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Copy pathdataset.rs
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133 lines (121 loc) · 4.79 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 datafusion::catalog::Session;
use pyo3::exceptions::PyValueError;
/// Implements a Datafusion TableProvider that delegates to a PyArrow Dataset
/// This allows us to use PyArrow Datasets as Datafusion tables while pushing down projections and filters
use pyo3::prelude::*;
use pyo3::types::PyType;
use std::any::Any;
use std::sync::Arc;
use async_trait::async_trait;
use datafusion::arrow::datatypes::SchemaRef;
use datafusion::arrow::pyarrow::PyArrowType;
use datafusion::datasource::{TableProvider, TableType};
use datafusion::error::{DataFusionError, Result as DFResult};
use datafusion::logical_expr::Expr;
use datafusion::logical_expr::TableProviderFilterPushDown;
use datafusion::physical_plan::ExecutionPlan;
use crate::dataset_exec::DatasetExec;
use crate::pyarrow_filter_expression::PyArrowFilterExpression;
// Wraps a pyarrow.dataset.Dataset class and implements a Datafusion TableProvider around it
#[derive(Debug)]
pub(crate) struct Dataset {
dataset: PyObject,
}
impl Dataset {
// Creates a Python PyArrow.Dataset
pub fn new(dataset: &Bound<'_, PyAny>, py: Python) -> PyResult<Self> {
// Ensure that we were passed an instance of pyarrow.dataset.Dataset
let ds = PyModule::import(py, "pyarrow.dataset")?;
let ds_attr = ds.getattr("Dataset")?;
let ds_type = ds_attr.downcast::<PyType>()?;
if dataset.is_instance(ds_type)? {
Ok(Dataset {
dataset: dataset.clone().unbind(),
})
} else {
Err(PyValueError::new_err(
"dataset argument must be a pyarrow.dataset.Dataset object",
))
}
}
}
#[async_trait]
impl TableProvider for Dataset {
/// Returns the table provider as [`Any`](std::any::Any) so that it can be
/// downcast to a specific implementation.
fn as_any(&self) -> &dyn Any {
self
}
/// Get a reference to the schema for this table
fn schema(&self) -> SchemaRef {
Python::with_gil(|py| {
let dataset = self.dataset.bind(py);
// This can panic but since we checked that self.dataset is a pyarrow.dataset.Dataset it should never
Arc::new(
dataset
.getattr("schema")
.unwrap()
.extract::<PyArrowType<_>>()
.unwrap()
.0,
)
})
}
/// Get the type of this table for metadata/catalog purposes.
fn table_type(&self) -> TableType {
TableType::Base
}
/// Create an ExecutionPlan that will scan the table.
/// The table provider will be usually responsible of grouping
/// the source data into partitions that can be efficiently
/// parallelized or distributed.
async fn scan(
&self,
_ctx: &dyn Session,
projection: Option<&Vec<usize>>,
filters: &[Expr],
// limit can be used to reduce the amount scanned
// from the datasource as a performance optimization.
// If set, it contains the amount of rows needed by the `LogicalPlan`,
// The datasource should return *at least* this number of rows if available.
_limit: Option<usize>,
) -> DFResult<Arc<dyn ExecutionPlan>> {
Python::with_gil(|py| {
let plan: Arc<dyn ExecutionPlan> = Arc::new(
DatasetExec::new(py, self.dataset.bind(py), projection.cloned(), filters)
.map_err(|err| DataFusionError::External(Box::new(err)))?,
);
Ok(plan)
})
}
/// Tests whether the table provider can make use of a filter expression
/// to optimise data retrieval.
fn supports_filters_pushdown(
&self,
filter: &[&Expr],
) -> DFResult<Vec<TableProviderFilterPushDown>> {
filter
.iter()
.map(|&f| match PyArrowFilterExpression::try_from(f) {
Ok(_) => Ok(TableProviderFilterPushDown::Exact),
_ => Ok(TableProviderFilterPushDown::Unsupported),
})
.collect()
}
}