feat: Add support for Int8 and Int16 data types in data page statistics

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

@@ -572,13 +572,38 @@ macro_rules! make_data_page_stats_iterator {
     };
 }
 
+make_data_page_stats_iterator!(MinInt32DataPageStatsIterator, min, Index::INT32, i32);
+make_data_page_stats_iterator!(MaxInt32DataPageStatsIterator, max, Index::INT32, i32);
 make_data_page_stats_iterator!(MinInt64DataPageStatsIterator, min, Index::INT64, i64);
 make_data_page_stats_iterator!(MaxInt64DataPageStatsIterator, max, Index::INT64, i64);
 
 macro_rules! get_data_page_statistics {
     ($stat_type_prefix: ident, $data_type: ident, $iterator: ident) => {
         paste! {
             match $data_type {
+                Some(DataType::Int8) => Ok(Arc::new(
+                    Int8Array::from_iter(
+                        [<$stat_type_prefix Int32DataPageStatsIterator>]::new($iterator)
+                            .map(|x| {
+                                x.into_iter().filter_map(|x| {
+                                    x.and_then(|x| i8::try_from(x).ok())
+                                })
+                            })
+                            .flatten()
+                    )
+                )),
+                Some(DataType::Int16) => Ok(Arc::new(
+                    Int16Array::from_iter(
+                        [<$stat_type_prefix Int32DataPageStatsIterator>]::new($iterator)
+                            .map(|x| {
+                                x.into_iter().filter_map(|x| {
+                                    x.and_then(|x| i16::try_from(x).ok())
+                                })
+                            })
+                            .flatten()
+                    )
+                )),
+                Some(DataType::Int32) => Ok(Arc::new(Int32Array::from_iter([<$stat_type_prefix Int32DataPageStatsIterator>]::new($iterator).flatten()))),
                 Some(DataType::Int64) => Ok(Arc::new(Int64Array::from_iter([<$stat_type_prefix Int64DataPageStatsIterator>]::new($iterator).flatten()))),
                 _ => unimplemented!()
             }
@@ -666,6 +691,11 @@ where
 {
     let iter = iterator.flat_map(|(len, index)| match index {
         Index::NONE => vec![None; len],
+        Index::INT32(native_index) => native_index
+            .indexes
+            .iter()
+            .map(|x| x.null_count.map(|x| x as u64))
+            .collect::<Vec<_>>(),
         Index::INT64(native_index) => native_index
             .indexes
             .iter()

datafusion/core/tests/parquet/arrow_statistics.rs

@@ -550,16 +550,11 @@ async fn test_int_32() {
         // row counts are [5, 5, 5, 5]
         expected_row_counts: UInt64Array::from(vec![5, 5, 5, 5]),
         column_name: "i32",
-        check: Check::RowGroup,
+        check: Check::Both,
     }
     .run();
 }
 
-// BUG: ignore this test for now
-// https://github.com/apache/datafusion/issues/10585
-// Note that the file has 4 columns named "i8", "i16", "i32", "i64".
-//   - The tests on column i32 and i64 passed.
-//   - The tests on column i8 and i16 failed.
 #[tokio::test]
 async fn test_int_16() {
     // This creates a parquet files of 4 columns named "i8", "i16", "i32", "i64"
@@ -573,16 +568,6 @@ async fn test_int_16() {
     Test {
         reader: &reader,
         // mins are [-5, -4, 0, 5]
-        // BUG: not sure why this returns same data but in Int32Array type even though I debugged and the columns name is "i16" an its data is Int16
-        // My debugging tells me the bug is either at:
-        //   1. The new code to get "iter". See the code in this PR with
-        // // Get an iterator over the column statistics
-        // let iter = row_groups
-        // .iter()
-        // .map(|x| x.column(parquet_idx).statistics());
-        //    OR
-        //   2. in the function (and/or its marco) `pub(crate) fn min_statistics<'a, I: Iterator<Item = Option<&'a ParquetStatistics>>>` here
-        //      https://github.com/apache/datafusion/blob/ea023e2d4878240eece870cf4b346c7a0667aeed/datafusion/core/src/datasource/physical_plan/parquet/statistics.rs#L179
         expected_min: Arc::new(Int16Array::from(vec![-5, -4, 0, 5])), // panic here because the actual data is Int32Array
         // maxes are [-1, 0, 4, 9]
         expected_max: Arc::new(Int16Array::from(vec![-1, 0, 4, 9])),
@@ -591,13 +576,11 @@ async fn test_int_16() {
         // row counts are [5, 5, 5, 5]
         expected_row_counts: UInt64Array::from(vec![5, 5, 5, 5]),
         column_name: "i16",
-        check: Check::RowGroup,
+        check: Check::Both,
     }
     .run();
 }
 
-// BUG (same as above): ignore this test for now
-// https://github.com/apache/datafusion/issues/10585
 #[tokio::test]
 async fn test_int_8() {
     // This creates a parquet files of 4 columns named "i8", "i16", "i32", "i64"
@@ -611,7 +594,6 @@ async fn test_int_8() {
     Test {
         reader: &reader,
         // mins are [-5, -4, 0, 5]
-        // BUG: not sure why this returns same data but in Int32Array even though I debugged and the columns name is "i8" an its data is Int8
         expected_min: Arc::new(Int8Array::from(vec![-5, -4, 0, 5])), // panic here because the actual data is Int32Array
         // maxes are [-1, 0, 4, 9]
         expected_max: Arc::new(Int8Array::from(vec![-1, 0, 4, 9])),
@@ -620,7 +602,7 @@ async fn test_int_8() {
         // row counts are [5, 5, 5, 5]
         expected_row_counts: UInt64Array::from(vec![5, 5, 5, 5]),
         column_name: "i8",
-        check: Check::RowGroup,
+        check: Check::Both,
     }
     .run();
 }