Add configuration option to StatisticsConverter to control interpretation of missing null counts in Parquet statistics (#6485)

* Write null counts in parquet files when they are present

* revert writing of Some(0)

* fix docstring

---------

Co-authored-by: Andrew Lamb <andrew@nerdnetworks.org>

Author: etseidl

parquet/src/arrow/arrow_reader/statistics.rs

@@ -1159,7 +1159,7 @@ where
 ///
 /// # Schemas
 ///
-/// The converter ues the schema of the Parquet file and the Arrow schema to
+/// The converter uses the schema of the Parquet file and the Arrow schema to
 /// convert the underlying statistics value (stored as a parquet value) into the
 /// corresponding Arrow value. For example, Decimals are stored as binary in
 /// parquet files and this structure handles mapping them to the `i128`
@@ -1175,6 +1175,8 @@ pub struct StatisticsConverter<'a> {
     parquet_column_index: Option<usize>,
     /// The field (with data type) of the column in the Arrow schema
     arrow_field: &'a Field,
+    /// treat missing null_counts as 0 nulls
+    missing_null_counts_as_zero: bool,
 }
 
 impl<'a> StatisticsConverter<'a> {
@@ -1191,6 +1193,23 @@ impl<'a> StatisticsConverter<'a> {
         self.arrow_field
     }
 
+    /// Set the statistics converter to treat missing null counts as missing
+    ///
+    /// By default, the converter will treat missing null counts as though
+    /// the null count is known to be `0`.
+    ///
+    /// Note that parquet files written by parquet-rs currently do not store
+    /// null counts even when it is known there are zero nulls, and the reader
+    /// will return 0 for the null counts in that instance. This behavior may
+    /// change in a future release.
+    ///
+    /// Both parquet-java and parquet-cpp store null counts as 0 when there are
+    /// no nulls, and don't write unknown values to the null count field.
+    pub fn with_missing_null_counts_as_zero(mut self, missing_null_counts_as_zero: bool) -> Self {
+        self.missing_null_counts_as_zero = missing_null_counts_as_zero;
+        self
+    }
+
     /// Returns a [`UInt64Array`] with row counts for each row group
     ///
     /// # Return Value
@@ -1284,6 +1303,7 @@ impl<'a> StatisticsConverter<'a> {
         Ok(Self {
             parquet_column_index: parquet_index,
             arrow_field,
+            missing_null_counts_as_zero: true,
         })
     }
 
@@ -1382,7 +1402,15 @@ impl<'a> StatisticsConverter<'a> {
         let null_counts = metadatas
             .into_iter()
             .map(|x| x.column(parquet_index).statistics())
-            .map(|s| s.and_then(|s| s.null_count_opt()));
+            .map(|s| {
+                s.and_then(|s| {
+                    if self.missing_null_counts_as_zero {
+                        Some(s.null_count_opt().unwrap_or(0))
+                    } else {
+                        s.null_count_opt()
+                    }
+                })
+            });
         Ok(UInt64Array::from_iter(null_counts))
     }
 
@@ -1593,3 +1621,5 @@ impl<'a> StatisticsConverter<'a> {
         new_null_array(data_type, num_row_groups)
     }
 }
+
+// See tests in parquet/tests/arrow_reader/statistics.rs

parquet/tests/arrow_reader/statistics.rs

@@ -22,6 +22,7 @@ use std::default::Default;
 use std::fs::File;
 use std::sync::Arc;
 
+use super::make_test_file_rg;
 use super::{struct_array, Scenario};
 use arrow::compute::kernels::cast_utils::Parser;
 use arrow::datatypes::{
@@ -37,16 +38,17 @@ use arrow_array::{
     TimestampMillisecondArray, TimestampNanosecondArray, TimestampSecondArray, UInt16Array,
     UInt32Array, UInt64Array, UInt8Array,
 };
-use arrow_schema::{DataType, Field, Schema, TimeUnit};
+use arrow_schema::{DataType, Field, Schema, SchemaRef, TimeUnit};
 use half::f16;
 use parquet::arrow::arrow_reader::statistics::StatisticsConverter;
 use parquet::arrow::arrow_reader::{
     ArrowReaderBuilder, ArrowReaderOptions, ParquetRecordBatchReaderBuilder,
 };
 use parquet::arrow::ArrowWriter;
+use parquet::file::metadata::{ColumnChunkMetaData, RowGroupMetaData};
 use parquet::file::properties::{EnabledStatistics, WriterProperties};
-
-use super::make_test_file_rg;
+use parquet::file::statistics::{Statistics, ValueStatistics};
+use parquet::schema::types::{SchemaDescPtr, SchemaDescriptor};
 
 #[derive(Debug, Default, Clone)]
 struct Int64Case {
@@ -2139,6 +2141,65 @@ async fn test_missing_statistics() {
     .run();
 }
 
+#[test]
+fn missing_null_counts_as_zero() {
+    let min = None;
+    let max = None;
+    let distinct_count = None;
+    let null_count = None; // NB: no null count
+    let is_min_max_deprecated = false;
+    let stats = Statistics::Boolean(ValueStatistics::new(
+        min,
+        max,
+        distinct_count,
+        null_count,
+        is_min_max_deprecated,
+    ));
+    let (arrow_schema, parquet_schema) = bool_arrow_and_parquet_schema();
+
+    let column_chunk = ColumnChunkMetaData::builder(parquet_schema.column(0))
+        .set_statistics(stats)
+        .build()
+        .unwrap();
+    let metadata = RowGroupMetaData::builder(parquet_schema.clone())
+        .set_column_metadata(vec![column_chunk])
+        .build()
+        .unwrap();
+
+    let converter = StatisticsConverter::try_new("b", &arrow_schema, &parquet_schema).unwrap();
+
+    // by default null count should be 0
+    assert_eq!(
+        converter.row_group_null_counts([&metadata]).unwrap(),
+        UInt64Array::from_iter(vec![Some(0)])
+    );
+
+    // if we disable missing null counts as zero flag null count will be None
+    let converter = converter.with_missing_null_counts_as_zero(false);
+    assert_eq!(
+        converter.row_group_null_counts([&metadata]).unwrap(),
+        UInt64Array::from_iter(vec![None])
+    );
+}
+
+/// return an Arrow schema and corresponding Parquet SchemaDescriptor for
+/// a schema with a single boolean column "b"
+fn bool_arrow_and_parquet_schema() -> (SchemaRef, SchemaDescPtr) {
+    let arrow_schema = Arc::new(Schema::new(vec![Field::new("b", DataType::Boolean, true)]));
+    use parquet::schema::types::Type as ParquetType;
+    let parquet_schema = ParquetType::group_type_builder("schema")
+        .with_fields(vec![Arc::new(
+            ParquetType::primitive_type_builder("a", parquet::basic::Type::INT32)
+                .build()
+                .unwrap(),
+        )])
+        .build()
+        .unwrap();
+
+    let parquet_schema = Arc::new(SchemaDescriptor::new(Arc::new(parquet_schema)));
+    (arrow_schema, parquet_schema)
+}
+
 /////// NEGATIVE TESTS ///////
 // column not found
 #[tokio::test]