUDF { fn __repr__(&self) -> PyResult { Ok(format!("WindowUDF({})", self.function.name())) } + + #[getter] + fn name(&self) -> &str { + self.function.name() + } } #[derive(Debug)] diff --git a/python/datafusion/user_defined.py b/python/datafusion/user_defined.py index c18a14b2e..da756473a 100644 --- a/python/datafusion/user_defined.py +++ b/python/datafusion/user_defined.py @@ -132,7 +132,6 @@ def __init__( See helper method :py:func:`udf` for argument details. """ - self._name = name if hasattr(func, "__datafusion_scalar_udf__"): self._udf = df_internal.ScalarUDF.from_pycapsule(func) return @@ -144,8 +143,13 @@ def __init__( @property def name(self) -> str: - """Return the registered name of this UDF.""" - return self._name + """Return the registered name of this UDF. + + For UDFs imported via the FFI capsule protocol, this is the + name the capsule itself reports — not the ``name`` argument + passed to the constructor (which is ignored on the FFI path). + """ + return self._udf.name def __repr__(self) -> str: """Print a string representation of the Scalar UDF.""" @@ -400,7 +404,6 @@ def __init__( See :py:func:`udaf` for a convenience function and argument descriptions. """ - self._name = name if hasattr(accumulator, "__datafusion_aggregate_udf__"): self._udaf = df_internal.AggregateUDF.from_pycapsule(accumulator) return @@ -427,8 +430,13 @@ def __init__( @property def name(self) -> str: - """Return the registered name of this UDAF.""" - return self._name + """Return the registered name of this UDAF. + + For UDAFs imported via the FFI capsule protocol, this is the + name the capsule itself reports — not the ``name`` argument + passed to the constructor (which is ignored on the FFI path). + """ + return self._udaf.name def __repr__(self) -> str: """Print a string representation of the Aggregate UDF.""" @@ -833,7 +841,6 @@ def __init__( See :py:func:`udwf` for a convenience function and argument descriptions. """ - self._name = name if hasattr(func, "__datafusion_window_udf__"): self._udwf = df_internal.WindowUDF.from_pycapsule(func) return @@ -843,8 +850,13 @@ def __init__( @property def name(self) -> str: - """Return the registered name of this UDWF.""" - return self._name + """Return the registered name of this UDWF. + + For UDWFs imported via the FFI capsule protocol, this is the + name the capsule itself reports — not the ``name`` argument + passed to the constructor (which is ignored on the FFI path). + """ + return self._udwf.name def __repr__(self) -> str: """Print a string representation of the Window UDF.""" From e39311c8dd8b83ed814fb16acedbc54e03b9d2cd Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 12:57:18 -0400 Subject: [PATCH 46/57] refactor(ctx): make with_python_udf_inlining `enabled` keyword-only MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `ctx.with_python_udf_inlining(True)` and `ctx.with_python_udf_inlining(False)` read identically at the call site — a bare positional `True` / `False` does not convey which direction the toggle moves. Forcing `enabled=` at the call site disambiguates: `with_python_udf_inlining(enabled=False)` is unambiguously the opt-out form. The Rust constructor stays positional (internal API); only the public Python wrapper changes signature. All in-tree callers (tests, docs, FFI example test) already used or now use the keyword form. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../source/user-guide/io/distributing_work.rst | 6 +++--- python/datafusion/context.py | 8 +++++++- python/datafusion/ipc.py | 2 +- python/tests/test_pickle_expr.py | 18 +++++++++--------- 4 files changed, 20 insertions(+), 14 deletions(-) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 40b5dd965..3fa74a275 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -170,7 +170,7 @@ worker slots are unused. To opt into the strict format, :py:func:`~datafusion.ipc.set_sender_ctx` with a session built via - :py:meth:`SessionContext.with_python_udf_inlining(False) + :py:meth:`SessionContext.with_python_udf_inlining(enabled=False) `. 2. *Worker side — what registrations does decode need?* For built-ins @@ -265,7 +265,7 @@ Disabling Python UDF inlining ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ For a stricter wire format, call -:py:meth:`SessionContext.with_python_udf_inlining(False) +:py:meth:`SessionContext.with_python_udf_inlining(enabled=False) ` on the session producing or consuming the bytes. With inlining disabled, Python UDFs travel by name only — the same way FFI-capsule UDFs do — and @@ -295,7 +295,7 @@ session as the driver's *sender context*: from datafusion import SessionContext from datafusion.ipc import set_sender_ctx - set_sender_ctx(SessionContext().with_python_udf_inlining(False)) + set_sender_ctx(SessionContext().with_python_udf_inlining(enabled=False)) # Every subsequent pickle.dumps(expr) on this thread encodes # without inlining the Python callable. diff --git a/python/datafusion/context.py b/python/datafusion/context.py index 9019eebb9..d89dcbdf1 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -1770,9 +1770,15 @@ def with_physical_extension_codec(self, codec: Any) -> SessionContext: new.ctx = new_internal return new - def with_python_udf_inlining(self, enabled: bool) -> SessionContext: + def with_python_udf_inlining(self, *, enabled: bool) -> SessionContext: """Toggle inline encoding of Python-defined UDFs on this session. + ``enabled`` is keyword-only: + ``with_python_udf_inlining(enabled=False)`` reads at the call + site as the inverse of + ``with_python_udf_inlining(enabled=True)``, where a positional + ``True`` / ``False`` would not. + When ``True`` (the default), Python scalar, aggregate, and window UDFs travel inside the serialized expression and are reconstructed on the receiver without pre-registration. diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 48b92a2fd..7efd4b2ea 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -49,7 +49,7 @@ def init_worker(): from datafusion import SessionContext from datafusion.ipc import set_sender_ctx - driver_ctx = SessionContext().with_python_udf_inlining(False) + driver_ctx = SessionContext().with_python_udf_inlining(enabled=False) set_sender_ctx(driver_ctx) pickle.dumps(expr) # encoded with inlining disabled diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 993a93f26..bdf41eb3c 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -247,7 +247,7 @@ def test_window_udf_decodes_via_pickle_with_no_worker_ctx(self): class TestPythonUdfInliningToggle: - """`SessionContext.with_python_udf_inlining(False)` opts out of + """`SessionContext.with_python_udf_inlining(enabled=False)` opts out of inline Python UDF encoding for both encode and decode paths.""" def _build_double_udf(self): @@ -263,7 +263,7 @@ def test_strict_encoder_emits_smaller_blob(self): """Strict mode skips cloudpickle of the Python callable, so the encoded bytes are dramatically smaller than the inline form.""" ctx_inline = SessionContext() - ctx_strict = ctx_inline.with_python_udf_inlining(False) + ctx_strict = ctx_inline.with_python_udf_inlining(enabled=False) u = self._build_double_udf() e = u(col("a")) @@ -276,11 +276,11 @@ def test_strict_roundtrip_via_registry(self): """When both sender and receiver disable inlining, the UDF travels by name only and the receiver resolves it from its registered functions.""" - strict_sender = SessionContext().with_python_udf_inlining(False) + strict_sender = SessionContext().with_python_udf_inlining(enabled=False) u = self._build_double_udf() blob = u(col("a")).to_bytes(strict_sender) - receiver = SessionContext().with_python_udf_inlining(False) + receiver = SessionContext().with_python_udf_inlining(enabled=False) receiver.register_udf(u) restored = Expr.from_bytes(blob, ctx=receiver) assert "double" in restored.canonical_name() @@ -292,7 +292,7 @@ def test_strict_decoder_refuses_inline_payload(self): u = self._build_double_udf() blob = u(col("a")).to_bytes(sender) - strict_receiver = SessionContext().with_python_udf_inlining(False) + strict_receiver = SessionContext().with_python_udf_inlining(enabled=False) strict_receiver.register_udf(u) # `RuntimeError` (not bare `Exception`): the codec refusal is # surfaced through `parse_expr` → `PyRuntimeError`. Tightening @@ -314,7 +314,7 @@ def test_sender_ctx_propagates_through_pickle(self): blob_default = pickle.dumps(e) - strict_sender = SessionContext().with_python_udf_inlining(False) + strict_sender = SessionContext().with_python_udf_inlining(enabled=False) set_sender_ctx(strict_sender) try: blob_strict = pickle.dumps(e) @@ -333,14 +333,14 @@ def test_sender_ctx_strict_roundtrip_via_pickle(self): u = self._build_double_udf() e = u(col("a")) - strict_sender = SessionContext().with_python_udf_inlining(False) + strict_sender = SessionContext().with_python_udf_inlining(enabled=False) set_sender_ctx(strict_sender) try: blob = pickle.dumps(e) finally: clear_sender_ctx() - worker = SessionContext().with_python_udf_inlining(False) + worker = SessionContext().with_python_udf_inlining(enabled=False) worker.register_udf(u) set_worker_ctx(worker) try: @@ -358,7 +358,7 @@ def test_sender_ctx_strict_pickle_accepted_by_inline_worker_with_registry(self): u = self._build_double_udf() e = u(col("a")) - strict_sender = SessionContext().with_python_udf_inlining(False) + strict_sender = SessionContext().with_python_udf_inlining(enabled=False) set_sender_ctx(strict_sender) try: blob = pickle.dumps(e) From 9ef5d0ac3409dd619a1d9838204456ddf5439a02 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:01:41 -0400 Subject: [PATCH 47/57] refactor(codec): factor shared schema/signature helpers out of UDF encoders MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Scalar, aggregate, and window UDF encode/decode bodies in codec.rs each contained the same five blocks: * a `match TypeSignature::Exact { ... } else err` to extract input dtypes, * an `arg_{i}` input-field synthesis with a verbatim 6-line comment, * a `Schema::new(vec![Field::new(...)])` for the return type, * a `schema_from_ipc_bytes(...).first().ok_or_else(...)` decode of the single-field return blob, and * a `parse_volatility(&volatility_str).map_err(...)` round-trip. Six near-identical bodies meant the same comment text lived in three places, and each `map_err(|e| PyValueError::new_err(format!("{e}")))` chain appeared a handful of times per body. Extract: * `signature_input_dtypes(sig, kind)` — `Signature::Exact` extraction with a flavor-tagged error. * `build_input_schema_bytes(&[DataType])` — synth `arg_{i}` fields and write IPC. Carries the comment explaining why field metadata is discarded on decode. * `build_single_field_schema_bytes(&Field)` / `build_schema_bytes(Vec)` — IPC writer wrappers. * `read_input_dtypes(&[u8])` / `read_single_return_field(&[u8], kind)` — decode side. * `arrow_to_py_err(ArrowError) -> PyErr` and `parse_volatility_str` to collapse the repeated `map_err` chains. Wire format unchanged. Six encode/decode bodies collectively shrink from ~300 to ~140 LOC and stop carrying triplicated comment text that would otherwise drift independently. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 301 +++++++++++++++------------------------ 1 file changed, 116 insertions(+), 185 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 6382e80e3..08c5053db 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -77,7 +77,7 @@ use std::sync::Arc; -use arrow::datatypes::{Field, Schema, SchemaRef}; +use arrow::datatypes::{DataType, Field, Schema, SchemaRef}; use arrow::ipc::reader::StreamReader; use arrow::ipc::writer::StreamWriter; use datafusion::common::{Result, TableReference}; @@ -85,7 +85,7 @@ use datafusion::datasource::TableProvider; use datafusion::datasource::file_format::FileFormatFactory; use datafusion::execution::TaskContext; use datafusion::logical_expr::{ - AggregateUDF, AggregateUDFImpl, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, + AggregateUDF, AggregateUDFImpl, Extension, LogicalPlan, ScalarUDF, ScalarUDFImpl, Signature, TypeSignature, Volatility, WindowUDF, WindowUDFImpl, }; use datafusion::physical_expr::PhysicalExpr; @@ -478,46 +478,15 @@ pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result, udf: &PythonFunctionScalarUDF) -> PyResult> { - let cloudpickle = cloudpickle(py)?; - let signature = udf.signature(); - let input_dtypes: Vec = match &signature.type_signature { - TypeSignature::Exact(types) => types.clone(), - other => { - return Err(pyo3::exceptions::PyValueError::new_err(format!( - "PythonFunctionScalarUDF expected Signature::Exact, got {other:?}" - ))); - } - }; - // Input fields exist only as a transport for the per-arg - // `DataType`. Names default to `arg_{i}` and nullability to - // `true` because the underlying `TypeSignature::Exact` cannot - // express either — the receiver immediately collapses these - // fields back to `Vec` when reconstructing the - // `Signature`, so any nullability or metadata set here would be - // discarded. - let input_fields: Vec = input_dtypes - .into_iter() - .enumerate() - .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) - .collect(); - let input_schema = Schema::new(input_fields); - let input_schema_bytes = schema_to_ipc_bytes(&input_schema) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - - let return_schema = Schema::new(vec![udf.return_field().as_ref().clone()]); - let return_schema_bytes = schema_to_ipc_bytes(&return_schema) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - + let input_dtypes = signature_input_dtypes(signature, "PythonFunctionScalarUDF")?; + let input_schema_bytes = build_input_schema_bytes(&input_dtypes)?; + let return_schema_bytes = build_single_field_schema_bytes(udf.return_field().as_ref())?; let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( @@ -531,15 +500,14 @@ fn encode_python_scalar_udf(py: Python<'_>, udf: &PythonFunctionScalarUDF) -> Py ], )?; - let blob = cloudpickle.call_method1("dumps", (payload,))?; - blob.extract::>() + cloudpickle(py)? + .call_method1("dumps", (payload,))? + .extract::>() } /// Inverse of [`encode_python_scalar_udf`]. fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = cloudpickle(py)?; - - let tuple = cloudpickle + let tuple = cloudpickle(py)? .call_method1("loads", (PyBytes::new(py, payload),))? .cast_into::()?; @@ -549,29 +517,9 @@ fn decode_python_scalar_udf(py: Python<'_>, payload: &[u8]) -> PyResult = tuple.get_item(3)?.extract()?; let volatility_str: String = tuple.get_item(4)?.extract()?; - let input_schema = schema_from_ipc_bytes(&input_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let input_types: Vec = input_schema - .fields() - .iter() - .map(|f| f.data_type().clone()) - .collect(); - - let return_schema = schema_from_ipc_bytes(&return_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let return_field = return_schema - .fields() - .first() - .ok_or_else(|| { - pyo3::exceptions::PyValueError::new_err( - "PythonFunctionScalarUDF return schema must contain exactly one field", - ) - })? - .as_ref() - .clone(); - - let volatility = datafusion_python_util::parse_volatility(&volatility_str) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let input_types = read_input_dtypes(&input_schema_bytes)?; + let return_field = read_single_return_field(&return_schema_bytes, "PythonFunctionScalarUDF")?; + let volatility = parse_volatility_str(&volatility_str)?; Ok(PythonFunctionScalarUDF::from_parts( name, @@ -601,6 +549,82 @@ fn schema_from_ipc_bytes(bytes: &[u8]) -> arrow::error::Result { Ok(reader.schema().as_ref().clone()) } +/// Extract the per-arg `DataType`s from a `Signature` known to be +/// `TypeSignature::Exact` (all Python-defined UDFs are constructed +/// with `Signature::exact`). Any other variant indicates the impl was +/// not built by this crate's UDF/UDAF/UDWF constructors. +fn signature_input_dtypes(signature: &Signature, kind: &str) -> PyResult> { + match &signature.type_signature { + TypeSignature::Exact(types) => Ok(types.clone()), + other => Err(pyo3::exceptions::PyValueError::new_err(format!( + "{kind} expected Signature::Exact, got {other:?}" + ))), + } +} + +/// Wrap per-arg `DataType`s in synthetic `arg_{i}` fields and emit +/// the IPC schema blob the encoder writes into the cloudpickle tuple. +/// +/// The names and `nullable: true` are arbitrary: the underlying +/// `TypeSignature::Exact` carries no per-input nullability or +/// metadata, and the receiver collapses these fields back to +/// `Vec` via [`read_input_dtypes`], so anything set here +/// beyond the data type is discarded on decode. +fn build_input_schema_bytes(dtypes: &[DataType]) -> PyResult> { + let fields: Vec = dtypes + .iter() + .enumerate() + .map(|(i, dt)| Field::new(format!("arg_{i}"), dt.clone(), true)) + .collect(); + schema_to_ipc_bytes(&Schema::new(fields)).map_err(arrow_to_py_err) +} + +/// Emit a single-field IPC schema blob. Used for return-type and +/// state-field payloads where the receiver needs to recover field +/// metadata (names, nullability, key/value attributes) verbatim. +fn build_single_field_schema_bytes(field: &Field) -> PyResult> { + schema_to_ipc_bytes(&Schema::new(vec![field.clone()])).map_err(arrow_to_py_err) +} + +/// Emit a multi-field IPC schema blob. +fn build_schema_bytes(fields: Vec) -> PyResult> { + schema_to_ipc_bytes(&Schema::new(fields)).map_err(arrow_to_py_err) +} + +/// Decode the per-arg `DataType`s the encoder wrote via +/// [`build_input_schema_bytes`]. +fn read_input_dtypes(bytes: &[u8]) -> PyResult> { + let schema = schema_from_ipc_bytes(bytes).map_err(arrow_to_py_err)?; + Ok(schema + .fields() + .iter() + .map(|f| f.data_type().clone()) + .collect()) +} + +/// Decode a single-field IPC schema blob and return that field by +/// value. `kind` names the UDF flavor in the error message produced +/// when the blob is empty (should be unreachable for sender-side +/// payloads built via [`build_single_field_schema_bytes`]). +fn read_single_return_field(bytes: &[u8], kind: &str) -> PyResult { + let schema = schema_from_ipc_bytes(bytes).map_err(arrow_to_py_err)?; + let field = schema.fields().first().ok_or_else(|| { + pyo3::exceptions::PyValueError::new_err(format!( + "{kind} return schema must contain exactly one field" + )) + })?; + Ok(field.as_ref().clone()) +} + +fn arrow_to_py_err(e: arrow::error::ArrowError) -> PyErr { + pyo3::exceptions::PyValueError::new_err(format!("{e}")) +} + +fn parse_volatility_str(s: &str) -> PyResult { + datafusion_python_util::parse_volatility(s) + .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}"))) +} + /// Stable wire-format string for a `Volatility`. Pinned to the three /// tokens [`datafusion_python_util::parse_volatility`] accepts, so an /// upstream change to `Volatility`'s `Debug` repr cannot silently @@ -671,37 +695,11 @@ pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result, udf: &PythonFunctionWindowUDF) -> PyResult> { - let cloudpickle = cloudpickle(py)?; - let signature = WindowUDFImpl::signature(udf); - let input_dtypes: Vec = match &signature.type_signature { - TypeSignature::Exact(types) => types.clone(), - other => { - return Err(pyo3::exceptions::PyValueError::new_err(format!( - "PythonFunctionWindowUDF expected Signature::Exact, got {other:?}" - ))); - } - }; - // Input fields exist only as a transport for the per-arg - // `DataType`. Names default to `arg_{i}` and nullability to - // `true` because the underlying `TypeSignature::Exact` cannot - // express either — the receiver immediately collapses these - // fields back to `Vec` when reconstructing the - // `Signature`, so any nullability or metadata set here would be - // discarded. - let input_fields: Vec = input_dtypes - .into_iter() - .enumerate() - .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) - .collect(); - let input_schema = Schema::new(input_fields); - let input_schema_bytes = schema_to_ipc_bytes(&input_schema) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - - let return_schema = Schema::new(vec![Field::new("result", udf.return_type().clone(), true)]); - let return_schema_bytes = schema_to_ipc_bytes(&return_schema) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - + let input_dtypes = signature_input_dtypes(signature, "PythonFunctionWindowUDF")?; + let input_schema_bytes = build_input_schema_bytes(&input_dtypes)?; + let return_field = Field::new("result", udf.return_type().clone(), true); + let return_schema_bytes = build_single_field_schema_bytes(&return_field)?; let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( @@ -715,14 +713,13 @@ fn encode_python_window_udf(py: Python<'_>, udf: &PythonFunctionWindowUDF) -> Py ], )?; - let blob = cloudpickle.call_method1("dumps", (payload,))?; - blob.extract::>() + cloudpickle(py)? + .call_method1("dumps", (payload,))? + .extract::>() } fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = cloudpickle(py)?; - - let tuple = cloudpickle + let tuple = cloudpickle(py)? .call_method1("loads", (PyBytes::new(py, payload),))? .cast_into::()?; @@ -732,29 +729,11 @@ fn decode_python_window_udf(py: Python<'_>, payload: &[u8]) -> PyResult = tuple.get_item(3)?.extract()?; let volatility_str: String = tuple.get_item(4)?.extract()?; - let input_schema = schema_from_ipc_bytes(&input_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let input_types: Vec = input_schema - .fields() - .iter() - .map(|f| f.data_type().clone()) - .collect(); - - let return_schema = schema_from_ipc_bytes(&return_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let return_type = return_schema - .fields() - .first() - .ok_or_else(|| { - pyo3::exceptions::PyValueError::new_err( - "PythonFunctionWindowUDF return schema must contain exactly one field", - ) - })? + let input_types = read_input_dtypes(&input_schema_bytes)?; + let return_type = read_single_return_field(&return_schema_bytes, "PythonFunctionWindowUDF")? .data_type() .clone(); - - let volatility = datafusion_python_util::parse_volatility(&volatility_str) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let volatility = parse_volatility_str(&volatility_str)?; Ok(PythonFunctionWindowUDF::new( name, @@ -806,44 +785,17 @@ pub(crate) fn try_decode_python_agg_udf(buf: &[u8]) -> Result, udf: &PythonFunctionAggregateUDF) -> PyResult> { - let cloudpickle = cloudpickle(py)?; - let signature = AggregateUDFImpl::signature(udf); - let input_dtypes: Vec = match &signature.type_signature { - TypeSignature::Exact(types) => types.clone(), - other => { - return Err(pyo3::exceptions::PyValueError::new_err(format!( - "PythonFunctionAggregateUDF expected Signature::Exact, got {other:?}" - ))); - } - }; - // Input fields exist only as a transport for the per-arg - // `DataType`. Names default to `arg_{i}` and nullability to - // `true` because the underlying `TypeSignature::Exact` cannot - // express either — the receiver immediately collapses these - // fields back to `Vec` when reconstructing the - // `Signature`, so any nullability or metadata set here would be - // discarded. - let input_fields: Vec = input_dtypes - .into_iter() - .enumerate() - .map(|(i, dt)| Field::new(format!("arg_{i}"), dt, true)) - .collect(); - let input_schema_bytes = schema_to_ipc_bytes(&Schema::new(input_fields)) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - - let return_schema = Schema::new(vec![Field::new("result", udf.return_type().clone(), true)]); - let return_schema_bytes = schema_to_ipc_bytes(&return_schema) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - + let input_dtypes = signature_input_dtypes(signature, "PythonFunctionAggregateUDF")?; + let input_schema_bytes = build_input_schema_bytes(&input_dtypes)?; + let return_field = Field::new("result", udf.return_type().clone(), true); + let return_schema_bytes = build_single_field_schema_bytes(&return_field)?; let state_fields: Vec = udf .state_fields_ref() .iter() .map(|f| f.as_ref().clone()) .collect(); - let state_schema_bytes = schema_to_ipc_bytes(&Schema::new(state_fields)) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - + let state_schema_bytes = build_schema_bytes(state_fields)?; let volatility = volatility_wire_str(signature.volatility); let payload = PyTuple::new( @@ -858,14 +810,13 @@ fn encode_python_agg_udf(py: Python<'_>, udf: &PythonFunctionAggregateUDF) -> Py ], )?; - let blob = cloudpickle.call_method1("dumps", (payload,))?; - blob.extract::>() + cloudpickle(py)? + .call_method1("dumps", (payload,))? + .extract::>() } fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult { - let cloudpickle = cloudpickle(py)?; - - let tuple = cloudpickle + let tuple = cloudpickle(py)? .call_method1("loads", (PyBytes::new(py, payload),))? .cast_into::()?; @@ -876,39 +827,19 @@ fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult = tuple.get_item(4)?.extract()?; let volatility_str: String = tuple.get_item(5)?.extract()?; - let input_schema = schema_from_ipc_bytes(&input_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let input_types: Vec = input_schema - .fields() - .iter() - .map(|f| f.data_type().clone()) - .collect(); - - let return_schema = schema_from_ipc_bytes(&return_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; - let return_type = return_schema - .fields() - .first() - .ok_or_else(|| { - pyo3::exceptions::PyValueError::new_err( - "PythonFunctionAggregateUDF return schema must contain exactly one field", - ) - })? + let input_types = read_input_dtypes(&input_schema_bytes)?; + let return_type = read_single_return_field(&return_schema_bytes, "PythonFunctionAggregateUDF")? .data_type() .clone(); - - let state_schema = schema_from_ipc_bytes(&state_schema_bytes) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; // Preserve the encoded state field metadata (names, nullability, // arbitrary key/value attributes) so the post-decode UDF reports // the same state schema as the sender's instance — important for // accumulators whose `StateFieldsArgs` consumers key off names or // nullability rather than positional `DataType`. + let state_schema = schema_from_ipc_bytes(&state_schema_bytes).map_err(arrow_to_py_err)?; let state_fields: Vec = state_schema.fields().iter().cloned().collect(); - - let volatility = datafusion_python_util::parse_volatility(&volatility_str) - .map_err(|e| pyo3::exceptions::PyValueError::new_err(format!("{e}")))?; + let volatility = parse_volatility_str(&volatility_str)?; Ok(PythonFunctionAggregateUDF::from_parts( name, From 9e15f72f147286b7ca95644c91f2b3ab4606757e Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:07:20 -0400 Subject: [PATCH 48/57] feat(codec): split UDF wire-format magic into family prefix + version byte MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The wire-format magics `DFPYUDF1` / `DFPYUDA1` / `DFPYUDW1` encoded the version in the trailing byte but the decoder did nothing with it: a payload tagged for a future tuple shape would either skip the prefix check (and fall through to `inner`) or, if the prefix still matched, detonate inside `cloudpickle.loads` with an opaque tuple-unpack error. Split the framing into a 7-byte family prefix plus a 1-byte version: * Family magics: `DFPYUDF` (scalar), `DFPYUDA` (aggregate), `DFPYUDW` (window). * `WIRE_VERSION_CURRENT: u8 = 1` — version emitted by this build. * `WIRE_VERSION_MIN_SUPPORTED: u8 = 1` — oldest version this build decodes; raise when retiring a payload shape. * `write_wire_header(buf, family)` and `strip_wire_header(buf, family, kind) -> Result>` centralize the framing. The decoder returns `Ok(None)` when the family prefix is absent (fall through to `inner`), `Ok(Some(payload))` inside the supported version range, and an `Execution("Inline Python {kind} payload wire-format version vN, this build supports vMIN..=vCURRENT. Align datafusion-python versions on sender and receiver.")` outside it. Wire format is unreleased, so the rename of the magic constants is rename-grade not BC-break-grade. Six unit tests cover the helper: family absent → fall through; truncated version byte; too-new version; too-old version (when applicable); successful round-trip; family mismatch. Module doc updated with the new framing layout and a version-bump policy. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 230 ++++++++++++++++++++++++++++++--------- 1 file changed, 178 insertions(+), 52 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index 08c5053db..f26939a7b 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -48,29 +48,36 @@ //! plans to survive a serialization round-trip. Both codecs share //! the same payload framing for that reason. //! -//! Payloads emitted by these codecs are tagged with an 8-byte magic -//! prefix so the decoder can distinguish them from arbitrary bytes -//! (empty `fun_definition` from the default codec, user FFI payloads -//! that picked a non-colliding prefix). Dispatch precedence on -//! decode: **Python-inline payload (magic prefix match) → `inner` -//! codec → caller's `FunctionRegistry` fallback.** +//! Payloads emitted by these codecs are framed as +//! ` `. The +//! family magic identifies the UDF flavor; the version byte lets the +//! decoder reject too-new or too-old payloads with a clean error +//! instead of falling into an opaque `cloudpickle` tuple-unpack +//! failure when the tuple shape changes. Dispatch precedence on +//! decode: **family match + supported version → `inner` codec → +//! caller's `FunctionRegistry` fallback.** //! -//! ## Wire-format magic prefix registry +//! ## Wire-format family registry //! -//! | Layer + kind | Magic prefix | -//! | ----------------------------- | ------------ | -//! | `PythonLogicalCodec` scalar | `DFPYUDF1` | -//! | `PythonLogicalCodec` agg | `DFPYUDA1` | -//! | `PythonLogicalCodec` window | `DFPYUDW1` | -//! | `PythonPhysicalCodec` scalar | `DFPYUDF1` | -//! | `PythonPhysicalCodec` agg | `DFPYUDA1` | -//! | `PythonPhysicalCodec` window | `DFPYUDW1` | -//! | `PythonPhysicalCodec` expr | `DFPYPE1` | -//! | User FFI extension codec | user-chosen | -//! | Default codec | (none) | +//! | Layer + kind | Family prefix | +//! | ----------------------------- | ------------- | +//! | `PythonLogicalCodec` scalar | `DFPYUDF` | +//! | `PythonLogicalCodec` agg | `DFPYUDA` | +//! | `PythonLogicalCodec` window | `DFPYUDW` | +//! | `PythonPhysicalCodec` scalar | `DFPYUDF` | +//! | `PythonPhysicalCodec` agg | `DFPYUDA` | +//! | `PythonPhysicalCodec` window | `DFPYUDW` | +//! | User FFI extension codec | user-chosen | +//! | Default codec | (none) | //! -//! Downstream FFI codecs should pick non-colliding prefixes (use a -//! `DF` namespace plus a crate-specific suffix). The codec +//! Current wire-format version is [`WIRE_VERSION_CURRENT`]; supported +//! receive range is `WIRE_VERSION_MIN_SUPPORTED..=WIRE_VERSION_CURRENT`. +//! Bump [`WIRE_VERSION_CURRENT`] whenever the cloudpickle tuple shape +//! changes; raise [`WIRE_VERSION_MIN_SUPPORTED`] when dropping support +//! for an older shape. +//! +//! Downstream FFI codecs should pick non-colliding family prefixes +//! (use a `DF` namespace plus a crate-specific suffix). The codec //! implementations in this module currently delegate every method to //! `inner`; the encoder/decoder hooks for each kind are added as the //! corresponding Python-side type becomes serializable. @@ -100,21 +107,76 @@ use crate::udaf::PythonFunctionAggregateUDF; use crate::udf::PythonFunctionScalarUDF; use crate::udwf::PythonFunctionWindowUDF; -/// Wire-format prefix that tags a `fun_definition` payload as an -/// inlined Python scalar UDF (cloudpickled tuple of name, callable, -/// input schema, return field, volatility). Defined once here so -/// the encoder and decoder cannot drift. -pub(crate) const PY_SCALAR_UDF_MAGIC: &[u8] = b"DFPYUDF1"; +// Wire-format framing for inlined Python UDF payloads. +// +// Layout: ` `. +// The family magic identifies the UDF flavor; the version byte lets +// the decoder reject too-new or too-old payloads with a clean error +// instead of falling into an opaque `cloudpickle` tuple-unpack failure +// when the tuple shape changes. Bump [`WIRE_VERSION_CURRENT`] whenever +// the tuple shape changes; raise [`WIRE_VERSION_MIN_SUPPORTED`] when +// dropping support for an older shape. + +/// Family prefix for an inlined Python scalar UDF +/// (cloudpickled tuple of name, callable, input schema, return field, +/// volatility). +pub(crate) const PY_SCALAR_UDF_FAMILY: &[u8] = b"DFPYUDF"; -/// Wire-format prefix for an inlined Python aggregate UDF +/// Family prefix for an inlined Python aggregate UDF /// (cloudpickled tuple of name, accumulator factory, input schema, /// return type, state types schema, volatility). -pub(crate) const PY_AGG_UDF_MAGIC: &[u8] = b"DFPYUDA1"; +pub(crate) const PY_AGG_UDF_FAMILY: &[u8] = b"DFPYUDA"; -/// Wire-format prefix for an inlined Python window UDF (cloudpickled -/// tuple of name, evaluator factory, input schema, return type, -/// volatility). -pub(crate) const PY_WINDOW_UDF_MAGIC: &[u8] = b"DFPYUDW1"; +/// Family prefix for an inlined Python window UDF +/// (cloudpickled tuple of name, evaluator factory, input schema, +/// return type, volatility). +pub(crate) const PY_WINDOW_UDF_FAMILY: &[u8] = b"DFPYUDW"; + +/// Wire-format version this build emits. +pub(crate) const WIRE_VERSION_CURRENT: u8 = 1; + +/// Oldest wire-format version this build still decodes. Bump when +/// retiring support for an older payload shape. +pub(crate) const WIRE_VERSION_MIN_SUPPORTED: u8 = 1; + +/// Tag `buf` with the framing header for `family` at the current +/// wire-format version. Append-only — the caller writes the +/// cloudpickle payload after. +fn write_wire_header(buf: &mut Vec, family: &[u8]) { + buf.extend_from_slice(family); + buf.push(WIRE_VERSION_CURRENT); +} + +/// Inspect the framing on `buf`. +/// +/// * `Ok(None)` — `buf` does not carry `family`. The caller should +/// delegate to its `inner` codec. +/// * `Ok(Some(payload))` — `buf` carries `family` at a version this +/// build accepts; `payload` is the cloudpickle blob. +/// * `Err(_)` — `buf` carries `family` but at a version outside +/// `WIRE_VERSION_MIN_SUPPORTED..=WIRE_VERSION_CURRENT`. The error +/// names the version and the supported range so an operator can +/// diagnose sender/receiver version drift instead of seeing an +/// opaque cloudpickle tuple-unpack failure. +fn strip_wire_header<'a>(buf: &'a [u8], family: &[u8], kind: &str) -> Result> { + if !buf.starts_with(family) { + return Ok(None); + } + let version_idx = family.len(); + let Some(&version) = buf.get(version_idx) else { + return Err(datafusion::error::DataFusionError::Execution(format!( + "Truncated inline Python {kind} payload: missing wire-format version byte" + ))); + }; + if !(WIRE_VERSION_MIN_SUPPORTED..=WIRE_VERSION_CURRENT).contains(&version) { + return Err(datafusion::error::DataFusionError::Execution(format!( + "Inline Python {kind} payload wire-format version v{version}; \ + this build supports v{WIRE_VERSION_MIN_SUPPORTED}..=v{WIRE_VERSION_CURRENT}. \ + Align datafusion-python versions on sender and receiver." + ))); + } + Ok(Some(&buf[version_idx + 1..])) +} /// `LogicalExtensionCodec` parked on every `SessionContext`. Holds /// the Python-aware encoding hooks for logical-layer types @@ -239,7 +301,7 @@ impl LogicalExtensionCodec for PythonLogicalCodec { if let Some(udf) = try_decode_python_scalar_udf(buf)? { return Ok(udf); } - } else if buf.starts_with(PY_SCALAR_UDF_MAGIC) { + } else if buf.starts_with(PY_SCALAR_UDF_FAMILY) { return Err(refuse_inline_payload("scalar UDF", name)); } self.inner.try_decode_udf(name, buf) @@ -257,7 +319,7 @@ impl LogicalExtensionCodec for PythonLogicalCodec { if let Some(udaf) = try_decode_python_agg_udf(buf)? { return Ok(udaf); } - } else if buf.starts_with(PY_AGG_UDF_MAGIC) { + } else if buf.starts_with(PY_AGG_UDF_FAMILY) { return Err(refuse_inline_payload("aggregate UDF", name)); } self.inner.try_decode_udaf(name, buf) @@ -275,7 +337,7 @@ impl LogicalExtensionCodec for PythonLogicalCodec { if let Some(udwf) = try_decode_python_window_udf(buf)? { return Ok(udwf); } - } else if buf.starts_with(PY_WINDOW_UDF_MAGIC) { + } else if buf.starts_with(PY_WINDOW_UDF_FAMILY) { return Err(refuse_inline_payload("window UDF", name)); } self.inner.try_decode_udwf(name, buf) @@ -309,7 +371,7 @@ fn refuse_inline_payload(kind: &str, name: &str) -> datafusion::error::DataFusio /// encoding on this layer too — otherwise a plan with a Python UDF /// would round-trip at the logical level but break at the physical /// level. Both layers reuse the shared payload framing -/// ([`PY_SCALAR_UDF_MAGIC`] et al.) so the wire format is identical. +/// ([`PY_SCALAR_UDF_FAMILY`] et al.) so the wire format is identical. #[derive(Debug)] pub struct PythonPhysicalCodec { inner: Arc, @@ -371,7 +433,7 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { if let Some(udf) = try_decode_python_scalar_udf(buf)? { return Ok(udf); } - } else if buf.starts_with(PY_SCALAR_UDF_MAGIC) { + } else if buf.starts_with(PY_SCALAR_UDF_FAMILY) { return Err(refuse_inline_payload("scalar UDF", name)); } self.inner.try_decode_udf(name, buf) @@ -401,7 +463,7 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { if let Some(udaf) = try_decode_python_agg_udf(buf)? { return Ok(udaf); } - } else if buf.starts_with(PY_AGG_UDF_MAGIC) { + } else if buf.starts_with(PY_AGG_UDF_FAMILY) { return Err(refuse_inline_payload("aggregate UDF", name)); } self.inner.try_decode_udaf(name, buf) @@ -419,7 +481,7 @@ impl PhysicalExtensionCodec for PythonPhysicalCodec { if let Some(udwf) = try_decode_python_window_udf(buf)? { return Ok(udwf); } - } else if buf.starts_with(PY_WINDOW_UDF_MAGIC) { + } else if buf.starts_with(PY_WINDOW_UDF_FAMILY) { return Err(refuse_inline_payload("window UDF", name)); } self.inner.try_decode_udwf(name, buf) @@ -452,21 +514,20 @@ pub(crate) fn try_encode_python_scalar_udf(node: &ScalarUDF, buf: &mut Vec) Python::attach(|py| -> Result { let bytes = encode_python_scalar_udf(py, py_udf) .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; - buf.extend_from_slice(PY_SCALAR_UDF_MAGIC); + write_wire_header(buf, PY_SCALAR_UDF_FAMILY); buf.extend_from_slice(&bytes); Ok(true) }) } /// Decode an inline Python scalar UDF payload. Returns `Ok(None)` -/// when `buf` does not carry the `DFPYUDF1` prefix, signalling the -/// caller to delegate to its `inner` codec (and eventually the +/// when `buf` does not carry the `DFPYUDF` family prefix, signalling +/// the caller to delegate to its `inner` codec (and eventually the /// `FunctionRegistry`). pub(crate) fn try_decode_python_scalar_udf(buf: &[u8]) -> Result>> { - if !buf.starts_with(PY_SCALAR_UDF_MAGIC) { + let Some(payload) = strip_wire_header(buf, PY_SCALAR_UDF_FAMILY, "scalar UDF")? else { return Ok(None); - } - let payload = &buf[PY_SCALAR_UDF_MAGIC.len()..]; + }; Python::attach(|py| -> Result>> { let udf = decode_python_scalar_udf(py, payload) @@ -675,17 +736,16 @@ pub(crate) fn try_encode_python_window_udf(node: &WindowUDF, buf: &mut Vec) Python::attach(|py| -> Result { let bytes = encode_python_window_udf(py, py_udf) .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; - buf.extend_from_slice(PY_WINDOW_UDF_MAGIC); + write_wire_header(buf, PY_WINDOW_UDF_FAMILY); buf.extend_from_slice(&bytes); Ok(true) }) } pub(crate) fn try_decode_python_window_udf(buf: &[u8]) -> Result>> { - if !buf.starts_with(PY_WINDOW_UDF_MAGIC) { + let Some(payload) = strip_wire_header(buf, PY_WINDOW_UDF_FAMILY, "window UDF")? else { return Ok(None); - } - let payload = &buf[PY_WINDOW_UDF_MAGIC.len()..]; + }; Python::attach(|py| -> Result>> { let udf = decode_python_window_udf(py, payload) @@ -765,17 +825,16 @@ pub(crate) fn try_encode_python_agg_udf(node: &AggregateUDF, buf: &mut Vec) Python::attach(|py| -> Result { let bytes = encode_python_agg_udf(py, py_udf) .map_err(|e| datafusion::error::DataFusionError::External(Box::new(e)))?; - buf.extend_from_slice(PY_AGG_UDF_MAGIC); + write_wire_header(buf, PY_AGG_UDF_FAMILY); buf.extend_from_slice(&bytes); Ok(true) }) } pub(crate) fn try_decode_python_agg_udf(buf: &[u8]) -> Result>> { - if !buf.starts_with(PY_AGG_UDF_MAGIC) { + let Some(payload) = strip_wire_header(buf, PY_AGG_UDF_FAMILY, "aggregate UDF")? else { return Ok(None); - } - let payload = &buf[PY_AGG_UDF_MAGIC.len()..]; + }; Python::attach(|py| -> Result>> { let udf = decode_python_agg_udf(py, payload) @@ -850,3 +909,70 @@ fn decode_python_agg_udf(py: Python<'_>, payload: &[u8]) -> PyResult Date: Fri, 15 May 2026 13:08:33 -0400 Subject: [PATCH 49/57] fix(codec): point strict refusal at the sender, not the receiver MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous refusal text told the operator to "re-encode the bytes with inlining enabled" — but the operator seeing this error is the receiver, whose session has inlining off. The receiver cannot re-encode bytes it did not produce; only the sender can. Reword to direct the fix at the side that can apply it: ask the sender to re-encode with inlining disabled (the by-name form a strict receiver actually accepts), or register the UDF on the receiver and enable inlining on both sides. Phrase preserves the "inlining is disabled" substring the strict-mode test asserts against. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index f26939a7b..be012665d 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -353,10 +353,10 @@ fn refuse_inline_payload(kind: &str, name: &str) -> datafusion::error::DataFusio // error would mis-route it into "fix your SQL" buckets. datafusion::error::DataFusionError::Execution(format!( "Refusing to deserialize inline Python {kind} '{name}': Python UDF \ - inlining is disabled on this session. Re-encode the bytes with \ - inlining enabled, or register '{name}' on the sender's session \ - before encode (and on the receiver before decode) so the UDF \ - travels by name." + inlining is disabled on this session. Ask the sender to re-encode \ + with inlining disabled (so the UDF travels by name), or register \ + '{name}' on this receiver's session and enable inlining on both \ + sides — receivers cannot re-encode bytes they did not produce." )) } From fafefd012e2665d5bb8cafe31baf66541c5078c6 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:10:33 -0400 Subject: [PATCH 50/57] fix(udf): log Python __eq__ exceptions instead of silently mapping to unequal MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `PartialEq` on `PythonFunctionScalarUDF` / `PythonFunctionAggregateUDF` / `PythonFunctionWindowUDF` used `unwrap_or(false)` when the underlying Python `__eq__` raised. Two same-callable UDFs whose `__eq__` happened to throw — e.g. a deliberately strict `__eq__` that runs validation — would compare unequal silently, breaking expression dedup and cache lookups without leaving any trace for an operator to investigate. Rust's `PartialEq` cannot return `Result`, so `false` remains the conservative choice (better to over-distinguish than wrongly merge), but the exception is now logged at `log::debug` with the UDF's registered name and the exception text. Mark each call site with a `FIXME` pointing at the upstream `*UDFImpl` traits — when those expose a fallible `PartialEq` we can drop the fallback and surface the error. Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/udaf.rs | 14 +++++++++++++- crates/core/src/udf.rs | 22 +++++++++++++++++++++- crates/core/src/udwf.rs | 14 +++++++++++++- 3 files changed, 47 insertions(+), 3 deletions(-) diff --git a/crates/core/src/udaf.rs b/crates/core/src/udaf.rs index d77793506..cb84fa375 100644 --- a/crates/core/src/udaf.rs +++ b/crates/core/src/udaf.rs @@ -243,10 +243,22 @@ impl PartialEq for PythonFunctionAggregateUDF { // `__eq__` only for two distinct callables. && (self.accumulator.as_ptr() == other.accumulator.as_ptr() || Python::attach(|py| { + // See `PythonFunctionScalarUDF::eq` for the + // rationale on swallowing the exception as `false` + // and logging at `debug`. FIXME: revisit if + // upstream `AggregateUDFImpl` exposes a fallible + // `PartialEq`. self.accumulator .bind(py) .eq(other.accumulator.bind(py)) - .unwrap_or(false) + .unwrap_or_else(|e| { + log::debug!( + target: "datafusion_python::udaf", + "PythonFunctionAggregateUDF {:?} __eq__ raised; treating as unequal: {e}", + self.name, + ); + false + }) })) } } diff --git a/crates/core/src/udf.rs b/crates/core/src/udf.rs index 0fdb16e44..72cdddba1 100644 --- a/crates/core/src/udf.rs +++ b/crates/core/src/udf.rs @@ -112,7 +112,27 @@ impl PartialEq for PythonFunctionScalarUDF { // circuits before touching the GIL. && (self.func.as_ptr() == other.func.as_ptr() || Python::attach(|py| { - self.func.bind(py).eq(other.func.bind(py)).unwrap_or(false) + // Rust's `PartialEq` cannot return `Result`, so we + // have to pick a side when Python `__eq__` raises. + // `false` is the conservative choice — better to + // report two UDFs as distinct than to wrongly + // merge them — but the silent miss can still + // surface as expression-dedup or cache-lookup + // anomalies. Log at `debug` so the failure is + // observable without flooding production logs. + // FIXME: revisit if upstream `ScalarUDFImpl` + // exposes a fallible `PartialEq`. + self.func + .bind(py) + .eq(other.func.bind(py)) + .unwrap_or_else(|e| { + log::debug!( + target: "datafusion_python::udf", + "PythonFunctionScalarUDF {:?} __eq__ raised; treating as unequal: {e}", + self.name, + ); + false + }) })) } } diff --git a/crates/core/src/udwf.rs b/crates/core/src/udwf.rs index ed6932fe8..5ce09e6d2 100644 --- a/crates/core/src/udwf.rs +++ b/crates/core/src/udwf.rs @@ -329,10 +329,22 @@ impl PartialEq for PythonFunctionWindowUDF { // `__eq__` only for two distinct callables. && (self.evaluator.as_ptr() == other.evaluator.as_ptr() || Python::attach(|py| { + // See `PythonFunctionScalarUDF::eq` for the + // rationale on swallowing the exception as `false` + // and logging at `debug`. FIXME: revisit if + // upstream `WindowUDFImpl` exposes a fallible + // `PartialEq`. self.evaluator .bind(py) .eq(other.evaluator.bind(py)) - .unwrap_or(false) + .unwrap_or_else(|e| { + log::debug!( + target: "datafusion_python::udwf", + "PythonFunctionWindowUDF {:?} __eq__ raised; treating as unequal: {e}", + self.name, + ); + false + }) })) } } From 865d9df83ca8b075c49d769af7d5c26d312d1d99 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:11:18 -0400 Subject: [PATCH 51/57] docs(expr): restate to_bytes behavior when ctx is None MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous docstring on Expr.to_bytes called out the no-ctx case ("Without ctx a default codec is used"). The pickle-support rewrite replaced the docstring with a description of what travels inside the bytes but dropped the sentence that answered the immediate question a reader has when they see the optional `ctx` parameter. Re-add a one-paragraph explanation: with `ctx`, encoding routes through the session's installed LogicalExtensionCodec — so `with_python_udf_inlining` takes effect; without `ctx`, the default codec is used (Python UDF inlining on, no user-installed extension codec). Co-Authored-By: Claude Opus 4.7 (1M context) --- python/datafusion/expr.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/datafusion/expr.py b/python/datafusion/expr.py index a6dc016c9..2ea060cc1 100644 --- a/python/datafusion/expr.py +++ b/python/datafusion/expr.py @@ -439,6 +439,12 @@ def to_bytes(self, ctx: SessionContext | None = None) -> bytes: Use this — or :func:`pickle.dumps` — to send an expression to a worker process for distributed evaluation. + When ``ctx`` is supplied, encoding routes through that session's + installed :class:`LogicalExtensionCodec` (so settings like + :meth:`SessionContext.with_python_udf_inlining` take effect). + When ``ctx`` is ``None``, the default codec is used (Python UDF + inlining on, no user-installed extension codec). + Built-in functions and Python UDFs (scalar, aggregate, window) travel inside the returned bytes; the worker does not need to pre-register them. UDFs imported via the FFI capsule protocol From cbfa6521cf3c5a809bc66856071f59e847f33cea Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:12:46 -0400 Subject: [PATCH 52/57] docs(ipc): explain why _resolve_ctx imports SessionContext lazily The `# noqa: PLC0415` flag silenced ruff's "import outside top-level" lint but didn't say why the import was placed there in the first place. Add a comment naming the actual reason: `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, and neither pulls `ipc.py` back), but a single new import added to `context.py`'s transitive dependency tree could turn this into a real circular import. Deferring keeps `datafusion.ipc` import-order-independent. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/datafusion/ipc.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/python/datafusion/ipc.py b/python/datafusion/ipc.py index 7efd4b2ea..c97e54ec4 100644 --- a/python/datafusion/ipc.py +++ b/python/datafusion/ipc.py @@ -159,6 +159,13 @@ def _resolve_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() From dfcf3bc14928d6ec6b58ca4da21525d812c6f99e Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:15:32 -0400 Subject: [PATCH 53/57] chore: replace blanket S301 allow with per-line noqa at pickle.loads MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The pickle-Expr work added `S301` (suspicious-pickle-usage) to the `per-file-ignores` for both `python/tests/*` and `examples/*`. That silenced ruff's pickle-usage warning everywhere in those trees, not just at the call sites that genuinely need it — a future test that adds an unrelated `pickle.loads` on untrusted bytes would pass review without ever pinging the linter. Drop the blanket allows and tag each intentional `pickle.loads` call with a per-line `# noqa: S301` instead (12 sites total: 9 in `test_pickle_expr.py`, 2 in `_pickle_multiprocessing_helpers.py`, 1 in the FFI example test). New `pickle.loads` calls now have to justify themselves with their own noqa, keeping S301 a real signal. Production code (`Expr.__reduce__`) only calls `pickle.dumps`, which S301 does not cover, so no noqa is needed outside tests/examples. Co-Authored-By: Claude Opus 4.7 (1M context) --- .../python/tests/_test_pickle_strict_ffi.py | 2 +- pyproject.toml | 2 -- .../tests/_pickle_multiprocessing_helpers.py | 4 ++-- python/tests/test_pickle_expr.py | 18 +++++++++--------- 4 files changed, 12 insertions(+), 14 deletions(-) diff --git a/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py b/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py index ac5e972e2..67c0b245a 100644 --- a/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py +++ b/examples/datafusion-ffi-example/python/tests/_test_pickle_strict_ffi.py @@ -98,7 +98,7 @@ def test_strict_ffi_udf_pickle_roundtrip_via_thread_locals(): set_worker_ctx(receiver) try: - restored = pickle.loads(blob) + restored = pickle.loads(blob) # noqa: S301 finally: clear_worker_ctx() diff --git a/pyproject.toml b/pyproject.toml index f7d106836..a02f4608a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -127,7 +127,6 @@ extend-allowed-calls = ["datafusion.lit", "lit"] "PT011", "RUF015", "S101", - "S301", "S608", "SLF", ] @@ -141,7 +140,6 @@ extend-allowed-calls = ["datafusion.lit", "lit"] "PLR2004", "RUF015", "S101", - "S301", "T201", "W505", ] diff --git a/python/tests/_pickle_multiprocessing_helpers.py b/python/tests/_pickle_multiprocessing_helpers.py index 4aa56efab..4f04967f2 100644 --- a/python/tests/_pickle_multiprocessing_helpers.py +++ b/python/tests/_pickle_multiprocessing_helpers.py @@ -69,7 +69,7 @@ def unpickle_and_describe(blob: bytes) -> str: """Unpickle a proto-bytes blob and return its canonical name.""" import pickle - expr = pickle.loads(blob) + expr = pickle.loads(blob) # noqa: S301 return expr.canonical_name() @@ -82,7 +82,7 @@ def unpickle_and_evaluate(blob: bytes, batch: list[int]) -> list[int]: """ import pickle - expr = pickle.loads(blob) + expr = pickle.loads(blob) # noqa: S301 ctx = SessionContext() df = ctx.from_pydict({"a": batch}) out = df.with_column("result", expr).select("result") diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index bdf41eb3c..27a5037d4 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -67,7 +67,7 @@ class TestProtoRoundTrip: def test_builtin_round_trip(self): e = col("a") + lit(1) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 assert decoded.canonical_name() == e.canonical_name() def test_to_bytes_from_bytes(self): @@ -107,14 +107,14 @@ def test_udf_decodes_into_fresh_ctx(self): def test_udf_decodes_via_pickle_with_no_worker_ctx(self): e = _double_udf()(col("a")) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 assert "double" in decoded.canonical_name() def test_udf_decodes_via_pickle_with_worker_ctx(self): set_worker_ctx(SessionContext()) e = _double_udf()(col("a")) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 assert "double" in decoded.canonical_name() def test_closure_capturing_udf_names_match(self): @@ -132,7 +132,7 @@ def fn(arr): ) e = u(col("a")) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 # Round-trip names match; functional verification of captured state # happens in test_pickle_multiprocessing via an actual UDF call. assert decoded.canonical_name() == e.canonical_name() @@ -189,7 +189,7 @@ def test_agg_udf_decodes_via_pickle_with_no_worker_ctx(self): u = self._build_aggregate_udf() e = u(col("a")) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 assert "count_all" in decoded.canonical_name() def test_agg_udf_evaluates_after_roundtrip(self): @@ -197,7 +197,7 @@ def test_agg_udf_evaluates_after_roundtrip(self): partitions, exercising the round-tripped state-field schema.""" u = self._build_aggregate_udf() e = u(col("a")) - decoded = pickle.loads(pickle.dumps(e)) + decoded = pickle.loads(pickle.dumps(e)) # noqa: S301 ctx = SessionContext() df = ctx.from_pydict({"a": [1, 2, 3, 4, 5]}) @@ -242,7 +242,7 @@ def test_window_udf_decodes_via_pickle_with_no_worker_ctx(self): u = self._build_window_udf() e = u(col("a")) blob = pickle.dumps(e) - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 assert "count_up" in decoded.canonical_name() @@ -344,7 +344,7 @@ def test_sender_ctx_strict_roundtrip_via_pickle(self): worker.register_udf(u) set_worker_ctx(worker) try: - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 finally: clear_worker_ctx() @@ -369,7 +369,7 @@ def test_sender_ctx_strict_pickle_accepted_by_inline_worker_with_registry(self): worker.register_udf(u) set_worker_ctx(worker) try: - decoded = pickle.loads(blob) + decoded = pickle.loads(blob) # noqa: S301 finally: clear_worker_ctx() From b9c60454ce9f268a610e7fbe251a2295b3765506 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:16:02 -0400 Subject: [PATCH 54/57] test(pickle): drop dead register_udf in strict refusal test MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `test_strict_decoder_refuses_inline_payload` registered the UDF on the strict receiver, but the codec refusal trips before the receiver's function registry is ever consulted — the registration was never exercised. Worse: keeping it there would mask a real regression that moved the strict-mode check downstream of registry lookup, because the registry hit would resolve the UDF instead of letting the refusal surface. Drop the register_udf call and call out in the docstring why the absence is load-bearing. Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_expr.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 27a5037d4..7e2605117 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -287,13 +287,18 @@ def test_strict_roundtrip_via_registry(self): def test_strict_decoder_refuses_inline_payload(self): """An inline-encoded blob fed to a strict receiver raises with a - clear error rather than silently invoking cloudpickle.loads.""" + clear error rather than silently invoking cloudpickle.loads. + + The receiver is intentionally *not* given a matching + registration: the codec refusal must trip before the registry + is ever consulted, so registering the UDF here would only mask + a regression that moved the check after registry lookup. + """ sender = SessionContext() u = self._build_double_udf() blob = u(col("a")).to_bytes(sender) strict_receiver = SessionContext().with_python_udf_inlining(enabled=False) - strict_receiver.register_udf(u) # `RuntimeError` (not bare `Exception`): the codec refusal is # surfaced through `parse_expr` → `PyRuntimeError`. Tightening # the assertion catches a regression that swallows the refusal From 13430570eef78a8706968650c772572a4c2b9eac Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:16:48 -0400 Subject: [PATCH 55/57] test(pickle): cover with_python_udf_inlining toggle off-then-on MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The strict-mode tests exercise inline→strict but not the reverse: `with_python_udf_inlining(enabled=False).with_python_udf_inlining(enabled=True)` was idempotent in code but never verified, so a future change that mutated shared codec state instead of cloning (e.g. interning the codec across cloned contexts) would silently leave the second context in strict mode without any test tripping. Add `test_toggle_off_then_on_restores_inline_encoding`: encode the same expression once via a fresh `SessionContext` and once via a context toggled off-then-on, assert the blobs are byte-identical, and decode the toggled-back blob through a fresh context to confirm the payload is self-contained inline (not a strict by-name payload that needs registry resolution). Co-Authored-By: Claude Opus 4.7 (1M context) --- python/tests/test_pickle_expr.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/python/tests/test_pickle_expr.py b/python/tests/test_pickle_expr.py index 7e2605117..62692f8a6 100644 --- a/python/tests/test_pickle_expr.py +++ b/python/tests/test_pickle_expr.py @@ -272,6 +272,36 @@ def test_strict_encoder_emits_smaller_blob(self): assert len(blob_strict) < len(blob_inline) // 4 + def test_toggle_off_then_on_restores_inline_encoding(self): + """`with_python_udf_inlining` is per-call clone semantics: + flipping off and then on must produce a context that emits the + same inline form as a fresh default context, byte-for-byte. + + Guards against a regression where the off→on transition leaves + the codec in a sticky strict state (e.g. by mutating shared + codec state instead of cloning). + """ + u = self._build_double_udf() + e = u(col("a")) + + baseline = SessionContext() + toggled = ( + SessionContext() + .with_python_udf_inlining(enabled=False) + .with_python_udf_inlining(enabled=True) + ) + + blob_baseline = e.to_bytes(baseline) + blob_toggled = e.to_bytes(toggled) + + assert blob_baseline == blob_toggled + + # Sanity check the decoded form against a fresh ctx — the + # toggled-back blob should be self-contained inline, not a + # strict by-name payload that needs registry resolution. + decoded = Expr.from_bytes(blob_toggled, ctx=SessionContext()) + assert "double" in decoded.canonical_name() + def test_strict_roundtrip_via_registry(self): """When both sender and receiver disable inlining, the UDF travels by name only and the receiver resolves it from its From 8aad8daa2b41c20065578373286498c9ea35263b Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:17:55 -0400 Subject: [PATCH 56/57] docs(distributing): narrow Python 3.14 start-method note to Linux MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The previous wording said Python 3.14 changed the "POSIX default" from fork to forkserver, but macOS (also POSIX) has defaulted to `spawn` since Python 3.8 — only Linux was using `fork` as default, and only Linux flipped to `forkserver` in 3.14. Windows has always been `spawn`. Replace "POSIX" with "Linux" and add a parenthetical naming the defaults on the other platforms so readers don't infer the wrong platform coverage from the change note. Co-Authored-By: Claude Opus 4.7 (1M context) --- docs/source/user-guide/io/distributing_work.rst | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 3fa74a275..396428149 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -238,12 +238,13 @@ context to resolve. Python 3.14 default change ~~~~~~~~~~~~~~~~~~~~~~~~~~ -Python 3.14 changed the POSIX default start method for -:py:mod:`multiprocessing` from ``fork`` to ``forkserver``. With -``fork``, any state set in the parent was visible in workers via -copy-on-write; with ``forkserver`` and ``spawn`` it is not. The -:py:func:`~datafusion.ipc.set_worker_ctx` pattern works on every -start method — prefer it over relying on inherited state. +Python 3.14 changed the Linux default start method for +:py:mod:`multiprocessing` from ``fork`` to ``forkserver`` (macOS has +defaulted to ``spawn`` since Python 3.8; Windows has always used +``spawn``). With ``fork``, any state set in the parent was visible in +workers via copy-on-write; with ``forkserver`` and ``spawn`` it is +not. The :py:func:`~datafusion.ipc.set_worker_ctx` pattern works on +every start method — prefer it over relying on inherited state. Practical considerations ~~~~~~~~~~~~~~~~~~~~~~~~ From a843b37ef0f74eefb99f3196614da272b75ba1b7 Mon Sep 17 00:00:00 2001 From: Tim Saucer Date: Fri, 15 May 2026 13:19:26 -0400 Subject: [PATCH 57/57] docs: centralize pickle security caveat, link from codec/context docs MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The pickle-on-untrusted-input warning was written out three times: in the `PythonLogicalCodec::with_python_udf_inlining` rustdoc, in the matching Python `SessionContext.with_python_udf_inlining` docstring, and in two places inside `distributing_work.rst` (a free-floating paragraph at the end of the inlining section plus the dedicated Security warning block). Three copies of the same load-bearing text would inevitably drift. Pick `distributing_work.rst` Security section as canonical: * Keep the Security warning block intact — it is the single source of truth. * Trim the redundant "Note that pickle.loads itself remains unsafe..." paragraph above it to a one-line summary + Sphinx cross-reference (`Security`_) so the section header still anchors the link. * Replace the Python docstring's 5-line warning paragraph with a one-sentence summary + `:doc:` link to `distributing_work`. * Replace the rustdoc warning with a one-sentence summary, a relative pointer to `docs/source/user-guide/io/distributing_work.rst`, and a link to the upstream Python pickle module security warning so the rustdoc remains self-contained for someone reading just the crate. Behavior unchanged; the strict-toggle test continues to assert on the substring "inlining is disabled", which lives in `refuse_inline_payload` (separate from these docs). Co-Authored-By: Claude Opus 4.7 (1M context) --- crates/core/src/codec.rs | 16 ++++++++-------- docs/source/user-guide/io/distributing_work.rst | 12 ++++-------- python/datafusion/context.py | 10 +++++----- 3 files changed, 17 insertions(+), 21 deletions(-) diff --git a/crates/core/src/codec.rs b/crates/core/src/codec.rs index be012665d..326310740 100644 --- a/crates/core/src/codec.rs +++ b/crates/core/src/codec.rs @@ -212,17 +212,17 @@ impl PythonLogicalCodec { /// cross-language wire bytes, or reject `cloudpickle.loads` on /// untrusted `from_bytes` input. /// - /// Security scope: strict mode (`false`) protects only the codec + /// Security scope: strict mode (`false`) narrows only the codec /// layer — it stops `Expr::from_bytes` from invoking /// `cloudpickle.loads` on the inline `DFPY*` payload. It does - /// **not** make `pickle.loads(untrusted_bytes)` safe. Python's - /// pickle protocol permits arbitrary code execution via - /// `__reduce__` (and `Expr.__reduce__` returns - /// `Expr._reconstruct(bytes)` — an honest reducer here, but the - /// outer pickle stream can contain any reducer). Treat every + /// **not** make `pickle.loads(untrusted_bytes)` safe; treat every /// `pickle.loads` on untrusted input as unsafe regardless of this - /// setting; the toggle only narrows the surface inside - /// `from_bytes`. + /// setting. See `docs/source/user-guide/io/distributing_work.rst` + /// (Security section) for the full threat model, and Python's + /// [pickle module security warning][1] for why `pickle.loads` is + /// unsafe in general. + /// + /// [1]: https://docs.python.org/3/library/pickle.html#module-pickle pub fn with_python_udf_inlining(mut self, enabled: bool) -> Self { self.python_udf_inlining = enabled; self diff --git a/docs/source/user-guide/io/distributing_work.rst b/docs/source/user-guide/io/distributing_work.rst index 396428149..c1dabb7c7 100644 --- a/docs/source/user-guide/io/distributing_work.rst +++ b/docs/source/user-guide/io/distributing_work.rst @@ -308,14 +308,10 @@ side also refuses inline payloads. Explicit honor the supplied ``ctx`` directly and ignore the sender / worker contexts. -Note that :py:func:`pickle.loads` itself remains unsafe on untrusted -input regardless of this setting — an attacker producing the outer -pickle envelope can execute arbitrary code before the codec ever -sees the bytes (see the -`pickle module security warning -`_ in -the Python standard library docs). The toggle only protects the -:py:meth:`Expr.from_bytes` API surface. +The toggle only narrows the :py:meth:`Expr.from_bytes` surface; +:py:func:`pickle.loads` on untrusted bytes remains unsafe regardless +of this setting. See the `Security`_ section below for the full +threat model. Security ~~~~~~~~ diff --git a/python/datafusion/context.py b/python/datafusion/context.py index d89dcbdf1..e3ceb3b05 100644 --- a/python/datafusion/context.py +++ b/python/datafusion/context.py @@ -1802,11 +1802,11 @@ def with_python_udf_inlining(self, *, enabled: bool) -> SessionContext: :func:`datafusion.ipc.set_worker_ctx` for the corresponding :func:`pickle.loads`. - ``pickle.loads`` on untrusted bytes remains unsafe regardless of - this setting (see the `pickle module security warning - `_ - in the Python standard library docs). Only the - ``to_bytes`` / ``from_bytes`` API is affected. + For the full security model, see + :doc:`/user-guide/io/distributing_work` (Security section). In + short: this toggle narrows only the :meth:`Expr.from_bytes` + surface; :func:`pickle.loads` on untrusted bytes remains + unsafe regardless of the toggle. """ new_internal = self.ctx.with_python_udf_inlining(enabled) new = SessionContext.__new__(SessionContext)