fix: respect inexact flags in row group metadata

datafusion/core/src/datasource/file_format/parquet.rs

@@ -603,11 +603,23 @@ mod tests {
         let dic_array = DictionaryArray::<Int32Type>::try_new(keys, Arc::new(values))?;
         let c_dic: ArrayRef = Arc::new(dic_array);
 
-        let batch1 = RecordBatch::try_from_iter(vec![("c_dic", c_dic)])?;
+        // Data for column string_truncation: ["a".repeat(128), null, null, null, null]
+        let string_truncation: ArrayRef = Arc::new(StringArray::from(vec![
+            Some("a".repeat(128)),
+            None,
+            Some("b".repeat(128)),
+            None,
+        ]));
+
+        let batch1 = RecordBatch::try_from_iter(vec![
+            ("c_dic", c_dic),
+            ("string_truncation", string_truncation),
+        ])?;
 
         // Use store_parquet to write each batch to its own file
         // . batch1 written into first file and includes:
         //    - column c_dic that has 4 rows with no null. Stats min and max of dictionary column is available.
+        //    - column string_truncation that has 4 rows with 3 nulls. Stats min and max of string column is available but not exact.
         let store = Arc::new(RequestCountingObjectStore::new(Arc::new(
             LocalFileSystem::new(),
         )));
@@ -647,6 +659,19 @@ mod tests {
             Precision::Exact(Utf8(Some("a".into())))
         );
 
+        // column string_truncation
+        let string_truncation_stats = &stats.column_statistics[1];
+
+        assert_eq!(string_truncation_stats.null_count, Precision::Exact(2));
+        assert_eq!(
+            string_truncation_stats.max_value,
+            Precision::Inexact(ScalarValue::Utf8View(Some("b".repeat(63) + "c")))
+        );
+        assert_eq!(
+            string_truncation_stats.min_value,
+            Precision::Inexact(ScalarValue::Utf8View(Some("a".repeat(64))))
+        );
+
         Ok(())
     }
 

datafusion/datasource-parquet/src/file_format.rs

@@ -19,13 +19,13 @@
 
 use std::any::Any;
 use std::cell::RefCell;
-use std::fmt;
 use std::fmt::Debug;
 use std::ops::Range;
 use std::rc::Rc;
 use std::sync::Arc;
+use std::{fmt, vec};
 
-use arrow::array::RecordBatch;
+use arrow::array::{ArrayRef, BooleanArray, RecordBatch};
 use arrow::datatypes::{Fields, Schema, SchemaRef, TimeUnit};
 use datafusion_datasource::file_compression_type::FileCompressionType;
 use datafusion_datasource::file_sink_config::{FileSink, FileSinkConfig};
@@ -36,7 +36,8 @@ use datafusion_datasource::write::{
 use datafusion_datasource::file_format::{FileFormat, FileFormatFactory};
 use datafusion_datasource::write::demux::DemuxedStreamReceiver;
 
-use arrow::compute::sum;
+use arrow::compute::kernels::cmp::eq;
+use arrow::compute::{and, sum};
 use arrow::datatypes::{DataType, Field, FieldRef};
 use datafusion_common::config::{ConfigField, ConfigFileType, TableParquetOptions};
 #[cfg(feature = "parquet_encryption")]
@@ -46,7 +47,7 @@ use datafusion_common::parsers::CompressionTypeVariant;
 use datafusion_common::stats::Precision;
 use datafusion_common::{
     internal_datafusion_err, internal_err, not_impl_err, ColumnStatistics,
-    DataFusionError, GetExt, HashSet, Result, DEFAULT_PARQUET_EXTENSION,
+    DataFusionError, GetExt, HashSet, Result, ScalarValue, DEFAULT_PARQUET_EXTENSION,
 };
 use datafusion_common::{HashMap, Statistics};
 use datafusion_common_runtime::{JoinSet, SpawnedTask};
@@ -1170,7 +1171,8 @@ pub async fn fetch_statistics(
 /// # When only some columns have statistics:
 ///
 /// For columns with statistics:
-/// - Min/max values are properly extracted and represented as Precision::Exact
+/// - Min/max values are properly extracted and represented as [Precision::Exact] or [Precision::Inexact]
+///   depending on the `is_max_value_exact` and `is_min_value_exact` flags.
 /// - Null counts are calculated by summing across row groups
 ///
 /// For columns without statistics,
@@ -1216,6 +1218,8 @@ pub fn statistics_from_parquet_meta_calc(
         let (mut max_accs, mut min_accs) = create_max_min_accs(&table_schema);
         let mut null_counts_array =
             vec![Precision::Exact(0); table_schema.fields().len()];
+        let mut is_max_value_exact = vec![Some(true); table_schema.fields().len()];
+        let mut is_min_value_exact = vec![Some(true); table_schema.fields().len()];
 
         table_schema
             .fields()
@@ -1232,6 +1236,8 @@ pub fn statistics_from_parquet_meta_calc(
                             &mut min_accs,
                             &mut max_accs,
                             &mut null_counts_array,
+                            &mut is_min_value_exact,
+                            &mut is_max_value_exact,
                             idx,
                             num_rows,
                             &stats_converter,
@@ -1251,6 +1257,8 @@ pub fn statistics_from_parquet_meta_calc(
             null_counts_array,
             &mut max_accs,
             &mut min_accs,
+            &mut is_max_value_exact,
+            &mut is_min_value_exact,
         )
     } else {
         Statistics::unknown_column(&table_schema)
@@ -1264,21 +1272,39 @@ fn get_col_stats(
     null_counts: Vec<Precision<usize>>,
     max_values: &mut [Option<MaxAccumulator>],
     min_values: &mut [Option<MinAccumulator>],
+    is_max_value_exact: &mut [Option<bool>],
+    is_min_value_exact: &mut [Option<bool>],
 ) -> Vec<ColumnStatistics> {
     (0..schema.fields().len())
         .map(|i| {
-            let max_value = match max_values.get_mut(i).unwrap() {
-                Some(max_value) => max_value.evaluate().ok(),
-                None => None,
+            let max_value = match (
+                max_values.get_mut(i).unwrap(),
+                is_max_value_exact.get(i).unwrap(),
+            ) {
+                (Some(max_value), Some(true)) => {
+                    max_value.evaluate().ok().map(Precision::Exact)
+                }
+                (Some(max_value), Some(false)) | (Some(max_value), None) => {
+                    max_value.evaluate().ok().map(Precision::Inexact)
+                }
+                (None, _) => None,
             };
-            let min_value = match min_values.get_mut(i).unwrap() {
-                Some(min_value) => min_value.evaluate().ok(),
-                None => None,
+            let min_value = match (
+                min_values.get_mut(i).unwrap(),
+                is_min_value_exact.get(i).unwrap(),
+            ) {
+                (Some(min_value), Some(true)) => {
+                    min_value.evaluate().ok().map(Precision::Exact)
+                }
+                (Some(min_value), Some(false)) | (Some(min_value), None) => {
+                    min_value.evaluate().ok().map(Precision::Inexact)
+                }
+                (None, _) => None,
             };
             ColumnStatistics {
                 null_count: null_counts[i],
-                max_value: max_value.map(Precision::Exact).unwrap_or(Precision::Absent),
-                min_value: min_value.map(Precision::Exact).unwrap_or(Precision::Absent),
+                max_value: max_value.unwrap_or(Precision::Absent),
+                min_value: min_value.unwrap_or(Precision::Absent),
                 sum_value: Precision::Absent,
                 distinct_count: Precision::Absent,
             }
@@ -1290,6 +1316,8 @@ fn summarize_min_max_null_counts(
     min_accs: &mut [Option<MinAccumulator>],
     max_accs: &mut [Option<MaxAccumulator>],
     null_counts_array: &mut [Precision<usize>],
+    is_min_value_exact: &mut [Option<bool>],
+    is_max_value_exact: &mut [Option<bool>],
     arrow_schema_index: usize,
     num_rows: usize,
     stats_converter: &StatisticsConverter,
@@ -1298,13 +1326,29 @@ fn summarize_min_max_null_counts(
     let max_values = stats_converter.row_group_maxes(row_groups_metadata)?;
     let min_values = stats_converter.row_group_mins(row_groups_metadata)?;
     let null_counts = stats_converter.row_group_null_counts(row_groups_metadata)?;
+    let is_max_value_exact_stat =
+        stats_converter.row_group_is_max_value_exact(row_groups_metadata)?;
+    let is_min_value_exact_stat =
+        stats_converter.row_group_is_min_value_exact(row_groups_metadata)?;
 
     if let Some(max_acc) = &mut max_accs[arrow_schema_index] {
-        max_acc.update_batch(&[max_values])?;
+        max_acc.update_batch(&[Arc::clone(&max_values)])?;
+        let mut cur_max_acc = max_acc.clone();
+        is_max_value_exact[arrow_schema_index] = has_any_exact_match(
+            cur_max_acc.evaluate()?,
+            max_values,
+            is_max_value_exact_stat,
+        );
     }
 
     if let Some(min_acc) = &mut min_accs[arrow_schema_index] {
-        min_acc.update_batch(&[min_values])?;
+        min_acc.update_batch(&[Arc::clone(&min_values)])?;
+        let mut cur_min_acc = min_acc.clone();
+        is_min_value_exact[arrow_schema_index] = has_any_exact_match(
+            cur_min_acc.evaluate()?,
+            min_values,
+            is_min_value_exact_stat,
+        );
     }
 
     null_counts_array[arrow_schema_index] = Precision::Exact(match sum(&null_counts) {
@@ -1967,6 +2011,31 @@ fn create_max_min_accs(
     (max_values, min_values)
 }
 
+/// Checks if any occurrence of `value` in `array` corresponds to a `true`
+/// entry in the `exactness` array.
+///
+/// This is used to determine if a calculated statistic (e.g., min or max)
+/// is exact, by checking if at least one of its source values was exact.
+///
+/// # Example
+/// - `value`: `0`
+/// - `array`: `[0, 1, 0, 3, 0, 5]`
+/// - `exactness`: `[true, false, false, false, false, false]`
+///
+/// The value `0` appears at indices `[0, 2, 4]`. The corresponding exactness
+/// values are `[true, false, false]`. Since at least one is `true`, the
+/// function returns `Some(true)`.
+fn has_any_exact_match(
+    value: ScalarValue,
+    array: ArrayRef,
+    exactness: BooleanArray,
+) -> Option<bool> {
+    let scalar_array = value.to_scalar().ok()?;
+    let eq_mask = eq(&scalar_array, &array).ok()?;
+    let combined_mask = and(&eq_mask, &exactness).ok()?;
+    Some(combined_mask.true_count() > 0)
+}
+
 #[cfg(test)]
 mod tests {
     use std::sync::Arc;

datafusion/functions-aggregate/src/min_max.rs

@@ -736,7 +736,7 @@ macro_rules! min_max {
 }
 
 /// An accumulator to compute the maximum value
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub struct MaxAccumulator {
     max: ScalarValue,
 }
@@ -1057,7 +1057,7 @@ impl AggregateUDFImpl for Min {
 }
 
 /// An accumulator to compute the minimum value
-#[derive(Debug)]
+#[derive(Debug, Clone)]
 pub struct MinAccumulator {
     min: ScalarValue,
 }