feat: Support Spark expression hours

native/core/src/execution/expressions/temporal.rs

@@ -25,7 +25,8 @@ use datafusion::logical_expr::ScalarUDF;
 use datafusion::physical_expr::{PhysicalExpr, ScalarFunctionExpr};
 use datafusion_comet_proto::spark_expression::Expr;
 use datafusion_comet_spark_expr::{
-    SparkHour, SparkMinute, SparkSecond, SparkUnixTimestamp, TimestampTruncExpr,
+    SparkHour, SparkHoursTransform, SparkMinute, SparkSecond, SparkUnixTimestamp,
+    TimestampTruncExpr,
 };
 
 use crate::execution::{
@@ -160,3 +161,29 @@ impl ExpressionBuilder for TruncTimestampBuilder {
         Ok(Arc::new(TimestampTruncExpr::new(child, format, timezone)))
     }
 }
+
+pub struct HoursTransformBuilder;
+
+impl ExpressionBuilder for HoursTransformBuilder {
+    fn build(
+        &self,
+        spark_expr: &Expr,
+        input_schema: SchemaRef,
+        planner: &PhysicalPlanner,
+    ) -> Result<Arc<dyn PhysicalExpr>, ExecutionError> {
+        let expr = extract_expr!(spark_expr, HoursTransform);
+        let child = planner.create_expr(expr.child.as_ref().unwrap(), Arc::clone(&input_schema))?;
+        let args = vec![child];
+        let comet_hours_transform = Arc::new(ScalarUDF::new_from_impl(SparkHoursTransform::new()));
+        let field_ref = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let expr: ScalarFunctionExpr = ScalarFunctionExpr::new(
+            "hours_transform",
+            comet_hours_transform,
+            args,
+            field_ref,
+            Arc::new(ConfigOptions::default()),
+        );
+
+        Ok(Arc::new(expr))
+    }
+}

native/core/src/execution/planner/expression_registry.rs

@@ -110,6 +110,7 @@ pub enum ExpressionType {
     Second,
     TruncTimestamp,
     UnixTimestamp,
+    HoursTransform,
 }
 
 /// Registry for expression builders
@@ -310,6 +311,10 @@ impl ExpressionRegistry {
             ExpressionType::TruncTimestamp,
             Box::new(TruncTimestampBuilder),
         );
+        self.builders.insert(
+            ExpressionType::HoursTransform,
+            Box::new(HoursTransformBuilder),
+        );
     }
 
     /// Extract expression type from Spark protobuf expression
@@ -382,6 +387,7 @@ impl ExpressionRegistry {
             Some(ExprStruct::Second(_)) => Ok(ExpressionType::Second),
             Some(ExprStruct::TruncTimestamp(_)) => Ok(ExpressionType::TruncTimestamp),
             Some(ExprStruct::UnixTimestamp(_)) => Ok(ExpressionType::UnixTimestamp),
+            Some(ExprStruct::HoursTransform(_)) => Ok(ExpressionType::HoursTransform),
 
             Some(other) => Err(ExecutionError::GeneralError(format!(
                 "Unsupported expression type: {:?}",

native/proto/src/proto/expr.proto

@@ -88,6 +88,7 @@ message Expr {
     UnixTimestamp unix_timestamp = 65;
     FromJson from_json = 66;
     ToCsv to_csv = 67;
+    HoursTransform hours_transform = 68;
   }
 
   // Optional QueryContext for error reporting (contains SQL text and position)
@@ -356,6 +357,10 @@ message Hour {
   string timezone = 2;
 }
 
+message HoursTransform {
+  Expr child = 1;
+}
+
 message Minute {
   Expr child = 1;
   string timezone = 2;

native/spark-expr/src/datetime_funcs/hours.rs

@@ -0,0 +1,281 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+//! Spark-compatible `hours` V2 partition transform.
+//!
+//! Computes the number of hours since the Unix epoch (1970-01-01 00:00:00 UTC).
+//!
+//! Both `TimestampType` and `TimestampNTZType` are computationally identical. They
+//! extract the absolute hours since the epoch by directly dividing the microsecond
+//! value by the number of microseconds in an hour, ignoring session timezone offsets.
+
+use arrow::array::cast::as_primitive_array;
+use arrow::array::types::TimestampMicrosecondType;
+use arrow::array::{Array, Int32Array};
+use arrow::datatypes::{DataType, TimeUnit::Microsecond};
+use datafusion::common::{internal_datafusion_err, DataFusionError};
+use datafusion::logical_expr::{
+    ColumnarValue, ScalarFunctionArgs, ScalarUDFImpl, Signature, Volatility,
+};
+use num::integer::div_floor;
+use std::{any::Any, fmt::Debug, sync::Arc};
+
+const MICROS_PER_HOUR: i64 = 3_600_000_000;
+
+#[derive(Debug, PartialEq, Eq, Hash)]
+pub struct SparkHoursTransform {
+    signature: Signature,
+}
+
+impl SparkHoursTransform {
+    pub fn new() -> Self {
+        Self {
+            signature: Signature::user_defined(Volatility::Immutable),
+        }
+    }
+}
+
+impl Default for SparkHoursTransform {
+    fn default() -> Self {
+        Self::new()
+    }
+}
+
+impl ScalarUDFImpl for SparkHoursTransform {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn name(&self) -> &str {
+        "hours_transform"
+    }
+
+    fn signature(&self) -> &Signature {
+        &self.signature
+    }
+
+    fn return_type(&self, _arg_types: &[DataType]) -> datafusion::common::Result<DataType> {
+        Ok(DataType::Int32)
+    }
+
+    fn invoke_with_args(
+        &self,
+        args: ScalarFunctionArgs,
+    ) -> datafusion::common::Result<ColumnarValue> {
+        let args: [ColumnarValue; 1] = args.args.try_into().map_err(|_| {
+            internal_datafusion_err!("hours_transform expects exactly one argument")
+        })?;
+
+        match args {
+            [ColumnarValue::Array(array)] => {
+                let result: Int32Array = match array.data_type() {
+                    DataType::Timestamp(Microsecond, _) => {
+                        let ts_array = as_primitive_array::<TimestampMicrosecondType>(&array);
+                        arrow::compute::kernels::arity::unary(ts_array, |micros| {
+                            div_floor(micros, MICROS_PER_HOUR) as i32
+                        })
+                    }
+                    other => {
+                        return Err(DataFusionError::Execution(format!(
+                            "hours_transform does not support input type: {:?}",
+                            other
+                        )));
+                    }
+                };
+                Ok(ColumnarValue::Array(Arc::new(result)))
+            }
+            _ => Err(DataFusionError::Execution(
+                "hours_transform(scalar) should be folded on Spark JVM side.".to_string(),
+            )),
+        }
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use arrow::array::TimestampMicrosecondArray;
+    use arrow::datatypes::Field;
+    use datafusion::config::ConfigOptions;
+    use std::sync::Arc;
+
+    #[test]
+    fn test_hours_transform_utc() {
+        let udf = SparkHoursTransform::new();
+        // 2023-10-01 14:30:00 UTC = 1696171800 seconds = 1696171800000000 micros
+        // Expected hours since epoch = 1696171800000000 / 3600000000 = 471158
+        let ts = TimestampMicrosecondArray::from(vec![Some(1_696_171_800_000_000i64)])
+            .with_timezone("UTC");
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), 471158);
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_ntz() {
+        let udf = SparkHoursTransform::new();
+        // Same timestamp but NTZ (no timezone on array)
+        let ts = TimestampMicrosecondArray::from(vec![Some(1_696_171_800_000_000i64)]);
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), 471158);
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_negative_epoch() {
+        let udf = SparkHoursTransform::new();
+        // 1969-12-31 23:30:00 UTC = -1800 seconds = -1800000000 micros
+        // Expected: floor_div(-1800000000, 3600000000) = -1
+        let ts =
+            TimestampMicrosecondArray::from(vec![Some(-1_800_000_000i64)]).with_timezone("UTC");
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), -1);
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_null() {
+        let udf = SparkHoursTransform::new();
+        let ts = TimestampMicrosecondArray::from(vec![None as Option<i64>]).with_timezone("UTC");
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert!(int_arr.is_null(0));
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_epoch_zero() {
+        let udf = SparkHoursTransform::new();
+        let ts = TimestampMicrosecondArray::from(vec![Some(0i64)]).with_timezone("UTC");
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), 0);
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_non_utc_timezone() {
+        // Spark's Hours partition transform evaluates absolute hours since epoch. Thus, a UTC
+        // timestamp of 1970-01-01 00:00:00 UTC (micros=0) maps to 0 hours, even if the
+        // timestamp array itself contains timezone metadata like Asia/Tokyo.
+        let udf = SparkHoursTransform::new();
+        let ts = TimestampMicrosecondArray::from(vec![Some(0i64)]).with_timezone("Asia/Tokyo");
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), 0);
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+
+    #[test]
+    fn test_hours_transform_ntz_ignores_timezone() {
+        // NTZ with micros=0 always returns 0 because NTZ is pure wall-clock time.
+        // There is no timezone offset logic applied to either TimestampType or NTZ.
+        let udf = SparkHoursTransform::new();
+        let ts = TimestampMicrosecondArray::from(vec![Some(0i64)]); // No timezone on array
+        let return_field = Arc::new(Field::new("hours_transform", DataType::Int32, true));
+        let args = ScalarFunctionArgs {
+            args: vec![ColumnarValue::Array(Arc::new(ts))],
+            number_rows: 1,
+            return_field,
+            config_options: Arc::new(ConfigOptions::default()),
+            arg_fields: vec![],
+        };
+        let result = udf.invoke_with_args(args).unwrap();
+        match result {
+            ColumnarValue::Array(arr) => {
+                let int_arr = arr.as_any().downcast_ref::<Int32Array>().unwrap();
+                assert_eq!(int_arr.value(0), 0); // NOT 9, because NTZ ignores timezone
+            }
+            _ => panic!("Expected array"),
+        }
+    }
+}

native/spark-expr/src/datetime_funcs/mod.rs

@@ -18,6 +18,7 @@
 mod date_diff;
 mod date_trunc;
 mod extract_date_part;
+mod hours;
 mod make_date;
 mod timestamp_trunc;
 mod unix_timestamp;
@@ -27,6 +28,7 @@ pub use date_trunc::SparkDateTrunc;
 pub use extract_date_part::SparkHour;
 pub use extract_date_part::SparkMinute;
 pub use extract_date_part::SparkSecond;
+pub use hours::SparkHoursTransform;
 pub use make_date::SparkMakeDate;
 pub use timestamp_trunc::TimestampTruncExpr;
 pub use unix_timestamp::SparkUnixTimestamp;

native/spark-expr/src/lib.rs

@@ -71,8 +71,8 @@ pub use comet_scalar_funcs::{
 };
 pub use csv_funcs::*;
 pub use datetime_funcs::{
-    SparkDateDiff, SparkDateTrunc, SparkHour, SparkMakeDate, SparkMinute, SparkSecond,
-    SparkUnixTimestamp, TimestampTruncExpr,
+    SparkDateDiff, SparkDateTrunc, SparkHour, SparkHoursTransform, SparkMakeDate, SparkMinute,
+    SparkSecond, SparkUnixTimestamp, TimestampTruncExpr,
 };
 pub use error::{decimal_overflow_error, SparkError, SparkErrorWithContext, SparkResult};
 pub use hash_funcs::*;