Prune Parquet RowGroup in a single call to PruningPredicate::prune, update StatisticsExtractor API

datafusion-examples/examples/parquet_index.rs

@@ -25,7 +25,7 @@ use arrow_schema::SchemaRef;
 use async_trait::async_trait;
 use datafusion::datasource::listing::PartitionedFile;
 use datafusion::datasource::physical_plan::{
-    parquet::{RequestedStatistics, StatisticsConverter},
+    parquet::StatisticsConverter,
     {FileScanConfig, ParquetExec},
 };
 use datafusion::datasource::TableProvider;
@@ -518,21 +518,17 @@ impl ParquetMetadataIndexBuilder {
 
         // extract the parquet statistics from the file's footer
         let metadata = reader.metadata();
+        let row_groups = metadata.row_groups();
 
         // Extract the min/max values for each row group from the statistics
-        let row_counts = StatisticsConverter::row_counts(reader.metadata())?;
-        let value_column_mins = StatisticsConverter::try_new(
+        let converter = StatisticsConverter::try_new(
             "value",
-            RequestedStatistics::Min,
             reader.schema(),
-        )?
-        .extract(reader.metadata())?;
-        let value_column_maxes = StatisticsConverter::try_new(
-            "value",
-            RequestedStatistics::Max,
-            reader.schema(),
-        )?
-        .extract(reader.metadata())?;
+            reader.parquet_schema(),
+        )?;
+        let row_counts = StatisticsConverter::row_group_row_counts(row_groups.iter())?;
+        let value_column_mins = converter.row_group_mins(row_groups.iter())?;
+        let value_column_maxes = converter.row_group_maxes(row_groups.iter())?;
 
         // In a real system you would have to handle nulls, which represent
         // unknown statistics. All statistics are known in this example

datafusion/core/benches/parquet_statistic.rs

@@ -24,9 +24,7 @@ use arrow_schema::{
     Field, Schema,
 };
 use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
-use datafusion::datasource::physical_plan::parquet::{
-    RequestedStatistics, StatisticsConverter,
-};
+use datafusion::datasource::physical_plan::parquet::StatisticsConverter;
 use parquet::arrow::{arrow_reader::ArrowReaderBuilder, ArrowWriter};
 use parquet::file::properties::WriterProperties;
 use std::sync::Arc;
@@ -159,41 +157,26 @@ fn criterion_benchmark(c: &mut Criterion) {
         let file = file.reopen().unwrap();
         let reader = ArrowReaderBuilder::try_new(file).unwrap();
         let metadata = reader.metadata();
+        let row_groups = metadata.row_groups();
 
         let mut group =
             c.benchmark_group(format!("Extract statistics for {}", dtype.clone()));
         group.bench_function(
             BenchmarkId::new("extract_statistics", dtype.clone()),
             |b| {
                 b.iter(|| {
-                    let _ = StatisticsConverter::try_new(
-                        "col",
-                        RequestedStatistics::Min,
-                        reader.schema(),
-                    )
-                    .unwrap()
-                    .extract(metadata)
-                    .unwrap();
-
-                    let _ = StatisticsConverter::try_new(
-                        "col",
-                        RequestedStatistics::Max,
-                        reader.schema(),
-                    )
-                    .unwrap()
-                    .extract(reader.metadata())
-                    .unwrap();
-
-                    let _ = StatisticsConverter::try_new(
+                    let converter = StatisticsConverter::try_new(
                         "col",
-                        RequestedStatistics::NullCount,
                         reader.schema(),
+                        reader.parquet_schema(),
                     )
-                    .unwrap()
-                    .extract(reader.metadata())
                     .unwrap();
 
-                    let _ = StatisticsConverter::row_counts(reader.metadata()).unwrap();
+                    let _ = converter.row_group_mins(row_groups.iter()).unwrap();
+                    let _ = converter.row_group_maxes(row_groups.iter()).unwrap();
+                    let _ = converter.row_group_null_counts(row_groups.iter()).unwrap();
+                    let _ = StatisticsConverter::row_group_row_counts(row_groups.iter())
+                        .unwrap();
                 })
             },
         );

datafusion/core/src/datasource/physical_plan/parquet/metrics.rs

@@ -29,11 +29,11 @@ use crate::physical_plan::metrics::{
 pub struct ParquetFileMetrics {
     /// Number of times the predicate could not be evaluated
     pub predicate_evaluation_errors: Count,
-    /// Number of row groups whose bloom filters were checked and matched
+    /// Number of row groups whose bloom filters were checked and matched (not pruned)
     pub row_groups_matched_bloom_filter: Count,
     /// Number of row groups pruned by bloom filters
     pub row_groups_pruned_bloom_filter: Count,
-    /// Number of row groups whose statistics were checked and matched
+    /// Number of row groups whose statistics were checked and matched (not pruned)
     pub row_groups_matched_statistics: Count,
     /// Number of row groups pruned by statistics
     pub row_groups_pruned_statistics: Count,

datafusion/core/src/datasource/physical_plan/parquet/mod.rs

@@ -62,7 +62,7 @@ use crate::datasource::schema_adapter::{
 pub use metrics::ParquetFileMetrics;
 use opener::ParquetOpener;
 pub use reader::{DefaultParquetFileReaderFactory, ParquetFileReaderFactory};
-pub use statistics::{RequestedStatistics, StatisticsConverter};
+pub use statistics::StatisticsConverter;
 pub use writer::plan_to_parquet;
 
 /// Execution plan for reading one or more Parquet files.

datafusion/core/src/datasource/physical_plan/parquet/row_groups.rs

@@ -17,26 +17,23 @@
 
 use arrow::{array::ArrayRef, datatypes::Schema};
 use arrow_array::BooleanArray;
-use arrow_schema::FieldRef;
-use datafusion_common::{Column, ScalarValue};
+use datafusion_common::{Column, Result, ScalarValue};
 use parquet::basic::Type;
 use parquet::data_type::Decimal;
-use parquet::file::metadata::ColumnChunkMetaData;
 use parquet::schema::types::SchemaDescriptor;
 use parquet::{
     arrow::{async_reader::AsyncFileReader, ParquetRecordBatchStreamBuilder},
     bloom_filter::Sbbf,
     file::metadata::RowGroupMetaData,
 };
 use std::collections::{HashMap, HashSet};
+use std::sync::Arc;
 
 use crate::datasource::listing::FileRange;
-use crate::datasource::physical_plan::parquet::statistics::{
-    max_statistics, min_statistics, parquet_column,
-};
+use crate::datasource::physical_plan::parquet::statistics::parquet_column;
 use crate::physical_optimizer::pruning::{PruningPredicate, PruningStatistics};
 
-use super::ParquetFileMetrics;
+use super::{ParquetFileMetrics, StatisticsConverter};
 
 /// Tracks which RowGroups within a parquet file should be scanned.
 ///
@@ -136,32 +133,35 @@ impl RowGroupSet {
         metrics: &ParquetFileMetrics,
     ) {
         assert_eq!(groups.len(), self.len());
-        for (idx, metadata) in groups.iter().enumerate() {
-            if !self.should_scan(idx) {
-                continue;
-            }
-            let pruning_stats = RowGroupPruningStatistics {
-                parquet_schema,
-                row_group_metadata: metadata,
-                arrow_schema,
-            };
-            match predicate.prune(&pruning_stats) {
-                Ok(values) => {
-                    // NB: false means don't scan row group
-                    if !values[0] {
+        // Indexes of row groups still to scan
+        let indexes = self.indexes();
+        let row_group_metadatas = indexes.iter().map(|&i| &groups[i]).collect::<Vec<_>>();
+
+        let pruning_stats = RowGroupPruningStatistics {
+            parquet_schema,
+            row_group_metadatas,
+            arrow_schema,
+        };
+
+        // try to prune the row groups
+        match predicate.prune(&pruning_stats) {
+            Ok(values) => {
+                // values[i] is false means the predicate could not be true for row group i
+                for (idx, &value) in indexes.iter().zip(values.iter()) {
+                    if !value {
+                        self.do_not_scan(*idx);
                         metrics.row_groups_pruned_statistics.add(1);
-                        self.do_not_scan(idx);
-                        continue;
+                    } else {
+                        metrics.row_groups_matched_statistics.add(1);
                     }
                 }
-                // stats filter array could not be built
-                // don't prune this row group
-                Err(e) => {
-                    log::debug!("Error evaluating row group predicate values {e}");
-                    metrics.predicate_evaluation_errors.add(1);
-                }
             }
-            metrics.row_groups_matched_statistics.add(1);
+            // stats filter array could not be built
+            // Can't prune.
+            Err(e) => {
+                log::debug!("Error evaluating row group predicate values {e}");
+                metrics.predicate_evaluation_errors.add(1);
+            }
         }
     }
 
@@ -360,49 +360,55 @@ impl PruningStatistics for BloomFilterStatistics {
     }
 }
 
-/// Wraps [`RowGroupMetaData`] in a way that implements [`PruningStatistics`]
-///
-/// Note: This should be implemented for an array of [`RowGroupMetaData`] instead
-/// of per row-group
+/// Wraps a slice of [`RowGroupMetaData`] in a way that implements [`PruningStatistics`]
 struct RowGroupPruningStatistics<'a> {
     parquet_schema: &'a SchemaDescriptor,
-    row_group_metadata: &'a RowGroupMetaData,
+    row_group_metadatas: Vec<&'a RowGroupMetaData>,
     arrow_schema: &'a Schema,
 }
 
 impl<'a> RowGroupPruningStatistics<'a> {
-    /// Lookups up the parquet column by name
-    fn column(&self, name: &str) -> Option<(&ColumnChunkMetaData, &FieldRef)> {
-        let (idx, field) = parquet_column(self.parquet_schema, self.arrow_schema, name)?;
-        Some((self.row_group_metadata.column(idx), field))
+    /// Return an iterator over the row group metadata
+    fn metadata_iter(&'a self) -> impl Iterator<Item = &'a RowGroupMetaData> + 'a {
+        self.row_group_metadatas.iter().copied()
+    }
+
+    fn statistics_converter<'b>(
+        &'a self,
+        column: &'b Column,
+    ) -> Result<StatisticsConverter<'a>> {
+        StatisticsConverter::try_new(&column.name, self.arrow_schema, self.parquet_schema)
     }
 }
 
 impl<'a> PruningStatistics for RowGroupPruningStatistics<'a> {
     fn min_values(&self, column: &Column) -> Option<ArrayRef> {
-        let (column, field) = self.column(&column.name)?;
-        min_statistics(field.data_type(), std::iter::once(column.statistics())).ok()
+        self.statistics_converter(column)
+            .and_then(|c| c.row_group_mins(self.metadata_iter()))
+            .ok()
     }
 
     fn max_values(&self, column: &Column) -> Option<ArrayRef> {
-        let (column, field) = self.column(&column.name)?;
-        max_statistics(field.data_type(), std::iter::once(column.statistics())).ok()
+        self.statistics_converter(column)
+            .and_then(|c| c.row_group_maxes(self.metadata_iter()))
+            .ok()
     }
 
     fn num_containers(&self) -> usize {
-        1
+        self.row_group_metadatas.len()
     }
 
     fn null_counts(&self, column: &Column) -> Option<ArrayRef> {
-        let (c, _) = self.column(&column.name)?;
-        let scalar = ScalarValue::UInt64(Some(c.statistics()?.null_count()));
-        scalar.to_array().ok()
+        self.statistics_converter(column)
+            .and_then(|c| c.row_group_null_counts(self.metadata_iter()))
+            .ok()
     }
 
-    fn row_counts(&self, column: &Column) -> Option<ArrayRef> {
-        let (c, _) = self.column(&column.name)?;
-        let scalar = ScalarValue::UInt64(Some(c.num_values() as u64));
-        scalar.to_array().ok()
+    fn row_counts(&self, _column: &Column) -> Option<ArrayRef> {
+        // row counts are the same for all columns in a row group
+        StatisticsConverter::row_group_row_counts(self.metadata_iter())
+            .ok()
+            .map(|counts| Arc::new(counts) as ArrayRef)
     }
 
     fn contained(
@@ -429,6 +435,7 @@ mod tests {
     use parquet::arrow::async_reader::ParquetObjectReader;
     use parquet::basic::LogicalType;
     use parquet::data_type::{ByteArray, FixedLenByteArray};
+    use parquet::file::metadata::ColumnChunkMetaData;
     use parquet::{
         basic::Type as PhysicalType, file::statistics::Statistics as ParquetStatistics,
         schema::types::SchemaDescPtr,