Feat: support bit_count function

native/spark-expr/src/bitwise_funcs/bitwise_count.rs

@@ -0,0 +1,103 @@
+// 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.
+
+use arrow::{array::*, datatypes::DataType};
+use datafusion::common::Result;
+use datafusion::{error::DataFusionError, logical_expr::ColumnarValue};
+use std::sync::Arc;
+
+macro_rules! compute_op {
+    ($OPERAND:expr, $DT:ident) => {{
+        let operand = $OPERAND
+            .as_any()
+            .downcast_ref::<$DT>()
+            .expect("compute_op failed to downcast array");
+
+        let result: Int32Array = operand
+            .iter()
+            .map(|x| x.map(|y| bit_count(y.into())))
+            .collect();
+
+        Ok(Arc::new(result))
+    }};
+}
+
+pub fn spark_bit_count(args: &[ColumnarValue]) -> Result<ColumnarValue> {
+    if args.len() != 1 {
+        return Err(DataFusionError::Internal(
+            "bit_count expects exactly one argument".to_string(),
+        ));
+    }
+    match &args[0] {
+        ColumnarValue::Array(array) => {
+            let result: Result<ArrayRef> = match array.data_type() {
+                DataType::Int8 | DataType::Boolean => compute_op!(array, Int8Array),
+                DataType::Int16 => compute_op!(array, Int16Array),
+                DataType::Int32 => compute_op!(array, Int32Array),
+                DataType::Int64 => compute_op!(array, Int64Array),
+                _ => Err(DataFusionError::Execution(format!(
+                    "Can't be evaluated because the expression's type is {:?}, not signed int",
+                    array.data_type(),
+                ))),
+            };
+            result.map(ColumnarValue::Array)
+        }
+        ColumnarValue::Scalar(_) => Err(DataFusionError::Internal(
+            "shouldn't go to bit_count scalar path".to_string(),
+        )),
+    }
+}
+
+// Here’s the equivalent Rust implementation of the bitCount function (similar to Apache Spark's bitCount for LongType)
+fn bit_count(i: i64) -> i32 {
+    let mut u = i as u64;
+    u = u - ((u >> 1) & 0x5555555555555555);
+    u = (u & 0x3333333333333333) + ((u >> 2) & 0x3333333333333333);
+    u = (u + (u >> 4)) & 0x0f0f0f0f0f0f0f0f;
+    u = u + (u >> 8);
+    u = u + (u >> 16);
+    u = u + (u >> 32);
+    (u as i32) & 0x7f
+}
+
+#[cfg(test)]
+mod tests {
+    use datafusion::common::{cast::as_int32_array, Result};
+
+    use super::*;
+
+    #[test]
+    fn bitwise_count_op() -> Result<()> {
+        let args = vec![ColumnarValue::Array(Arc::new(Int32Array::from(vec![
+            Some(1),
+            None,
+            Some(12345),
+            Some(89),
+            Some(-3456),
+        ])))];
+        let expected = &Int32Array::from(vec![Some(1), None, Some(6), Some(4), Some(54)]);
+
+        let ColumnarValue::Array(result) = spark_bit_count(&args)? else {
+            unreachable!()
+        };
+
+        let result = as_int32_array(&result).expect("failed to downcast to In32Array");
+        assert_eq!(result, expected);
+
+        Ok(())
+    }
+}

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

@@ -15,6 +15,8 @@
 // specific language governing permissions and limitations
 // under the License.
 
+mod bitwise_count;
 mod bitwise_not;
 
+pub use bitwise_count::spark_bit_count;
 pub use bitwise_not::{bitwise_not, BitwiseNotExpr};

native/spark-expr/src/comet_scalar_funcs.rs

@@ -17,10 +17,10 @@
 
 use crate::hash_funcs::*;
 use crate::{
-    spark_array_repeat, spark_ceil, spark_date_add, spark_date_sub, spark_decimal_div,
-    spark_decimal_integral_div, spark_floor, spark_hex, spark_isnan, spark_make_decimal,
-    spark_read_side_padding, spark_round, spark_rpad, spark_unhex, spark_unscaled_value,
-    SparkChrFunc,
+    spark_array_repeat, spark_bit_count, spark_ceil, spark_date_add, spark_date_sub,
+    spark_decimal_div, spark_decimal_integral_div, spark_floor, spark_hex, spark_isnan,
+    spark_make_decimal, spark_read_side_padding, spark_round, spark_rpad, spark_unhex,
+    spark_unscaled_value, SparkChrFunc,
 };
 use arrow::datatypes::DataType;
 use datafusion::common::{DataFusionError, Result as DataFusionResult};
@@ -145,6 +145,10 @@ pub fn create_comet_physical_fun(
             let func = Arc::new(spark_array_repeat);
             make_comet_scalar_udf!("array_repeat", func, without data_type)
         }
+        "bit_count" => {
+            let func = Arc::new(spark_bit_count);
+            make_comet_scalar_udf!("bit_count", func, without data_type)
+        }
         _ => registry.udf(fun_name).map_err(|e| {
             DataFusionError::Execution(format!(
                 "Function {fun_name} not found in the registry: {e}",

spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala

@@ -1655,6 +1655,12 @@ object QueryPlanSerde extends Logging with CometExprShim {
           binding,
           (builder, binaryExpr) => builder.setBitwiseXor(binaryExpr))
 
+      case BitwiseCount(child) =>
+        val childProto = exprToProto(child, inputs, binding)
+        val bitCountScalarExpr =
+          scalarFunctionExprToProtoWithReturnType("bit_count", IntegerType, childProto)
+        optExprWithInfo(bitCountScalarExpr, expr, expr.children: _*)
+
       case ShiftRight(left, right) =>
         // DataFusion bitwise shift right expression requires
         // same data type between left and right side

spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala

@@ -37,6 +37,7 @@ import org.apache.spark.sql.internal.SQLConf.SESSION_LOCAL_TIMEZONE
 import org.apache.spark.sql.types.{Decimal, DecimalType}
 
 import org.apache.comet.CometSparkSessionExtensions.isSpark40Plus
+import org.apache.comet.testing.{DataGenOptions, ParquetGenerator}
 
 class CometExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelper {
   import testImplicits._
@@ -90,6 +91,55 @@ class CometExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelper {
     }
   }
 
+  test("bitwise_count - min/max values") {
+    Seq(false, true).foreach { dictionary =>
+      withSQLConf("parquet.enable.dictionary" -> dictionary.toString) {
+        val table = "bitwise_count_test"
+        withTable(table) {
+          sql(s"create table $table(col1 long, col2 int, col3 short, col4 byte) using parquet")
+          sql(s"insert into $table values(1111, 2222, 17, 7)")
+          sql(
+            s"insert into $table values(${Long.MaxValue}, ${Int.MaxValue}, ${Short.MaxValue}, ${Byte.MaxValue})")
+          sql(
+            s"insert into $table values(${Long.MinValue}, ${Int.MinValue}, ${Short.MinValue}, ${Byte.MinValue})")
+
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(col1) FROM $table"))
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(col2) FROM $table"))
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(col3) FROM $table"))
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(col4) FROM $table"))
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(true) FROM $table"))
+          checkSparkAnswerAndOperator(sql(s"SELECT bit_count(false) FROM $table"))
+        }
+      }
+    }
+  }
+
+  test("bitwise_count - random values") {
+    withTempDir { dir =>
+      val path = new Path(dir.toURI.toString, "test.parquet")
+      val filename = path.toString
+      val random = new Random(42)
+      withSQLConf(CometConf.COMET_ENABLED.key -> "false") {
+        ParquetGenerator.makeParquetFile(
+          random,
+          spark,
+          filename,
+          10,
+          DataGenOptions(
+            allowNull = true,
+            generateNegativeZero = true,
+            generateArray = false,
+            generateStruct = false,
+            generateMap = false))
+      }
+      val table = spark.read.parquet(filename)
+      val df =
+        table.selectExpr("bit_count(c1)", "bit_count(c2)", "bit_count(c3)", "bit_count(c4)")
+
+      checkSparkAnswerAndOperator(df)
+    }
+  }
+
   test("bitwise shift with different left/right types") {
     Seq(false, true).foreach { dictionary =>
       withSQLConf("parquet.enable.dictionary" -> dictionary.toString) {