feat: rand expression support

native/core/src/execution/jni_api.rs

@@ -469,7 +469,7 @@ pub unsafe extern "system" fn Java_org_apache_comet_Native_executePlan(
         // query plan, we need to defer stream initialization to first time execution.
         if exec_context.root_op.is_none() {
             let start = Instant::now();
-            let planner = PhysicalPlanner::new(Arc::clone(&exec_context.session_ctx))
+            let planner = PhysicalPlanner::new(Arc::clone(&exec_context.session_ctx), partition)
                 .with_exec_id(exec_context_id);
             let (scans, root_op) = planner.create_plan(
                 &exec_context.spark_plan,

native/core/src/execution/planner.rs

@@ -83,6 +83,7 @@ use datafusion_comet_proto::{
     },
     spark_partitioning::{partitioning::PartitioningStruct, Partitioning as SparkPartitioning},
 };
+use datafusion_comet_spark_expr::rand::RandExpr;
 use datafusion_comet_spark_expr::{
     ArrayInsert, Avg, AvgDecimal, BitwiseNotExpr, Cast, CheckOverflow, Contains, Correlation,
     Covariance, CreateNamedStruct, DateTruncExpr, EndsWith, GetArrayStructFields, GetStructField,
@@ -128,6 +129,7 @@ pub const TEST_EXEC_CONTEXT_ID: i64 = -1;
 pub struct PhysicalPlanner {
     // The execution context id of this planner.
     exec_context_id: i64,
+    partition: i32,
     execution_props: ExecutionProps,
     session_ctx: Arc<SessionContext>,
 }
@@ -138,17 +140,19 @@ impl Default for PhysicalPlanner {
         let execution_props = ExecutionProps::new();
         Self {
             exec_context_id: TEST_EXEC_CONTEXT_ID,
+            partition: 0,
             execution_props,
             session_ctx,
         }
     }
 }
 
 impl PhysicalPlanner {
-    pub fn new(session_ctx: Arc<SessionContext>) -> Self {
+    pub fn new(session_ctx: Arc<SessionContext>, partition: i32) -> Self {
         let execution_props = ExecutionProps::new();
         Self {
             exec_context_id: TEST_EXEC_CONTEXT_ID,
+            partition,
             execution_props,
             session_ctx,
         }
@@ -157,6 +161,7 @@ impl PhysicalPlanner {
     pub fn with_exec_id(self, exec_context_id: i64) -> Self {
         Self {
             exec_context_id,
+            partition: self.partition,
             execution_props: self.execution_props,
             session_ctx: Arc::clone(&self.session_ctx),
         }
@@ -735,6 +740,10 @@ impl PhysicalPlanner {
                 ));
                 Ok(array_has_expr)
             }
+            ExprStruct::Rand(expr) => {
+                let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
+                Ok(Arc::new(RandExpr::new(child, self.partition)))
+            }
             expr => Err(ExecutionError::GeneralError(format!(
                 "Not implemented: {:?}",
                 expr

native/proto/src/proto/expr.proto

@@ -85,6 +85,7 @@ message Expr {
     BinaryExpr array_append = 58;
     ArrayInsert array_insert = 59;
     BinaryExpr array_contains = 60;
+    UnaryExpr rand = 61;
   }
 }
 

native/spark-expr/src/lib.rs

@@ -66,6 +66,8 @@ pub use normalize_nan::NormalizeNaNAndZero;
 mod variance;
 pub use variance::Variance;
 mod comet_scalar_funcs;
+pub mod rand;
+
 pub use cast::{spark_cast, Cast, SparkCastOptions};
 pub use comet_scalar_funcs::create_comet_physical_fun;
 pub use error::{SparkError, SparkResult};

native/spark-expr/src/rand.rs

@@ -0,0 +1,272 @@
+// 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 crate::spark_hash::spark_compatible_murmur3_hash;
+use arrow_array::builder::Float64Builder;
+use arrow_array::{Float64Array, RecordBatch};
+use arrow_schema::{DataType, Schema};
+use datafusion::logical_expr::ColumnarValue;
+use datafusion::physical_expr::PhysicalExpr;
+use datafusion::physical_expr_common::physical_expr::down_cast_any_ref;
+use datafusion_common::ScalarValue;
+use datafusion_common::{DataFusionError, Result};
+use std::any::Any;
+use std::fmt::Display;
+use std::hash::{Hash, Hasher};
+use std::sync::{Arc, Mutex};
+
+/// Adoption of the XOR-shift algorithm used in Apache Spark.
+/// See: https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala
+
+/// Normalization multiplier used in mapping from a random i64 value to the f64 interval [0.0, 1.0).
+/// Corresponds to the java implementation: https://github.com/openjdk/jdk/blob/master/src/java.base/share/classes/java/util/Random.java#L302)
+/// Due to the lack of hexadecimal float literals support in rust, the scientific notation is used instead.
+const DOUBLE_UNIT: f64 = 1.1102230246251565e-16;
+
+/// Spark-compatible initial seed which is actually a part of  the scala standard library murmurhash3 implementation.
+/// The references:
+/// https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala#L63
+/// https://github.com/scala/scala/blob/2.13.x/src/library/scala/util/hashing/MurmurHash3.scala#L331
+const SPARK_MURMUR_ARRAY_SEED: u32 = 0x3c074a61;
+
+#[derive(Debug, Clone)]
+struct XorShiftRandom {
+    seed: i64,
+}
+
+impl XorShiftRandom {
+    fn from_init_seed(init_seed: i64) -> Self {
+        XorShiftRandom {
+            seed: Self::init_seed(init_seed),
+        }
+    }
+
+    fn from_stored_seed(stored_seed: i64) -> Self {
+        XorShiftRandom { seed: stored_seed }
+    }
+
+    fn next(&mut self, bits: u8) -> i32 {
+        let mut next_seed = self.seed ^ (self.seed << 21);
+        next_seed ^= ((next_seed as u64) >> 35) as i64;
+        next_seed ^= next_seed << 4;
+        self.seed = next_seed;
+        (next_seed & ((1i64 << bits) - 1)) as i32
+    }
+
+    pub fn next_f64(&mut self) -> f64 {
+        let a = self.next(26) as i64;
+        let b = self.next(27) as i64;
+        ((a << 27) + b) as f64 * DOUBLE_UNIT
+    }
+
+    fn init_seed(init: i64) -> i64 {
+        let bytes_repr = init.to_be_bytes();
+        let low_bits = spark_compatible_murmur3_hash(bytes_repr, SPARK_MURMUR_ARRAY_SEED);
+        let high_bits = spark_compatible_murmur3_hash(bytes_repr, low_bits);
+        ((high_bits as i64) << 32) | (low_bits as i64 & 0xFFFFFFFFi64)
+    }
+}
+
+#[derive(Debug)]
+pub struct RandExpr {
+    seed: Arc<dyn PhysicalExpr>,
+    init_seed_shift: i32,
+    state_holder: Arc<Mutex<Option<i64>>>,
+}
+
+impl RandExpr {
+    pub fn new(seed: Arc<dyn PhysicalExpr>, init_seed_shift: i32) -> Self {
+        Self {
+            seed,
+            init_seed_shift,
+            state_holder: Arc::new(Mutex::new(None::<i64>)),
+        }
+    }
+
+    fn extract_init_state(seed: ScalarValue) -> Result<i64> {
+        if let ScalarValue::Int64(seed_opt) = seed.cast_to(&DataType::Int64)? {
+            Ok(seed_opt.unwrap_or(0))
+        } else {
+            Err(DataFusionError::Internal(
+                "unexpected execution branch".to_string(),
+            ))
+        }
+    }
+    fn evaluate_batch(&self, seed: ScalarValue, num_rows: usize) -> Result<ColumnarValue> {
+        let mut seed_state = self.state_holder.lock().unwrap();
+        let mut rnd = if seed_state.is_none() {
+            let init_seed = RandExpr::extract_init_state(seed)?;
+            let init_seed = init_seed.wrapping_add(self.init_seed_shift as i64);
+            *seed_state = Some(init_seed);
+            XorShiftRandom::from_init_seed(init_seed)
+        } else {
+            let stored_seed = seed_state.unwrap();
+            XorShiftRandom::from_stored_seed(stored_seed)
+        };
+
+        let mut arr_builder = Float64Builder::with_capacity(num_rows);
+        std::iter::repeat_with(|| rnd.next_f64())
+            .take(num_rows)
+            .for_each(|v| arr_builder.append_value(v));
+        let array_ref = Arc::new(Float64Array::from(arr_builder.finish()));
+        *seed_state = Some(rnd.seed);
+        Ok(ColumnarValue::Array(array_ref))
+    }
+}
+
+impl Display for RandExpr {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        write!(f, "RAND({})", self.seed)
+    }
+}
+
+impl PartialEq<dyn Any> for RandExpr {
+    fn eq(&self, other: &dyn Any) -> bool {
+        down_cast_any_ref(other)
+            .downcast_ref::<Self>()
+            .map(|x| self.seed.eq(&x.seed))
+            .unwrap_or(false)
+    }
+}
+
+impl PhysicalExpr for RandExpr {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn data_type(&self, _input_schema: &Schema) -> Result<DataType> {
+        Ok(DataType::Float64)
+    }
+
+    fn nullable(&self, _input_schema: &Schema) -> Result<bool> {
+        Ok(false)
+    }
+
+    fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
+        match self.seed.evaluate(batch)? {
+            ColumnarValue::Scalar(seed) => self.evaluate_batch(seed, batch.num_rows()),
+            ColumnarValue::Array(_arr) => Err(DataFusionError::NotImplemented(format!(
+                "Only literal seeds are supported for {}",
+                self
+            ))),
+        }
+    }
+
+    fn children(&self) -> Vec<&Arc<dyn PhysicalExpr>> {
+        vec![&self.seed]
+    }
+
+    fn with_new_children(
+        self: Arc<Self>,
+        children: Vec<Arc<dyn PhysicalExpr>>,
+    ) -> Result<Arc<dyn PhysicalExpr>> {
+        Ok(Arc::new(RandExpr::new(
+            Arc::clone(&children[0]),
+            self.init_seed_shift,
+        )))
+    }
+
+    fn dyn_hash(&self, state: &mut dyn Hasher) {
+        let mut s = state;
+        self.children().hash(&mut s);
+    }
+}
+
+pub fn rand(seed: Arc<dyn PhysicalExpr>, init_seed_shift: i32) -> Result<Arc<dyn PhysicalExpr>> {
+    Ok(Arc::new(RandExpr::new(seed, init_seed_shift)))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use arrow::{array::StringArray, compute::concat, datatypes::*};
+    use arrow_array::{Array, BooleanArray, Float64Array, Int64Array};
+    use datafusion_common::cast::as_float64_array;
+    use datafusion_physical_expr::expressions::lit;
+
+    const SPARK_SEED_42_FIRST_5: [f64; 5] = [
+        0.619189370225301,
+        0.5096018842446481,
+        0.8325259388871524,
+        0.26322809041172357,
+        0.6702867696264135,
+    ];
+
+    #[test]
+    fn test_rand_single_batch() -> Result<()> {
+        let schema = Schema::new(vec![Field::new("a", DataType::Utf8, true)]);
+        let data = StringArray::from(vec![Some("foo"), None, None, Some("bar"), None]);
+        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(data)])?;
+        let rand_expr = rand(lit(42), 0)?;
+        let result = rand_expr.evaluate(&batch)?.into_array(batch.num_rows())?;
+        let result = as_float64_array(&result)?;
+        let expected = &Float64Array::from(Vec::from(SPARK_SEED_42_FIRST_5));
+        assert_eq!(result, expected);
+        Ok(())
+    }
+
+    #[test]
+    fn test_rand_multi_batch() -> Result<()> {
+        let first_batch_schema = Schema::new(vec![Field::new("a", DataType::Int64, true)]);
+        let first_batch_data = Int64Array::from(vec![Some(42), None]);
+        let second_batch_schema = first_batch_schema.clone();
+        let second_batch_data = Int64Array::from(vec![None, Some(-42), None]);
+        let rand_expr = rand(lit(42), 0)?;
+        let first_batch = RecordBatch::try_new(
+            Arc::new(first_batch_schema),
+            vec![Arc::new(first_batch_data)],
+        )?;
+        let first_batch_result = rand_expr
+            .evaluate(&first_batch)?
+            .into_array(first_batch.num_rows())?;
+        let second_batch = RecordBatch::try_new(
+            Arc::new(second_batch_schema),
+            vec![Arc::new(second_batch_data)],
+        )?;
+        let second_batch_result = rand_expr
+            .evaluate(&second_batch)?
+            .into_array(second_batch.num_rows())?;
+        let result_arrays: Vec<&dyn Array> = vec![
+            as_float64_array(&first_batch_result)?,
+            as_float64_array(&second_batch_result)?,
+        ];
+        let result_arrays = &concat(&result_arrays)?;
+        let final_result = as_float64_array(result_arrays)?;
+        let expected = &Float64Array::from(Vec::from(SPARK_SEED_42_FIRST_5));
+        assert_eq!(final_result, expected);
+        Ok(())
+    }
+
+    #[test]
+    fn test_overflow_shift_seed() -> Result<()> {
+        let schema = Schema::new(vec![Field::new("a", DataType::Boolean, false)]);
+        let data = BooleanArray::from(vec![Some(true), Some(false)]);
+        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(data)])?;
+        let max_seed_and_shift_expr = rand(lit(i64::MAX), 1)?;
+        let min_seed_no_shift_expr = rand(lit(i64::MIN), 0)?;
+        let first_expr_result = max_seed_and_shift_expr
+            .evaluate(&batch)?
+            .into_array(batch.num_rows())?;
+        let first_expr_result = as_float64_array(&first_expr_result)?;
+        let second_expr_result = min_seed_no_shift_expr
+            .evaluate(&batch)?
+            .into_array(batch.num_rows())?;
+        let second_expr_result = as_float64_array(&second_expr_result)?;
+        assert_eq!(first_expr_result, second_expr_result);
+        Ok(())
+    }
+}

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

@@ -2278,6 +2278,10 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde with CometExprShim
             expr.children(1),
             inputs,
             (builder, binaryExpr) => builder.setArrayAppend(binaryExpr))
+
+        case Rand(child, _) =>
+          createUnaryExpr(child, inputs, (builder, unaryExpr) => builder.setRand(unaryExpr))
+
         case _ =>
           withInfo(expr, s"${expr.prettyName} is not supported", expr.children: _*)
           None

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

@@ -2529,4 +2529,24 @@ class CometExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelper {
         spark.sql("SELECT array_contains((CASE WHEN _2 =_3 THEN array(_4) END), _4) FROM t1"));
     }
   }
+
+  test("rand expression with random parameters") {
+    val partitionsNumber = Random.nextInt(10) + 1
+    val rowsNumber = Random.nextInt(500)
+    val seed = Random.nextLong()
+    // use this value to have both single-batch and multi-batch partitions
+    val cometBatchSize = math.max(1, math.ceil(rowsNumber.toDouble / partitionsNumber).toInt)
+    withSQLConf("spark.comet.batchSize" -> cometBatchSize.toString) {
+      withParquetDataFrame((0 until rowsNumber).map(Tuple1.apply)) { df =>
+        val dfWithRandParameters = df.repartition(partitionsNumber).withColumn("rnd", rand(seed))
+        checkSparkAnswer(dfWithRandParameters)
+        val dfWithOverflowSeed =
+          df.repartition(partitionsNumber).withColumn("rnd", rand(Long.MaxValue))
+        checkSparkAnswer(dfWithOverflowSeed)
+        val dfWithNullSeed =
+          df.repartition(partitionsNumber).selectExpr("_1", "rand(null) as rnd")
+        checkSparkAnswer(dfWithNullSeed)
+      }
+    }
+  }
 }