diff --git a/native/core/src/execution/jni_api.rs b/native/core/src/execution/jni_api.rs
index 1b11d9b25..8556dfdd2 100644
--- a/native/core/src/execution/jni_api.rs
+++ b/native/core/src/execution/jni_api.rs
@@ -495,7 +495,7 @@ 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,
diff --git a/native/core/src/execution/planner.rs b/native/core/src/execution/planner.rs
index bb1687607..e5a14cae0 100644
--- a/native/core/src/execution/planner.rs
+++ b/native/core/src/execution/planner.rs
@@ -93,6 +93,7 @@ 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,
@@ -147,6 +148,7 @@ 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>,
}
@@ -157,6 +159,7 @@ impl Default for PhysicalPlanner {
let execution_props = ExecutionProps::new();
Self {
exec_context_id: TEST_EXEC_CONTEXT_ID,
+ partition: 0,
execution_props,
session_ctx,
}
@@ -164,10 +167,11 @@ impl Default for PhysicalPlanner {
}
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,
}
@@ -176,6 +180,7 @@ 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),
}
@@ -845,6 +850,10 @@ impl PhysicalPlanner {
));
Ok(array_has_any_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
diff --git a/native/proto/src/proto/expr.proto b/native/proto/src/proto/expr.proto
index 71ad8cf3f..9484546d1 100644
--- a/native/proto/src/proto/expr.proto
+++ b/native/proto/src/proto/expr.proto
@@ -90,6 +90,7 @@ message Expr {
ArrayJoin array_join = 63;
BinaryExpr arrays_overlap = 64;
MathExpr integral_divide = 65;
+ UnaryExpr rand = 66;
}
}
diff --git a/native/spark-expr/src/lib.rs b/native/spark-expr/src/lib.rs
index ae8e639b3..c0d0cf0fe 100644
--- a/native/spark-expr/src/lib.rs
+++ b/native/spark-expr/src/lib.rs
@@ -48,6 +48,8 @@ mod string_funcs;
mod datetime_funcs;
pub use agg_funcs::*;
+pub mod rand;
+
pub use cast::{spark_cast, Cast, SparkCastOptions};
mod conditional_funcs;
mod conversion_funcs;
diff --git a/native/spark-expr/src/rand.rs b/native/spark-expr/src/rand.rs
new file mode 100644
index 000000000..e7034f227
--- /dev/null
+++ b/native/spark-expr/src/rand.rs
@@ -0,0 +1,270 @@
+// 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::hash_funcs::murmur3::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_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/91f3fdd25852b43095dd5273358fc394ffd11b66/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/07c9f7138affdf0d42ecdc30adcb854515569985/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/91f3fdd25852b43095dd5273358fc394ffd11b66/core/src/main/scala/org/apache/spark/util/random/XORShiftRandom.scala#L63
+/// https://github.com/scala/scala/blob/360d5da544d84b821c40e4662ad08703b51a44e1/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 for RandExpr {
+ fn eq(&self, other: &Self) -> bool {
+ self.seed.eq(&other.seed) && self.init_seed_shift == other.init_seed_shift
+ }
+}
+
+impl Eq for RandExpr {}
+
+impl Hash for RandExpr {
+ fn hash<H: Hasher>(&self, state: &mut H) {
+ self.children().hash(state);
+ }
+}
+
+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,
+ )))
+ }
+}
+
+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(())
+ }
+}
diff --git a/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala b/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
index f50a6606c..2e2827560 100644
--- a/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
+++ b/spark/src/main/scala/org/apache/comet/serde/QueryPlanSerde.scala
@@ -2008,6 +2008,15 @@ object QueryPlanSerde extends Logging with ShimQueryPlanSerde with CometExprShim
case _: ArraysOverlap => convert(CometArraysOverlap)
case _ @ArrayFilter(_, func) if func.children.head.isInstanceOf[IsNotNull] =>
convert(CometArrayCompact)
+
+ case Rand(child, _) =>
+ createUnaryExpr(
+ expr,
+ child,
+ inputs,
+ binding,
+ (builder, unaryExpr) => builder.setRand(unaryExpr))
+
case _ =>
withInfo(expr, s"${expr.prettyName} is not supported", expr.children: _*)
None
diff --git a/spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala b/spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala
index 4836b3604..7f171b7e3 100644
--- a/spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala
+++ b/spark/src/test/scala/org/apache/comet/CometExpressionSuite.scala
@@ -2734,4 +2734,23 @@ class CometExpressionSuite extends CometTestBase with AdaptiveSparkPlanHelper {
}
}
+ 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)
+ }
+ }
+ }
}