Add coerce int96 option for Parquet to support different TimeUnits, test int96_from_spark.parquet from parquet-testing

datafusion/common/src/config.rs

@@ -459,6 +459,14 @@ config_namespace! {
         /// BLOB instead.
         pub binary_as_string: bool, default = false
 
+        /// (reading) If true, parquet reader will read columns of
+        /// physical type int96 as originating from a different resolution
+        /// than nanosecond. This is useful for reading data from systems like Spark
+        /// which stores microsecond resolution timestamps in an int96 allowing it
+        /// to write values with a larger date range than 64-bit timestamps with
+        /// nanosecond resolution.
+        pub coerce_int96: Option<String>, transform = str::to_lowercase, default = None
+
         // The following options affect writing to parquet files
         // and map to parquet::file::properties::WriterProperties
 

datafusion/common/src/file_options/parquet_writer.rs

@@ -239,6 +239,7 @@ impl ParquetOptions {
             bloom_filter_on_read: _, // reads not used for writer props
             schema_force_view_types: _,
             binary_as_string: _, // not used for writer props
+            coerce_int96: _,     // not used for writer props
             skip_arrow_metadata: _,
         } = self;
 
@@ -516,6 +517,7 @@ mod tests {
             schema_force_view_types: defaults.schema_force_view_types,
             binary_as_string: defaults.binary_as_string,
             skip_arrow_metadata: defaults.skip_arrow_metadata,
+            coerce_int96: None,
         }
     }
 
@@ -622,6 +624,7 @@ mod tests {
                 schema_force_view_types: global_options_defaults.schema_force_view_types,
                 binary_as_string: global_options_defaults.binary_as_string,
                 skip_arrow_metadata: global_options_defaults.skip_arrow_metadata,
+                coerce_int96: None,
             },
             column_specific_options,
             key_value_metadata,

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

@@ -382,6 +382,15 @@ mod tests {
         let testdata = test_util::arrow_test_data();
         let store_root = format!("{testdata}/avro");
         let format = AvroFormat {};
-        scan_format(state, &format, &store_root, file_name, projection, limit).await
+        scan_format(
+            state,
+            &format,
+            None,
+            &store_root,
+            file_name,
+            projection,
+            limit,
+        )
+        .await
     }
 }

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

@@ -250,6 +250,7 @@ mod tests {
         let exec = scan_format(
             &state,
             &format,
+            None,
             root,
             "aggregate_test_100_with_nulls.csv",
             projection,
@@ -300,6 +301,7 @@ mod tests {
         let exec = scan_format(
             &state,
             &format,
+            None,
             root,
             "aggregate_test_100_with_nulls.csv",
             projection,
@@ -582,7 +584,7 @@ mod tests {
     ) -> Result<Arc<dyn ExecutionPlan>> {
         let root = format!("{}/csv", arrow_test_data());
         let format = CsvFormat::default().with_has_header(has_header);
-        scan_format(state, &format, &root, file_name, projection, limit).await
+        scan_format(state, &format, None, &root, file_name, projection, limit).await
     }
 
     #[tokio::test]

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

@@ -149,7 +149,7 @@ mod tests {
     ) -> Result<Arc<dyn ExecutionPlan>> {
         let filename = "tests/data/2.json";
         let format = JsonFormat::default();
-        scan_format(state, &format, ".", filename, projection, limit).await
+        scan_format(state, &format, None, ".", filename, projection, limit).await
     }
 
     #[tokio::test]

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

@@ -36,19 +36,20 @@ pub use datafusion_datasource::write;
 
 #[cfg(test)]
 pub(crate) mod test_util {
-    use std::sync::Arc;
-
+    use arrow_schema::SchemaRef;
     use datafusion_catalog::Session;
     use datafusion_common::Result;
     use datafusion_datasource::file_scan_config::FileScanConfigBuilder;
     use datafusion_datasource::{file_format::FileFormat, PartitionedFile};
     use datafusion_execution::object_store::ObjectStoreUrl;
+    use std::sync::Arc;
 
     use crate::test::object_store::local_unpartitioned_file;
 
     pub async fn scan_format(
         state: &dyn Session,
         format: &dyn FileFormat,
+        schema: Option<SchemaRef>,
         store_root: &str,
         file_name: &str,
         projection: Option<Vec<usize>>,
@@ -57,9 +58,13 @@ pub(crate) mod test_util {
         let store = Arc::new(object_store::local::LocalFileSystem::new()) as _;
         let meta = local_unpartitioned_file(format!("{store_root}/{file_name}"));
 
-        let file_schema = format
-            .infer_schema(state, &store, std::slice::from_ref(&meta))
-            .await?;
+        let file_schema = if let Some(file_schema) = schema {
+            file_schema
+        } else {
+            format
+                .infer_schema(state, &store, std::slice::from_ref(&meta))
+                .await?
+        };
 
         let statistics = format
             .infer_stats(state, &store, file_schema.clone(), &meta)

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

@@ -1075,7 +1075,10 @@ mod tests {
             .map(|factory| factory.create(state, &Default::default()).unwrap())
             .unwrap_or(Arc::new(ParquetFormat::new()));
 
-        scan_format(state, &*format, &testdata, file_name, projection, limit).await
+        scan_format(
+            state, &*format, None, &testdata, file_name, projection, limit,
+        )
+        .await
     }
 
     /// Test that 0-byte files don't break while reading

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

@@ -38,7 +38,7 @@ mod tests {
     use crate::prelude::{ParquetReadOptions, SessionConfig, SessionContext};
     use crate::test::object_store::local_unpartitioned_file;
     use arrow::array::{
-        ArrayRef, Date64Array, Int32Array, Int64Array, Int8Array, StringArray,
+        ArrayRef, AsArray, Date64Array, Int32Array, Int64Array, Int8Array, StringArray,
         StructArray,
     };
     use arrow::datatypes::{DataType, Field, Fields, Schema, SchemaBuilder};
@@ -1109,6 +1109,7 @@ mod tests {
         let parquet_exec = scan_format(
             &state,
             &ParquetFormat::default(),
+            None,
             &testdata,
             filename,
             Some(vec![0, 1, 2]),
@@ -1141,6 +1142,92 @@ mod tests {
         Ok(())
     }
 
+    #[tokio::test]
+    async fn parquet_exec_with_int96_from_spark() -> Result<()> {
+        // arrow-rs relies on the chrono library to convert between timestamps and strings, so
+        // instead compare as Int64. The underlying type should be a PrimitiveArray of Int64
+        // anyway, so this should be a zero-copy non-modifying cast at the SchemaAdapter.
+
+        let schema = Arc::new(Schema::new(vec![Field::new("a", DataType::Int64, true)]));
+        let testdata = datafusion_common::test_util::parquet_test_data();
+        let filename = "int96_from_spark.parquet";
+        let session_ctx = SessionContext::new();
+        let state = session_ctx.state();
+        let task_ctx = state.task_ctx();
+
+        let time_units_and_expected = vec![
+            (
+                None, // Same as "ns" time_unit
+                Arc::new(Int64Array::from(vec![
+                    Some(1704141296123456000), // Reads as nanosecond fine (note 3 extra 0s)
+                    Some(1704070800000000000), // Reads as nanosecond fine (note 3 extra 0s)
+                    Some(-4852191831933722624), // Cannot be represented with nanos timestamp (year 9999)
+                    Some(1735599600000000000), // Reads as nanosecond fine (note 3 extra 0s)
+                    None,
+                    Some(-4864435138808946688), // Cannot be represented with nanos timestamp (year 290000)
+                ])),
+            ),
+            (
+                Some("ns".to_string()),
+                Arc::new(Int64Array::from(vec![
+                    Some(1704141296123456000),
+                    Some(1704070800000000000),
+                    Some(-4852191831933722624),
+                    Some(1735599600000000000),
+                    None,
+                    Some(-4864435138808946688),
+                ])),
+            ),
+            (
+                Some("us".to_string()),
+                Arc::new(Int64Array::from(vec![
+                    Some(1704141296123456),
+                    Some(1704070800000000),
+                    Some(253402225200000000),
+                    Some(1735599600000000),
+                    None,
+                    Some(9089380393200000000),
+                ])),
+            ),
+        ];
+
+        for (time_unit, expected) in time_units_and_expected {
+            let parquet_exec = scan_format(
+                &state,
+                &ParquetFormat::default().with_coerce_int96(time_unit.clone()),
+                Some(schema.clone()),
+                &testdata,
+                filename,
+                Some(vec![0]),
+                None,
+            )
+            .await
+            .unwrap();
+            assert_eq!(parquet_exec.output_partitioning().partition_count(), 1);
+
+            let mut results = parquet_exec.execute(0, task_ctx.clone())?;
+            let batch = results.next().await.unwrap()?;
+
+            assert_eq!(6, batch.num_rows());
+            assert_eq!(1, batch.num_columns());
+
+            assert_eq!(batch.num_columns(), 1);
+            let column = batch.column(0);
+
+            assert_eq!(column.len(), expected.len());
+
+            column
+                .as_primitive::<arrow::datatypes::Int64Type>()
+                .iter()
+                .zip(expected.iter())
+                .for_each(|(lhs, rhs)| {
+                    assert_eq!(lhs, rhs);
+                });
+        }
+
+        Ok(())
+    }
+
     #[tokio::test]
     async fn parquet_exec_with_range() -> Result<()> {
         fn file_range(meta: &ObjectMeta, start: i64, end: i64) -> PartitionedFile {

datafusion/datasource-parquet/src/file_format.rs

@@ -24,9 +24,18 @@ use std::ops::Range;
 use std::sync::Arc;
 
 use arrow::array::RecordBatch;
+use arrow::datatypes::{Fields, Schema, SchemaRef, TimeUnit};
+use datafusion_datasource::file_compression_type::FileCompressionType;
+use datafusion_datasource::file_sink_config::{FileSink, FileSinkConfig};
+use datafusion_datasource::write::{create_writer, get_writer_schema, SharedBuffer};
+
+use datafusion_datasource::file_format::{
+    FileFormat, FileFormatFactory, FilePushdownSupport,
+};
+use datafusion_datasource::write::demux::DemuxedStreamReceiver;
+
 use arrow::compute::sum;
 use arrow::datatypes::{DataType, Field, FieldRef};
-use arrow::datatypes::{Fields, Schema, SchemaRef};
 use datafusion_common::config::{ConfigField, ConfigFileType, TableParquetOptions};
 use datafusion_common::parsers::CompressionTypeVariant;
 use datafusion_common::stats::Precision;
@@ -38,15 +47,8 @@ use datafusion_common::{HashMap, Statistics};
 use datafusion_common_runtime::{JoinSet, SpawnedTask};
 use datafusion_datasource::display::FileGroupDisplay;
 use datafusion_datasource::file::FileSource;
-use datafusion_datasource::file_compression_type::FileCompressionType;
-use datafusion_datasource::file_format::{
-    FileFormat, FileFormatFactory, FilePushdownSupport,
-};
 use datafusion_datasource::file_scan_config::{FileScanConfig, FileScanConfigBuilder};
-use datafusion_datasource::file_sink_config::{FileSink, FileSinkConfig};
 use datafusion_datasource::sink::{DataSink, DataSinkExec};
-use datafusion_datasource::write::demux::DemuxedStreamReceiver;
-use datafusion_datasource::write::{create_writer, get_writer_schema, SharedBuffer};
 use datafusion_execution::memory_pool::{MemoryConsumer, MemoryPool, MemoryReservation};
 use datafusion_execution::{SendableRecordBatchStream, TaskContext};
 use datafusion_expr::dml::InsertOp;
@@ -76,11 +78,13 @@ use parquet::arrow::arrow_writer::{
 };
 use parquet::arrow::async_reader::MetadataFetch;
 use parquet::arrow::{parquet_to_arrow_schema, ArrowSchemaConverter, AsyncArrowWriter};
+use parquet::basic::Type;
 use parquet::errors::ParquetError;
 use parquet::file::metadata::{ParquetMetaData, ParquetMetaDataReader, RowGroupMetaData};
 use parquet::file::properties::{WriterProperties, WriterPropertiesBuilder};
 use parquet::file::writer::SerializedFileWriter;
 use parquet::format::FileMetaData;
+use parquet::schema::types::SchemaDescriptor;
 use tokio::io::{AsyncWrite, AsyncWriteExt};
 use tokio::sync::mpsc::{self, Receiver, Sender};
 
@@ -268,6 +272,15 @@ impl ParquetFormat {
         self.options.global.binary_as_string = binary_as_string;
         self
     }
+
+    pub fn coerce_int96(&self) -> Option<String> {
+        self.options.global.coerce_int96.clone()
+    }
+
+    pub fn with_coerce_int96(mut self, time_unit: Option<String>) -> Self {
+        self.options.global.coerce_int96 = time_unit;
+        self
+    }
 }
 
 /// Clears all metadata (Schema level and field level) on an iterator
@@ -569,6 +582,46 @@ pub fn apply_file_schema_type_coercions(
     ))
 }
 
+/// Coerces the file schema's Timestamps to the provided TimeUnit if Parquet schema contains INT96.
+pub fn coerce_int96_to_resolution(
+    parquet_schema: &SchemaDescriptor,
+    file_schema: &Schema,
+    time_unit: &TimeUnit,
+) -> Option<Schema> {
+    let mut transform = false;
+    let parquet_fields: HashMap<_, _> = parquet_schema
+        .columns()
+        .iter()
+        .map(|f| {
+            let dt = f.physical_type();
+            if dt.eq(&Type::INT96) {
+                transform = true;
+            }
+            (f.name(), dt)
+        })
+        .collect();
+
+    if !transform {
+        return None;
+    }
+
+    let transformed_fields: Vec<Arc<Field>> = file_schema
+        .fields
+        .iter()
+        .map(|field| match parquet_fields.get(field.name().as_str()) {
+            Some(Type::INT96) => {
+                field_with_new_type(field, DataType::Timestamp(*time_unit, None))
+            }
+            _ => Arc::clone(field),
+        })
+        .collect();
+
+    Some(Schema::new_with_metadata(
+        transformed_fields,
+        file_schema.metadata.clone(),
+    ))
+}
+
 /// Coerces the file schema if the table schema uses a view type.
 #[deprecated(
     since = "47.0.0",