datafusion-physical-expr 32.0.0

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//
//   http://www.apache.org/licenses/LICENSE-2.0
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//! Interval and selectivity in [`AnalysisContext`]

use crate::expressions::Column;
use crate::intervals::cp_solver::PropagationResult;
use crate::intervals::{cardinality_ratio, ExprIntervalGraph, Interval, IntervalBound};
use crate::utils::collect_columns;
use crate::PhysicalExpr;

use arrow::datatypes::Schema;
use datafusion_common::{
    internal_err, ColumnStatistics, DataFusionError, Result, ScalarValue,
};

use std::fmt::Debug;
use std::sync::Arc;

/// The shared context used during the analysis of an expression. Includes
/// the boundaries for all known columns.
#[derive(Clone, Debug, PartialEq)]
pub struct AnalysisContext {
    // A list of known column boundaries, ordered by the index
    // of the column in the current schema.
    pub boundaries: Option<Vec<ExprBoundaries>>,
    /// The estimated percentage of rows that this expression would select, if
    /// it were to be used as a boolean predicate on a filter. The value will be
    /// between 0.0 (selects nothing) and 1.0 (selects everything).
    pub selectivity: Option<f64>,
}

impl AnalysisContext {
    pub fn new(boundaries: Vec<ExprBoundaries>) -> Self {
        Self {
            boundaries: Some(boundaries),
            selectivity: None,
        }
    }

    pub fn with_selectivity(mut self, selectivity: f64) -> Self {
        self.selectivity = Some(selectivity);
        self
    }

    /// Create a new analysis context from column statistics.
    pub fn from_statistics(
        input_schema: &Schema,
        statistics: &[ColumnStatistics],
    ) -> Self {
        let mut column_boundaries = vec![];
        for (idx, stats) in statistics.iter().enumerate() {
            column_boundaries.push(ExprBoundaries::from_column(
                stats,
                input_schema.fields()[idx].name().clone(),
                idx,
            ));
        }
        Self::new(column_boundaries)
    }
}

/// Represents the boundaries of the resulting value from a physical expression,
/// if it were to be an expression, if it were to be evaluated.
#[derive(Clone, Debug, PartialEq)]
pub struct ExprBoundaries {
    pub column: Column,
    /// Minimum and maximum values this expression can have.
    pub interval: Interval,
    /// Maximum number of distinct values this expression can produce, if known.
    pub distinct_count: Option<usize>,
}

impl ExprBoundaries {
    /// Create a new `ExprBoundaries` object from column level statistics.
    pub fn from_column(stats: &ColumnStatistics, col: String, index: usize) -> Self {
        Self {
            column: Column::new(&col, index),
            interval: Interval::new(
                IntervalBound::new_closed(
                    stats.min_value.clone().unwrap_or(ScalarValue::Null),
                ),
                IntervalBound::new_closed(
                    stats.max_value.clone().unwrap_or(ScalarValue::Null),
                ),
            ),
            distinct_count: stats.distinct_count,
        }
    }
}

/// Attempts to refine column boundaries and compute a selectivity value.
///
/// The function accepts boundaries of the input columns in the `context` parameter.
/// It then tries to tighten these boundaries based on the provided `expr`.
/// The resulting selectivity value is calculated by comparing the initial and final boundaries.
/// The computation assumes that the data within the column is uniformly distributed and not sorted.
///
/// # Arguments
///
/// * `context` - The context holding input column boundaries.
/// * `expr` - The expression used to shrink the column boundaries.
///
/// # Returns
///
/// * `AnalysisContext` constructed by pruned boundaries and a selectivity value.
pub fn analyze(
    expr: &Arc<dyn PhysicalExpr>,
    context: AnalysisContext,
) -> Result<AnalysisContext> {
    let target_boundaries = context.boundaries.ok_or_else(|| {
        DataFusionError::Internal("No column exists at the input to filter".to_string())
    })?;

    let mut graph = ExprIntervalGraph::try_new(expr.clone())?;

    let columns: Vec<Arc<dyn PhysicalExpr>> = collect_columns(expr)
        .into_iter()
        .map(|c| Arc::new(c) as Arc<dyn PhysicalExpr>)
        .collect();

    let target_expr_and_indices: Vec<(Arc<dyn PhysicalExpr>, usize)> =
        graph.gather_node_indices(columns.as_slice());

    let mut target_indices_and_boundaries: Vec<(usize, Interval)> =
        target_expr_and_indices
            .iter()
            .filter_map(|(expr, i)| {
                target_boundaries.iter().find_map(|bound| {
                    expr.as_any()
                        .downcast_ref::<Column>()
                        .filter(|expr_column| bound.column.eq(*expr_column))
                        .map(|_| (*i, bound.interval.clone()))
                })
            })
            .collect();

    match graph.update_ranges(&mut target_indices_and_boundaries)? {
        PropagationResult::Success => {
            shrink_boundaries(expr, graph, target_boundaries, target_expr_and_indices)
        }
        PropagationResult::Infeasible => {
            Ok(AnalysisContext::new(target_boundaries).with_selectivity(0.0))
        }
        PropagationResult::CannotPropagate => {
            Ok(AnalysisContext::new(target_boundaries).with_selectivity(1.0))
        }
    }
}

/// If the `PropagationResult` indicates success, this function calculates the
/// selectivity value by comparing the initial and final column boundaries.
/// Following this, it constructs and returns a new `AnalysisContext` with the
/// updated parameters.
fn shrink_boundaries(
    expr: &Arc<dyn PhysicalExpr>,
    mut graph: ExprIntervalGraph,
    mut target_boundaries: Vec<ExprBoundaries>,
    target_expr_and_indices: Vec<(Arc<dyn PhysicalExpr>, usize)>,
) -> Result<AnalysisContext> {
    let initial_boundaries = target_boundaries.clone();
    target_expr_and_indices.iter().for_each(|(expr, i)| {
        if let Some(column) = expr.as_any().downcast_ref::<Column>() {
            if let Some(bound) = target_boundaries
                .iter_mut()
                .find(|bound| bound.column.eq(column))
            {
                bound.interval = graph.get_interval(*i);
            };
        }
    });
    let graph_nodes = graph.gather_node_indices(&[expr.clone()]);
    let (_, root_index) = graph_nodes.first().ok_or_else(|| {
        DataFusionError::Internal("Error in constructing predicate graph".to_string())
    })?;
    let final_result = graph.get_interval(*root_index);

    // If during selectivity calculation we encounter an error, use 1.0 as cardinality estimate
    // safest estimate(e.q largest possible value).
    let selectivity = calculate_selectivity(
        &final_result.lower.value,
        &final_result.upper.value,
        &target_boundaries,
        &initial_boundaries,
    )
    .unwrap_or(1.0);

    if !(0.0..=1.0).contains(&selectivity) {
        return internal_err!("Selectivity is out of limit: {}", selectivity);
    }

    Ok(AnalysisContext::new(target_boundaries).with_selectivity(selectivity))
}

/// This function calculates the filter predicate's selectivity by comparing
/// the initial and pruned column boundaries. Selectivity is defined as the
/// ratio of rows in a table that satisfy the filter's predicate.
///
/// An exact propagation result at the root, i.e. `[true, true]` or `[false, false]`,
/// leads to early exit (returning a selectivity value of either 1.0 or 0.0). In such
/// a case, `[true, true]` indicates that all data values satisfy the predicate (hence,
/// selectivity is 1.0), and `[false, false]` suggests that no data value meets the
/// predicate (therefore, selectivity is 0.0).
fn calculate_selectivity(
    lower_value: &ScalarValue,
    upper_value: &ScalarValue,
    target_boundaries: &[ExprBoundaries],
    initial_boundaries: &[ExprBoundaries],
) -> Result<f64> {
    match (lower_value, upper_value) {
        (ScalarValue::Boolean(Some(true)), ScalarValue::Boolean(Some(true))) => Ok(1.0),
        (ScalarValue::Boolean(Some(false)), ScalarValue::Boolean(Some(false))) => Ok(0.0),
        _ => {
            // Since the intervals are assumed uniform and the values
            // are not correlated, we need to multiply the selectivities
            // of multiple columns to get the overall selectivity.
            target_boundaries.iter().enumerate().try_fold(
                1.0,
                |acc, (i, ExprBoundaries { interval, .. })| {
                    let temp =
                        cardinality_ratio(&initial_boundaries[i].interval, interval)?;
                    Ok(acc * temp)
                },
            )
        }
    }
}