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Copy pathudaf.rs
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217 lines (191 loc) · 7.04 KB
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// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License. You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the License for the
// specific language governing permissions and limitations
// under the License.
use std::sync::Arc;
use pyo3::{prelude::*, types::PyTuple};
use crate::common::data_type::PyScalarValue;
use crate::errors::{py_datafusion_err, to_datafusion_err, PyDataFusionResult};
use crate::expr::PyExpr;
use crate::utils::{parse_volatility, validate_pycapsule};
use datafusion::arrow::array::{Array, ArrayRef};
use datafusion::arrow::datatypes::DataType;
use datafusion::arrow::pyarrow::{PyArrowType, ToPyArrow};
use datafusion::common::ScalarValue;
use datafusion::error::{DataFusionError, Result};
use datafusion::logical_expr::{
create_udaf, Accumulator, AccumulatorFactoryFunction, AggregateUDF,
};
use datafusion_ffi::udaf::{FFI_AggregateUDF, ForeignAggregateUDF};
use pyo3::types::PyCapsule;
#[derive(Debug)]
struct RustAccumulator {
accum: PyObject,
}
impl RustAccumulator {
fn new(accum: PyObject) -> Self {
Self { accum }
}
}
impl Accumulator for RustAccumulator {
fn state(&mut self) -> Result<Vec<ScalarValue>> {
Python::with_gil(|py| {
self.accum
.bind(py)
.call_method0("state")?
.extract::<Vec<PyScalarValue>>()
})
.map(|v| v.into_iter().map(|x| x.0).collect())
.map_err(|e| DataFusionError::Execution(format!("{e}")))
}
fn evaluate(&mut self) -> Result<ScalarValue> {
Python::with_gil(|py| {
self.accum
.bind(py)
.call_method0("evaluate")?
.extract::<PyScalarValue>()
})
.map(|v| v.0)
.map_err(|e| DataFusionError::Execution(format!("{e}")))
}
fn update_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
Python::with_gil(|py| {
// 1. cast args to Pyarrow array
let py_args = values
.iter()
.map(|arg| arg.into_data().to_pyarrow(py).unwrap())
.collect::<Vec<_>>();
let py_args = PyTuple::new(py, py_args).map_err(to_datafusion_err)?;
// 2. call function
self.accum
.bind(py)
.call_method1("update", py_args)
.map_err(|e| DataFusionError::Execution(format!("{e}")))?;
Ok(())
})
}
fn merge_batch(&mut self, states: &[ArrayRef]) -> Result<()> {
Python::with_gil(|py| {
// // 1. cast states to Pyarrow arrays
let py_states: Result<Vec<PyObject>> = states
.iter()
.map(|state| {
state
.into_data()
.to_pyarrow(py)
.map_err(|e| DataFusionError::Execution(format!("{e}")))
})
.collect();
// 2. call merge
self.accum
.bind(py)
.call_method1("merge", (py_states?,))
.map_err(|e| DataFusionError::Execution(format!("{e}")))?;
Ok(())
})
}
fn size(&self) -> usize {
std::mem::size_of_val(self)
}
fn retract_batch(&mut self, values: &[ArrayRef]) -> Result<()> {
Python::with_gil(|py| {
// 1. cast args to Pyarrow array
let py_args = values
.iter()
.map(|arg| arg.into_data().to_pyarrow(py).unwrap())
.collect::<Vec<_>>();
let py_args = PyTuple::new(py, py_args).map_err(to_datafusion_err)?;
// 2. call function
self.accum
.bind(py)
.call_method1("retract_batch", py_args)
.map_err(|e| DataFusionError::Execution(format!("{e}")))?;
Ok(())
})
}
fn supports_retract_batch(&self) -> bool {
Python::with_gil(
|py| match self.accum.bind(py).call_method0("supports_retract_batch") {
Ok(x) => x.extract().unwrap_or(false),
Err(_) => false,
},
)
}
}
pub fn to_rust_accumulator(accum: PyObject) -> AccumulatorFactoryFunction {
Arc::new(move |_| -> Result<Box<dyn Accumulator>> {
let accum = Python::with_gil(|py| {
accum
.call0(py)
.map_err(|e| DataFusionError::Execution(format!("{e}")))
})?;
Ok(Box::new(RustAccumulator::new(accum)))
})
}
/// Represents an AggregateUDF
#[pyclass(name = "AggregateUDF", module = "datafusion", subclass)]
#[derive(Debug, Clone)]
pub struct PyAggregateUDF {
pub(crate) function: AggregateUDF,
}
#[pymethods]
impl PyAggregateUDF {
#[new]
#[pyo3(signature=(name, accumulator, input_type, return_type, state_type, volatility))]
fn new(
name: &str,
accumulator: PyObject,
input_type: PyArrowType<Vec<DataType>>,
return_type: PyArrowType<DataType>,
state_type: PyArrowType<Vec<DataType>>,
volatility: &str,
) -> PyResult<Self> {
let function = create_udaf(
name,
input_type.0,
Arc::new(return_type.0),
parse_volatility(volatility)?,
to_rust_accumulator(accumulator),
Arc::new(state_type.0),
);
Ok(Self { function })
}
#[staticmethod]
pub fn from_pycapsule(func: Bound<'_, PyAny>) -> PyDataFusionResult<Self> {
if func.hasattr("__datafusion_aggregate_udf__")? {
let capsule = func.getattr("__datafusion_aggregate_udf__")?.call0()?;
let capsule = capsule.downcast::<PyCapsule>().map_err(py_datafusion_err)?;
validate_pycapsule(capsule, "datafusion_aggregate_udf")?;
let udaf = unsafe { capsule.reference::<FFI_AggregateUDF>() };
let udaf: ForeignAggregateUDF = udaf.try_into()?;
Ok(Self {
function: udaf.into(),
})
} else {
Err(crate::errors::PyDataFusionError::Common(
"__datafusion_aggregate_udf__ does not exist on AggregateUDF object.".to_string(),
))
}
}
/// 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())
}
fn __repr__(&self) -> PyResult<String> {
Ok(format!("AggregateUDF({})", self.function.name()))
}
}