datafusion_python/

udwf.rs

// 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.

use std::ops::Range;
use std::ptr::NonNull;
use std::sync::Arc;

use arrow::array::{Array, ArrayData, ArrayRef, make_array};
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::window_state::WindowAggState;
use datafusion::logical_expr::{
    PartitionEvaluator, PartitionEvaluatorFactory, Signature, Volatility, WindowUDF, WindowUDFImpl,
};
use datafusion::scalar::ScalarValue;
use datafusion_ffi::udwf::FFI_WindowUDF;
use datafusion_python_util::parse_volatility;
use pyo3::exceptions::PyValueError;
use pyo3::prelude::*;
use pyo3::types::{PyCapsule, PyList, PyTuple};

use crate::common::data_type::PyScalarValue;
use crate::errors::{PyDataFusionResult, to_datafusion_err};
use crate::expr::PyExpr;

#[derive(Debug)]
struct RustPartitionEvaluator {
    evaluator: Py<PyAny>,
}

impl RustPartitionEvaluator {
    fn new(evaluator: Py<PyAny>) -> Self {
        Self { evaluator }
    }
}

impl PartitionEvaluator for RustPartitionEvaluator {
    fn memoize(&mut self, _state: &mut WindowAggState) -> Result<()> {
        Python::attach(|py| self.evaluator.bind(py).call_method0("memoize").map(|_| ()))
            .map_err(|e| DataFusionError::Execution(format!("{e}")))
    }

    fn get_range(&self, idx: usize, n_rows: usize) -> Result<Range<usize>> {
        Python::attach(|py| {
            let py_args = vec![idx.into_pyobject(py)?, n_rows.into_pyobject(py)?];
            let py_args = PyTuple::new(py, py_args)?;

            self.evaluator
                .bind(py)
                .call_method1("get_range", py_args)
                .and_then(|v| {
                    let tuple: Bound<'_, PyTuple> = v.extract()?;
                    if tuple.len() != 2 {
                        return Err(PyValueError::new_err(format!(
                            "Expected get_range to return tuple of length 2. Received length {}",
                            tuple.len()
                        )));
                    }

                    let start: usize = tuple.get_item(0).unwrap().extract()?;
                    let end: usize = tuple.get_item(1).unwrap().extract()?;

                    Ok(Range { start, end })
                })
        })
        .map_err(|e| DataFusionError::Execution(format!("{e}")))
    }

    fn is_causal(&self) -> bool {
        Python::attach(|py| {
            self.evaluator
                .bind(py)
                .call_method0("is_causal")
                .and_then(|v| v.extract())
                .unwrap_or(false)
        })
    }

    fn evaluate_all(&mut self, values: &[ArrayRef], num_rows: usize) -> Result<ArrayRef> {
        Python::attach(|py| {
            let py_values = PyList::new(
                py,
                values
                    .iter()
                    .map(|arg| arg.into_data().to_pyarrow(py).unwrap()),
            )?;
            let py_num_rows = num_rows.into_pyobject(py)?;
            let py_args = PyTuple::new(py, vec![py_values.as_any(), &py_num_rows])?;

            self.evaluator
                .bind(py)
                .call_method1("evaluate_all", py_args)
                .map(|v| {
                    let array_data = ArrayData::from_pyarrow_bound(&v).unwrap();
                    make_array(array_data)
                })
        })
        .map_err(to_datafusion_err)
    }

    fn evaluate(&mut self, values: &[ArrayRef], range: &Range<usize>) -> Result<ScalarValue> {
        Python::attach(|py| {
            let py_values = PyList::new(
                py,
                values
                    .iter()
                    .map(|arg| arg.into_data().to_pyarrow(py).unwrap()),
            )?;
            let range_tuple = PyTuple::new(py, vec![range.start, range.end])?;
            let py_args = PyTuple::new(py, vec![py_values.as_any(), range_tuple.as_any()])?;

            self.evaluator
                .bind(py)
                .call_method1("evaluate", py_args)
                .and_then(|v| v.extract::<PyScalarValue>())
                .map(|v| v.0)
        })
        .map_err(to_datafusion_err)
    }

    fn evaluate_all_with_rank(
        &self,
        num_rows: usize,
        ranks_in_partition: &[Range<usize>],
    ) -> Result<ArrayRef> {
        Python::attach(|py| {
            let ranks = ranks_in_partition
                .iter()
                .map(|r| PyTuple::new(py, vec![r.start, r.end]))
                .collect::<PyResult<Vec<_>>>()?;

            // 1. cast args to Pyarrow array
            let py_args = vec![
                num_rows.into_pyobject(py)?.into_any(),
                PyList::new(py, ranks)?.into_any(),
            ];

            let py_args = PyTuple::new(py, py_args)?;

            // 2. call function
            self.evaluator
                .bind(py)
                .call_method1("evaluate_all_with_rank", py_args)
                .map(|v| {
                    let array_data = ArrayData::from_pyarrow_bound(&v).unwrap();
                    make_array(array_data)
                })
        })
        .map_err(to_datafusion_err)
    }

    fn supports_bounded_execution(&self) -> bool {
        Python::attach(|py| {
            self.evaluator
                .bind(py)
                .call_method0("supports_bounded_execution")
                .and_then(|v| v.extract())
                .unwrap_or(false)
        })
    }

    fn uses_window_frame(&self) -> bool {
        Python::attach(|py| {
            self.evaluator
                .bind(py)
                .call_method0("uses_window_frame")
                .and_then(|v| v.extract())
                .unwrap_or(false)
        })
    }

    fn include_rank(&self) -> bool {
        Python::attach(|py| {
            self.evaluator
                .bind(py)
                .call_method0("include_rank")
                .and_then(|v| v.extract())
                .unwrap_or(false)
        })
    }
}

fn instantiate_partition_evaluator(evaluator: &Py<PyAny>) -> Result<Box<dyn PartitionEvaluator>> {
    let instance = Python::attach(|py| {
        evaluator
            .call0(py)
            .map_err(|e| DataFusionError::Execution(e.to_string()))
    })?;
    Ok(Box::new(RustPartitionEvaluator::new(instance)))
}

/// Wrap a Python evaluator factory in a `PartitionEvaluatorFactory`.
///
/// Retained for downstream callers that previously consumed this
/// helper to build a [`PartitionEvaluatorFactory`] for factory-based
/// APIs. New in-crate code should construct a
/// [`PythonFunctionWindowUDF`] directly so the codec can downcast and
/// ship it inline.
pub fn to_rust_partition_evaluator(evaluator: Py<PyAny>) -> PartitionEvaluatorFactory {
    Arc::new(move || instantiate_partition_evaluator(&evaluator))
}

/// Represents an WindowUDF
#[pyclass(
    from_py_object,
    frozen,
    name = "WindowUDF",
    module = "datafusion",
    subclass
)]
#[derive(Debug, Clone)]
pub struct PyWindowUDF {
    pub(crate) function: WindowUDF,
}

#[pymethods]
impl PyWindowUDF {
    #[new]
    #[pyo3(signature=(name, evaluator, input_types, return_type, volatility))]
    fn new(
        name: &str,
        evaluator: Py<PyAny>,
        input_types: Vec<PyArrowType<DataType>>,
        return_type: PyArrowType<DataType>,
        volatility: &str,
    ) -> PyResult<Self> {
        let return_type = return_type.0;
        let input_types: Vec<DataType> = input_types.into_iter().map(|t| t.0).collect();

        let function = WindowUDF::from(PythonFunctionWindowUDF::new(
            name,
            evaluator,
            input_types,
            return_type,
            parse_volatility(volatility)?,
        ));
        Ok(Self { function })
    }

    /// creates a new PyExpr with the call of the udf
    #[pyo3(signature = (*args))]
    fn __call__(&self, args: Vec<PyExpr>) -> PyResult<PyExpr> {
        let args = args.iter().map(|e| e.expr.clone()).collect();
        Ok(self.function.call(args).into())
    }

    #[staticmethod]
    pub fn from_pycapsule(func: Bound<'_, PyAny>) -> PyDataFusionResult<Self> {
        let capsule = if func.hasattr("__datafusion_window_udf__")? {
            func.getattr("__datafusion_window_udf__")?.call0()?
        } else {
            func
        };

        let capsule = capsule.cast::<PyCapsule>().map_err(to_datafusion_err)?;
        let data: NonNull<FFI_WindowUDF> = capsule
            .pointer_checked(Some(c"datafusion_window_udf"))?
            .cast();
        let udwf = unsafe { data.as_ref() };
        let udwf: Arc<dyn WindowUDFImpl> = udwf.into();

        Ok(Self {
            function: WindowUDF::new_from_shared_impl(udwf),
        })
    }

    fn __repr__(&self) -> PyResult<String> {
        Ok(format!("WindowUDF({})", self.function.name()))
    }

    #[getter]
    fn name(&self) -> &str {
        self.function.name()
    }
}

/// `WindowUDFImpl` for Python-defined window UDFs.
///
/// Holds the Python evaluator factory directly so the codec can
/// downcast and cloudpickle it across process boundaries. Replaces
/// the prior factory-erased `MultiColumnWindowUDF`; the old name is
/// kept as a type alias below for backward compatibility.
#[derive(Debug)]
pub struct PythonFunctionWindowUDF {
    name: String,
    evaluator: Py<PyAny>,
    signature: Signature,
    return_type: DataType,
}

/// Backward-compatible alias for downstream crates that referenced the
/// previous struct name. New code should use [`PythonFunctionWindowUDF`].
pub type MultiColumnWindowUDF = PythonFunctionWindowUDF;

impl PythonFunctionWindowUDF {
    pub fn new(
        name: impl Into<String>,
        evaluator: Py<PyAny>,
        input_types: Vec<DataType>,
        return_type: DataType,
        volatility: Volatility,
    ) -> Self {
        let name = name.into();
        let signature = Signature::exact(input_types, volatility);
        Self {
            name,
            evaluator,
            signature,
            return_type,
        }
    }

    /// 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<PyAny> {
        &self.evaluator
    }

    pub(crate) fn return_type(&self) -> &DataType {
        &self.return_type
    }
}

impl Eq for PythonFunctionWindowUDF {}
impl PartialEq for PythonFunctionWindowUDF {
    fn eq(&self, other: &Self) -> bool {
        self.name == other.name
            && self.signature == other.signature
            && self.return_type == other.return_type
            // 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| {
                    // 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_else(|e| {
                            log::debug!(
                                target: "datafusion_python::udwf",
                                "PythonFunctionWindowUDF {:?} __eq__ raised; treating as unequal: {e}",
                                self.name,
                            );
                            false
                        })
                }))
    }
}

impl std::hash::Hash for PythonFunctionWindowUDF {
    fn hash<H: std::hash::Hasher>(&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);
    }
}

impl WindowUDFImpl for PythonFunctionWindowUDF {
    fn name(&self) -> &str {
        &self.name
    }

    fn signature(&self) -> &Signature {
        &self.signature
    }

    fn field(&self, field_args: WindowUDFFieldArgs) -> Result<arrow::datatypes::FieldRef> {
        // TODO: Should nullable always be `true`?
        Ok(arrow::datatypes::Field::new(field_args.name(), self.return_type.clone(), true).into())
    }

    // TODO: Enable passing partition_evaluator_args to python?
    fn partition_evaluator(
        &self,
        _partition_evaluator_args: PartitionEvaluatorArgs,
    ) -> Result<Box<dyn PartitionEvaluator>> {
        instantiate_partition_evaluator(&self.evaluator)
    }
}