Add SchemaInferenceOptions options to infer_schema and option to configure int96 inference

src/io/parquet/read/schema/convert.rs

@@ -1,4 +1,4 @@
-//! This module has a single entry point, [`parquet_to_arrow_schema`].
+//! This module has entry points, [`parquet_to_arrow_schema`] and the more configurable [`parquet_to_arrow_schema_with_options`].
 use parquet2::schema::{
     types::{
         FieldInfo, GroupConvertedType, GroupLogicalType, IntegerType, ParquetType, PhysicalType,
@@ -8,11 +8,23 @@
 };
 
 use crate::datatypes::{DataType, Field, IntervalUnit, TimeUnit};
+use crate::io::parquet::read::schema::SchemaInferenceOptions;
 
 /// Converts [`ParquetType`]s to a [`Field`], ignoring parquet fields that do not contain
 /// any physical column.
 pub fn parquet_to_arrow_schema(fields: &[ParquetType]) -> Vec<Field> {
-    fields.iter().filter_map(to_field).collect::<Vec<_>>()
+    parquet_to_arrow_schema_with_options(fields, &None)
+}
+
+/// Like [`parquet_to_arrow_schema`] but with configurable options which affect the behavior of schema inference
+pub fn parquet_to_arrow_schema_with_options(
+    fields: &[ParquetType],
+    options: &Option<SchemaInferenceOptions>,
+) -> Vec<Field> {
+    fields
+        .iter()
+        .filter_map(|f| to_field(f, options.as_ref().unwrap_or(&Default::default())))
+        .collect::<Vec<_>>()
 }
 
 fn from_int32(
@@ -169,7 +181,10 @@
 }
 
 /// Maps a [`PhysicalType`] with optional metadata to a [`DataType`]
-fn to_primitive_type_inner(primitive_type: &PrimitiveType) -> DataType {
+fn to_primitive_type_inner(
+    primitive_type: &PrimitiveType,
+    options: &SchemaInferenceOptions,
+) -> DataType {
     match primitive_type.physical_type {
         PhysicalType::Boolean => DataType::Boolean,
         PhysicalType::Int32 => {
@@ -178,7 +193,7 @@
         PhysicalType::Int64 => {
             from_int64(primitive_type.logical_type, primitive_type.converted_type)
         }
-        PhysicalType::Int96 => DataType::Timestamp(TimeUnit::Nanosecond, None),
+        PhysicalType::Int96 => DataType::Timestamp(options.int96_coerce_to_timeunit, None),
         PhysicalType::Float => DataType::Float32,
         PhysicalType::Double => DataType::Float64,
         PhysicalType::ByteArray => {
@@ -195,8 +210,8 @@
 /// Entry point for converting parquet primitive type to arrow type.
 ///
 /// This function takes care of repetition.
-fn to_primitive_type(primitive_type: &PrimitiveType) -> DataType {
-    let base_type = to_primitive_type_inner(primitive_type);
+fn to_primitive_type(primitive_type: &PrimitiveType, options: &SchemaInferenceOptions) -> DataType {
+    let base_type = to_primitive_type_inner(primitive_type, options);
 
     if primitive_type.field_info.repetition == Repetition::Repeated {
         DataType::List(Box::new(Field::new(
@@ -214,23 +229,27 @@
     converted_type: &Option<GroupConvertedType>,
     fields: &[ParquetType],
     parent_name: &str,
+    options: &SchemaInferenceOptions,
 ) -> Option<DataType> {
     debug_assert!(!fields.is_empty());
     match (logical_type, converted_type) {
-        (Some(GroupLogicalType::List), _) => to_list(fields, parent_name),
-        (None, Some(GroupConvertedType::List)) => to_list(fields, parent_name),
-        (Some(GroupLogicalType::Map), _) => to_list(fields, parent_name),
+        (Some(GroupLogicalType::List), _) => to_list(fields, parent_name, options),
+        (None, Some(GroupConvertedType::List)) => to_list(fields, parent_name, options),
+        (Some(GroupLogicalType::Map), _) => to_list(fields, parent_name, options),
         (None, Some(GroupConvertedType::Map) | Some(GroupConvertedType::MapKeyValue)) => {
-            to_map(fields)
+            to_map(fields, options)
         }
-        _ => to_struct(fields),
+        _ => to_struct(fields, options),
     }
 }
 
 /// Converts a parquet group type to an arrow [`DataType::Struct`].
 /// Returns [`None`] if all its fields are empty
-fn to_struct(fields: &[ParquetType]) -> Option<DataType> {
-    let fields = fields.iter().filter_map(to_field).collect::<Vec<Field>>();
+fn to_struct(fields: &[ParquetType], options: &SchemaInferenceOptions) -> Option<DataType> {
+    let fields = fields
+        .iter()
+        .filter_map(|f| to_field(f, options))
+        .collect::<Vec<Field>>();
     if fields.is_empty() {
         None
     } else {
@@ -240,8 +259,8 @@
 
 /// Converts a parquet group type to an arrow [`DataType::Struct`].
 /// Returns [`None`] if all its fields are empty
-fn to_map(fields: &[ParquetType]) -> Option<DataType> {
-    let inner = to_field(&fields[0])?;
+fn to_map(fields: &[ParquetType], options: &SchemaInferenceOptions) -> Option<DataType> {
+    let inner = to_field(&fields[0], options)?;
     Some(DataType::Map(Box::new(inner), false))
 }
 
@@ -254,16 +273,17 @@
     converted_type: &Option<GroupConvertedType>,
     fields: &[ParquetType],
     parent_name: &str,
+    options: &SchemaInferenceOptions,
 ) -> Option<DataType> {
     debug_assert!(!fields.is_empty());
     if field_info.repetition == Repetition::Repeated {
         Some(DataType::List(Box::new(Field::new(
             &field_info.name,
-            to_struct(fields)?,
+            to_struct(fields, options)?,
             is_nullable(field_info),
         ))))
     } else {
-        non_repeated_group(logical_type, converted_type, fields, parent_name)
+        non_repeated_group(logical_type, converted_type, fields, parent_name, options)
     }
 }
 
@@ -279,10 +299,10 @@
 /// Converts parquet schema to arrow field.
 /// Returns `None` iff the parquet type has no associated primitive types,
 /// i.e. if it is a column-less group type.
-fn to_field(type_: &ParquetType) -> Option<Field> {
+fn to_field(type_: &ParquetType, options: &SchemaInferenceOptions) -> Option<Field> {
     Some(Field::new(
         &type_.get_field_info().name,
-        to_data_type(type_)?,
+        to_data_type(type_, options)?,
         is_nullable(type_.get_field_info()),
     ))
 }
@@ -291,21 +311,25 @@
 ///
 /// To fully understand this algorithm, please refer to
 /// [parquet doc](https://github.com/apache/parquet-format/blob/master/LogicalTypes.md).
-fn to_list(fields: &[ParquetType], parent_name: &str) -> Option<DataType> {
+fn to_list(
+    fields: &[ParquetType],
+    parent_name: &str,
+    options: &SchemaInferenceOptions,
+) -> Option<DataType> {
     let item = fields.first().unwrap();
 
     let item_type = match item {
-        ParquetType::PrimitiveType(primitive) => Some(to_primitive_type_inner(primitive)),
+        ParquetType::PrimitiveType(primitive) => Some(to_primitive_type_inner(primitive, options)),
         ParquetType::GroupType { fields, .. } => {
             if fields.len() == 1
                 && item.name() != "array"
                 && item.name() != format!("{parent_name}_tuple")
             {
                 // extract the repetition field
                 let nested_item = fields.first().unwrap();
-                to_data_type(nested_item)
+                to_data_type(nested_item, options)
             } else {
-                to_struct(fields)
+                to_struct(fields, options)
             }
         }
     }?;
@@ -346,9 +370,12 @@
 ///
 /// If this schema is a group type and none of its children is reserved in the
 /// conversion, the result is Ok(None).
-pub(crate) fn to_data_type(type_: &ParquetType) -> Option<DataType> {
+pub(crate) fn to_data_type(
+    type_: &ParquetType,
+    options: &SchemaInferenceOptions,
+) -> Option<DataType> {
     match type_ {
-        ParquetType::PrimitiveType(primitive) => Some(to_primitive_type(primitive)),
+        ParquetType::PrimitiveType(primitive) => Some(to_primitive_type(primitive, options)),
         ParquetType::GroupType {
             field_info,
             logical_type,
@@ -364,6 +391,7 @@
                     converted_type,
                     fields,
                     &field_info.name,
+                    options,
                 )
             }
         }
@@ -973,4 +1001,52 @@
         assert_eq!(arrow_fields, fields);
         Ok(())
     }
+
+    #[test]
+    fn test_int96_options() -> Result<()> {
+        for tu in [
+            TimeUnit::Second,
+            TimeUnit::Microsecond,
+            TimeUnit::Millisecond,
+            TimeUnit::Nanosecond,
+        ] {
+            let message_type = "
+            message arrow_schema {
+                REQUIRED INT96   int96_field;
+                OPTIONAL GROUP   int96_list (LIST) {
+                    REPEATED GROUP list {
+                        OPTIONAL INT96 element;
+                    }
+                }
+                REQUIRED GROUP int96_struct {
+                    REQUIRED INT96 int96_field;
+                }
+            }
+            ";
+            let coerced_to = DataType::Timestamp(tu, None);
+            let arrow_fields = vec![
+                Field::new("int96_field", coerced_to.clone(), false),
+                Field::new(
+                    "int96_list",
+                    DataType::List(Box::new(Field::new("element", coerced_to.clone(), true))),
+                    true,
+                ),
+                Field::new(
+                    "int96_struct",
+                    DataType::Struct(vec![Field::new("int96_field", coerced_to.clone(), false)]),
+                    false,
+                ),
+            ];
+
+            let parquet_schema = SchemaDescriptor::try_from_message(message_type)?;
+            let fields = parquet_to_arrow_schema_with_options(
+                parquet_schema.fields(),
+                &Some(SchemaInferenceOptions {
+                    int96_coerce_to_timeunit: tu,
+                }),
+            );
+            assert_eq!(arrow_fields, fields);
+        }
+        Ok(())
+    }
 }

src/io/parquet/read/schema/mod.rs

@@ -1,11 +1,11 @@
 //! APIs to handle Parquet <-> Arrow schemas.
-use crate::datatypes::Schema;
+use crate::datatypes::{Schema, TimeUnit};
 use crate::error::Result;
 
 mod convert;
 mod metadata;
 
-pub use convert::parquet_to_arrow_schema;
+pub use convert::{parquet_to_arrow_schema, parquet_to_arrow_schema_with_options};
 pub use metadata::read_schema_from_metadata;
 pub use parquet2::metadata::{FileMetaData, KeyValue, SchemaDescriptor};
 pub use parquet2::schema::types::ParquetType;
@@ -14,18 +14,46 @@ pub(crate) use convert::*;
 
 use self::metadata::parse_key_value_metadata;
 
+/// Options when inferring schemas from Parquet
+pub struct SchemaInferenceOptions {
+    /// When inferring schemas from the Parquet INT96 timestamp type, this is the corresponding TimeUnit
+    /// in the inferred Arrow Timestamp type.
+    ///
+    /// This defaults to `TimeUnit::Nanosecond`, but INT96 timestamps outside of the range of years 1678-2262,
+    /// will overflow when parsed as `Timestamp(TimeUnit::Nanosecond)`. Setting this to a lower resolution
+    /// (e.g. TimeUnit::Milliseconds) will result in loss of precision, but support a larger range of dates
+    /// without overflowing when parsing the data.
+    pub int96_coerce_to_timeunit: TimeUnit,
+}
+
+impl Default for SchemaInferenceOptions {
+    fn default() -> Self {
+        SchemaInferenceOptions {
+            int96_coerce_to_timeunit: TimeUnit::Nanosecond,
+        }
+    }
+}
+
 /// Infers a [`Schema`] from parquet's [`FileMetaData`]. This first looks for the metadata key
 /// `"ARROW:schema"`; if it does not exist, it converts the parquet types declared in the
 /// file's parquet schema to Arrow's equivalent.
 /// # Error
 /// This function errors iff the key `"ARROW:schema"` exists but is not correctly encoded,
 /// indicating that that the file's arrow metadata was incorrectly written.
 pub fn infer_schema(file_metadata: &FileMetaData) -> Result<Schema> {
+    infer_schema_with_options(file_metadata, &None)
+}
+
+/// Like [`infer_schema`] but with configurable options which affects the behavior of inference
+pub fn infer_schema_with_options(
+    file_metadata: &FileMetaData,
+    options: &Option<SchemaInferenceOptions>,
+) -> Result<Schema> {
     let mut metadata = parse_key_value_metadata(file_metadata.key_value_metadata());
 
     let schema = read_schema_from_metadata(&mut metadata)?;
     Ok(schema.unwrap_or_else(|| {
-        let fields = parquet_to_arrow_schema(file_metadata.schema().fields());
+        let fields = parquet_to_arrow_schema_with_options(file_metadata.schema().fields(), options);
         Schema { fields, metadata }
     }))
 }