datafusion-physical-expr 15.0.0

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// to you under the Apache License, Version 2.0 (the
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//
//   http://www.apache.org/licenses/LICENSE-2.0
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//! Physical exec for built-in window function expressions.

use super::window_frame_state::WindowFrameContext;
use super::BuiltInWindowFunctionExpr;
use super::WindowExpr;
use crate::{expressions::PhysicalSortExpr, PhysicalExpr};
use arrow::compute::{concat, SortOptions};
use arrow::record_batch::RecordBatch;
use arrow::{array::ArrayRef, datatypes::Field};
use datafusion_common::DataFusionError;
use datafusion_common::Result;
use datafusion_expr::WindowFrame;
use std::any::Any;
use std::ops::Range;
use std::sync::Arc;

/// A window expr that takes the form of a built in window function
#[derive(Debug)]
pub struct BuiltInWindowExpr {
    expr: Arc<dyn BuiltInWindowFunctionExpr>,
    partition_by: Vec<Arc<dyn PhysicalExpr>>,
    order_by: Vec<PhysicalSortExpr>,
    window_frame: Option<Arc<WindowFrame>>,
}

impl BuiltInWindowExpr {
    /// create a new built-in window function expression
    pub fn new(
        expr: Arc<dyn BuiltInWindowFunctionExpr>,
        partition_by: &[Arc<dyn PhysicalExpr>],
        order_by: &[PhysicalSortExpr],
        window_frame: Option<Arc<WindowFrame>>,
    ) -> Self {
        Self {
            expr,
            partition_by: partition_by.to_vec(),
            order_by: order_by.to_vec(),
            window_frame,
        }
    }
}

impl WindowExpr for BuiltInWindowExpr {
    /// Return a reference to Any that can be used for downcasting
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn name(&self) -> &str {
        self.expr.name()
    }

    fn field(&self) -> Result<Field> {
        self.expr.field()
    }

    fn expressions(&self) -> Vec<Arc<dyn PhysicalExpr>> {
        self.expr.expressions()
    }

    fn partition_by(&self) -> &[Arc<dyn PhysicalExpr>] {
        &self.partition_by
    }

    fn order_by(&self) -> &[PhysicalSortExpr] {
        &self.order_by
    }

    fn evaluate(&self, batch: &RecordBatch) -> Result<ArrayRef> {
        let evaluator = self.expr.create_evaluator()?;
        let num_rows = batch.num_rows();
        let partition_columns = self.partition_columns(batch)?;
        let partition_points =
            self.evaluate_partition_points(num_rows, &partition_columns)?;

        let results = if evaluator.uses_window_frame() {
            let sort_options: Vec<SortOptions> =
                self.order_by.iter().map(|o| o.options).collect();
            let (_, order_bys) = self.get_values_orderbys(batch)?;
            let window_frame = if !order_bys.is_empty() && self.window_frame.is_none() {
                // OVER (ORDER BY a) case
                // We create an implicit window for ORDER BY.
                Some(Arc::new(WindowFrame::default()))
            } else {
                self.window_frame.clone()
            };
            let mut row_wise_results = vec![];
            for partition_range in &partition_points {
                let length = partition_range.end - partition_range.start;
                let (values, order_bys) = self
                    .get_values_orderbys(&batch.slice(partition_range.start, length))?;
                let mut window_frame_ctx = WindowFrameContext::new(&window_frame);
                // We iterate on each row to calculate window frame range and and window function result
                for idx in 0..length {
                    let range = window_frame_ctx.calculate_range(
                        &order_bys,
                        &sort_options,
                        num_rows,
                        idx,
                    )?;
                    let range = Range {
                        start: range.0,
                        end: range.1,
                    };
                    let value = evaluator.evaluate_inside_range(&values, range)?;
                    row_wise_results.push(value.to_array());
                }
            }
            row_wise_results
        } else if evaluator.include_rank() {
            let columns = self.sort_columns(batch)?;
            let sort_partition_points =
                self.evaluate_partition_points(num_rows, &columns)?;
            evaluator.evaluate_with_rank(partition_points, sort_partition_points)?
        } else {
            let (values, _) = self.get_values_orderbys(batch)?;
            evaluator.evaluate(&values, partition_points)?
        };
        let results = results.iter().map(|i| i.as_ref()).collect::<Vec<_>>();
        concat(&results).map_err(DataFusionError::ArrowError)
    }
}