polars_io/parquet/read/
predicates.rs

use polars_core::config;
use polars_core::prelude::*;
use polars_parquet::read::statistics::{deserialize, Statistics};
use polars_parquet::read::RowGroupMetadata;

use crate::predicates::{BatchStats, ColumnStats, PhysicalIoExpr};

/// Collect the statistics in a row-group
pub(crate) fn collect_statistics(
    md: &RowGroupMetadata,
    schema: &ArrowSchema,
) -> PolarsResult<Option<BatchStats>> {
    // TODO! fix this performance. This is a full sequential scan.
    let stats = schema
        .iter_values()
        .map(|field| {
            let mut iter = md.columns_under_root_iter(&field.name).unwrap();

            let statistics = deserialize(field, &mut iter)?;
            assert!(iter.next().is_none());

            // We don't support reading nested statistics for now. It does not really make any
            // sense at the moment with how we structure statistics.
            let Some(Statistics::Column(stats)) = statistics else {
                return Ok(ColumnStats::new(field.into(), None, None, None));
            };

            let stats = stats.into_arrow()?;

            let null_count = stats
                .null_count
                .map(|x| Scalar::from(x).into_series(PlSmallStr::EMPTY));
            let min_value = stats
                .min_value
                .map(|x| Series::try_from((PlSmallStr::EMPTY, x)).unwrap());
            let max_value = stats
                .max_value
                .map(|x| Series::try_from((PlSmallStr::EMPTY, x)).unwrap());

            Ok(ColumnStats::new(
                field.into(),
                null_count,
                min_value,
                max_value,
            ))
        })
        .collect::<PolarsResult<Vec<_>>>()?;

    if stats.is_empty() {
        return Ok(None);
    }

    Ok(Some(BatchStats::new(
        Arc::new(Schema::from_arrow_schema(schema)),
        stats,
        Some(md.num_rows()),
    )))
}

pub fn read_this_row_group(
    predicate: Option<&dyn PhysicalIoExpr>,
    md: &RowGroupMetadata,
    schema: &ArrowSchema,
) -> PolarsResult<bool> {
    if std::env::var("POLARS_NO_PARQUET_STATISTICS").is_ok() {
        return Ok(true);
    }

    let mut should_read = true;

    if let Some(pred) = predicate {
        if let Some(pred) = pred.as_stats_evaluator() {
            if let Some(stats) = collect_statistics(md, schema)? {
                let pred_result = pred.should_read(&stats);

                // a parquet file may not have statistics of all columns
                match pred_result {
                    Err(PolarsError::ColumnNotFound(errstr)) => {
                        return Err(PolarsError::ColumnNotFound(errstr))
                    },
                    Ok(false) => should_read = false,
                    _ => {},
                }
            }
        }

        if config::verbose() {
            if should_read {
                eprintln!(
                    "parquet row group must be read, statistics not sufficient for predicate."
                );
            } else {
                eprintln!("parquet row group can be skipped, the statistics were sufficient to apply the predicate.");
            }
        }
    }

    Ok(should_read)
}
polars_io/parquet/read/predicates.rs

polars_io/parquet/read/
predicates.rs
