zhangfengcdt commented on code in PR #7316:
URL: https://github.com/apache/datafusion-comet/pull/7316#discussion_r4445981914


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native/core/src/execution/planner/scalar_expansion.rs:
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@@ -0,0 +1,285 @@
+// 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.
+
+//! Bounds the rows per batch under an operator that expands a large string 
scalar.
+//!
+//! An aggregate, a hash partitioner and most string kernels expand a scalar 
argument to the
+//! batch's length: a 256 KiB string over the default 8192-row batch needs 2 
GiB of value bytes,
+//! past the 32-bit offsets of Arrow's `Utf8` and `Binary` arrays. Spark 
evaluates such scalars
+//! row by row and never materializes the column. See
+//! <https://github.com/apache/datafusion-comet/issues/7247>.
+//!
+//! The bound is enforced right under the operator that evaluates the 
expressions, with a
+//! [`BatchSplitExec`], because the operators below do not keep the session's 
batch size: a
+//! filter coalesces its output toward DataFusion's default size and a window 
emits its whole
+//! partition.
+
+use std::sync::Arc;
+
+use arrow::datatypes::{DataType, Schema};
+use arrow::record_batch::{RecordBatch, RecordBatchOptions};
+use datafusion::common::ScalarValue;
+use datafusion::logical_expr::{ColumnarValue, Volatility};
+use datafusion::physical_expr::expressions::{Column, InListExpr};
+use datafusion::physical_expr::{PhysicalExpr, ScalarFunctionExpr};
+use datafusion::physical_plan::ExecutionPlan;
+use datafusion::prelude::SessionConfig;
+use datafusion_comet_operators::BatchSplitExec;
+use datafusion_comet_spark_expr::jvm_udf::JvmScalarUdfExpr;
+use 
datafusion_comet_spark_expr::monotonically_increasing_id::MonotonicallyIncreasingId;
+use datafusion_comet_spark_expr::{
+    RandExpr, RandStrExpr, RandnExpr, ShuffleExpr, Subquery, UuidExpr,
+};
+
+use super::{PhysicalPlanner, TEST_EXEC_CONTEXT_ID};
+
+/// The most value bytes the string and binary scalars of one operator may 
take once expanded to
+/// a batch's length. Arrow's 32-bit offsets make 2 GiB the hard limit; half 
of it leaves room for
+/// kernels that append to the expanded value, such as `concat`, and matches 
what an Arrow Java
+/// vector can hold before its reallocation fails.
+const SCALAR_EXPANSION_BYTES: usize = 1 << 30;
+
+/// The most rows per batch at which `scalar_bytes`, expanded to the batch's 
length, stay within
+/// `SCALAR_EXPANSION_BYTES`; unbounded when there is nothing to expand.
+fn scalar_rows_cap(scalar_bytes: usize) -> usize {
+    if scalar_bytes == 0 {
+        return usize::MAX;
+    }
+    (SCALAR_EXPANSION_BYTES / scalar_bytes).max(1)
+}
+
+impl PhysicalPlanner {
+    /// Wraps `input` so that the batches it emits keep the string and binary 
scalars in `exprs`,
+    /// expanded to a batch's length, within `SCALAR_EXPANSION_BYTES`. Returns 
`input` as is when
+    /// the widest batch it can emit already does.
+    pub(crate) fn bound_scalar_expansion<'a>(
+        &self,
+        input: Arc<dyn ExecutionPlan>,
+        exprs: impl IntoIterator<Item = &'a Arc<dyn PhysicalExpr>>,
+    ) -> Arc<dyn ExecutionPlan> {
+        let schema = input.schema();
+        let bytes: usize = exprs
+            .into_iter()
+            .map(|expr| self.expanded_scalar_bytes(expr, &schema))
+            .sum();
+        let cap = scalar_rows_cap(bytes);
+        let batch_size = self.session_ctx.copied_config().batch_size();
+        // A filter below coalesces toward DataFusion's default batch size 
whatever the session's
+        // is, so a session batch size under the default does not bound what 
arrives here.
+        let widest_input = 
batch_size.max(SessionConfig::default().batch_size());
+        if cap < widest_input {

Review Comment:
   Should be fixed now. The split no longer depends on an assumed upstream 
width. Also, I added your 64 KiB over 32768-row window query to the test.



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