feat: randn expression support

docs/spark_expressions_support.md

@@ -351,6 +351,8 @@
  - [ ] input_file_name
  - [ ] monotonically_increasing_id
  - [ ] raise_error
+ - [x] rand
+ - [x] randn
  - [ ] spark_partition_id
  - [ ] typeof
  - [x] user

native/core/src/execution/planner.rs

@@ -103,7 +103,7 @@ use datafusion_comet_proto::{
 use datafusion_comet_spark_expr::{
     ArrayInsert, Avg, AvgDecimal, Cast, CheckOverflow, Correlation, Covariance, CreateNamedStruct,
     GetArrayStructFields, GetStructField, IfExpr, ListExtract, NormalizeNaNAndZero, RLike,
-    RandExpr, SparkCastOptions, Stddev, StringSpaceExpr, SubstringExpr, SumDecimal,
+    RandExpr, RandnExpr, SparkCastOptions, Stddev, StringSpaceExpr, SubstringExpr, SumDecimal,
     TimestampTruncExpr, ToJson, UnboundColumn, Variance,
 };
 use itertools::Itertools;
@@ -794,6 +794,10 @@ impl PhysicalPlanner {
                 let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
                 Ok(Arc::new(RandExpr::new(child, self.partition)))
             }
+            ExprStruct::Randn(expr) => {
+                let child = self.create_expr(expr.child.as_ref().unwrap(), input_schema)?;
+                Ok(Arc::new(RandnExpr::new(child, self.partition)))
+            }
             expr => Err(GeneralError(format!("Not implemented: {expr:?}"))),
         }
     }

native/proto/src/proto/expr.proto

@@ -81,6 +81,7 @@ message Expr {
     MathExpr integral_divide = 59;
     ToPrettyString to_pretty_string = 60;
     UnaryExpr rand = 61;
+    UnaryExpr randn = 62;
   }
 }
 

native/spark-expr/src/nondetermenistic_funcs/internal/mod.rs

@@ -0,0 +1,21 @@
+// 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.
+
+mod rand_utils;
+
+pub use rand_utils::evaluate_batch_for_rand;
+pub use rand_utils::StatefulSeedValueGenerator;

native/spark-expr/src/nondetermenistic_funcs/internal/rand_utils.rs

@@ -0,0 +1,74 @@
+// 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::{Float64Array, Float64Builder};
+use arrow::datatypes::DataType;
+use datafusion::common::{DataFusionError, ScalarValue};
+use datafusion::logical_expr::ColumnarValue;
+use std::ops::Deref;
+use std::sync::{Arc, Mutex};
+
+fn extract_seed_from_scalar_value(seed: &ScalarValue) -> datafusion::common::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(),
+        ))
+    }
+}
+
+pub fn evaluate_batch_for_rand<R, S>(
+    state_holder: &Arc<Mutex<Option<S>>>,
+    seed: ScalarValue,
+    init_seed_shift: i64,
+    num_rows: usize,
+) -> datafusion::common::Result<ColumnarValue>
+where
+    R: StatefulSeedValueGenerator<S, f64>,
+    S: Copy,
+{
+    let seed_state = state_holder.lock().unwrap();
+    let init = extract_seed_from_scalar_value(&seed)?.wrapping_add(init_seed_shift);
+    let mut rnd = R::from_state_ref(seed_state, init);
+    let mut arr_builder = Float64Builder::with_capacity(num_rows);
+    std::iter::repeat_with(|| rnd.next_value())
+        .take(num_rows)
+        .for_each(|v| arr_builder.append_value(v));
+    let array_ref = Arc::new(Float64Array::from(arr_builder.finish()));
+    let mut seed_state = state_holder.lock().unwrap();
+    seed_state.replace(rnd.get_current_state());
+    Ok(ColumnarValue::Array(array_ref))
+}
+
+pub trait StatefulSeedValueGenerator<State: Copy, Value>: Sized {
+    fn from_init_seed(init_seed: i64) -> Self;
+
+    fn from_stored_state(stored_state: State) -> Self;
+
+    fn next_value(&mut self) -> Value;
+
+    fn get_current_state(&self) -> State;
+
+    fn from_state_ref(state: impl Deref<Target = Option<State>>, init_value: i64) -> Self {
+        if state.is_none() {
+            Self::from_init_seed(init_value)
+        } else {
+            Self::from_stored_state(state.unwrap())
+        }
+    }
+}

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

@@ -15,6 +15,9 @@
 // specific language governing permissions and limitations
 // under the License.
 
+pub mod internal;
 pub mod rand;
+pub mod randn;
 
 pub use rand::RandExpr;
+pub use randn::RandnExpr;

native/spark-expr/src/nondetermenistic_funcs/rand.rs

@@ -16,10 +16,11 @@
 // under the License.
 
 use crate::hash_funcs::murmur3::spark_compatible_murmur3_hash;
-use arrow::array::{Float64Array, Float64Builder, RecordBatch};
+
+use crate::internal::{evaluate_batch_for_rand, StatefulSeedValueGenerator};
+use arrow::array::RecordBatch;
 use arrow::datatypes::{DataType, Schema};
 use datafusion::common::Result;
-use datafusion::common::ScalarValue;
 use datafusion::error::DataFusionError;
 use datafusion::logical_expr::ColumnarValue;
 use datafusion::physical_expr::PhysicalExpr;
@@ -42,21 +43,11 @@ const DOUBLE_UNIT: f64 = 1.1102230246251565e-16;
 const SPARK_MURMUR_ARRAY_SEED: u32 = 0x3c074a61;
 
 #[derive(Debug, Clone)]
-struct XorShiftRandom {
-    seed: i64,
+pub(crate) struct XorShiftRandom {
+    pub(crate) 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;
@@ -70,12 +61,27 @@ impl XorShiftRandom {
         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();
+impl StatefulSeedValueGenerator<i64, f64> for XorShiftRandom {
+    fn from_init_seed(init_seed: i64) -> Self {
+        let bytes_repr = init_seed.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)
+        let init_seed = ((high_bits as i64) << 32) | (low_bits as i64 & 0xFFFFFFFFi64);
+        XorShiftRandom { seed: init_seed }
+    }
+
+    fn from_stored_state(stored_state: i64) -> Self {
+        XorShiftRandom { seed: stored_state }
+    }
+
+    fn next_value(&mut self) -> f64 {
+        self.next_f64()
+    }
+
+    fn get_current_state(&self) -> i64 {
+        self.seed
     }
 }
 
@@ -94,36 +100,6 @@ impl RandExpr {
             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 {
@@ -161,7 +137,12 @@ impl PhysicalExpr for RandExpr {
 
     fn evaluate(&self, batch: &RecordBatch) -> Result<ColumnarValue> {
         match self.seed.evaluate(batch)? {
-            ColumnarValue::Scalar(seed) => self.evaluate_batch(seed, batch.num_rows()),
+            ColumnarValue::Scalar(seed) => evaluate_batch_for_rand::<XorShiftRandom, i64>(
+                &self.state_holder,
+                seed,
+                self.init_seed_shift as i64,
+                batch.num_rows(),
+            ),
             ColumnarValue::Array(_arr) => Err(DataFusionError::NotImplemented(format!(
                 "Only literal seeds are supported for {self}"
             ))),
@@ -194,7 +175,7 @@ pub fn rand(seed: Arc<dyn PhysicalExpr>, init_seed_shift: i32) -> Result<Arc<dyn
 #[cfg(test)]
 mod tests {
     use super::*;
-    use arrow::array::{Array, BooleanArray, Int64Array};
+    use arrow::array::{Array, BooleanArray, Float64Array, Int64Array};
     use arrow::{array::StringArray, compute::concat, datatypes::*};
     use datafusion::common::cast::as_float64_array;
     use datafusion::physical_expr::expressions::lit;