[SPARK-33277][PYSPARK][SQL] Use ContextAwareIterator to stop consumin…

…g after the task ends

### What changes were proposed in this pull request?

This is a retry of #30177.

This is not a complete fix, but it would take long time to complete (#30242).
As discussed offline, at least using `ContextAwareIterator` should be helpful enough for many cases.

As the Python evaluation consumes the parent iterator in a separate thread, it could consume more data from the parent even after the task ends and the parent is closed. Thus, we should use `ContextAwareIterator` to stop consuming after the task ends.

### Why are the changes needed?

Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.

E.g.,:

```py
spark.range(0, 100000, 1, 1).write.parquet(path)

spark.conf.set("spark.sql.columnVector.offheap.enabled", True)

def f(x):
    return 0

fUdf = udf(f, LongType())

spark.read.parquet(path).select(fUdf('id')).head()
```

This is because, the Python evaluation consumes the parent iterator in a separate thread and it consumes more data from the parent even after the task ends and the parent is closed. If an off-heap column vector exists in the parent iterator, it could cause segmentation fault which crashes the executor.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Added tests, and manually.

Closes #30899 from ueshin/issues/SPARK-33277/context_aware_iterator.

Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
(cherry picked from commit 5c9b421)
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>

core/src/main/scala/org/apache/spark/ContextAwareIterator.scala

@@ -0,0 +1,40 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark
+
+import org.apache.spark.annotation.DeveloperApi
+
+/**
+ * :: DeveloperApi ::
+ * A TaskContext aware iterator.
+ *
+ * As the Python evaluation consumes the parent iterator in a separate thread,
+ * it could consume more data from the parent even after the task ends and the parent is closed.
+ * If an off-heap access exists in the parent iterator, it could cause segmentation fault
+ * which crashes the executor.
+ * Thus, we should use [[ContextAwareIterator]] to stop consuming after the task ends.
+ */
+@DeveloperApi
+class ContextAwareIterator[+T](val context: TaskContext, val delegate: Iterator[T])
+  extends Iterator[T] {
+
+  override def hasNext: Boolean =
+    !context.isCompleted() && !context.isInterrupted() && delegate.hasNext
+
+  override def next(): T = delegate.next()
+}

sql/core/src/main/scala/org/apache/spark/sql/execution/python/EvalPythonExec.scala

@@ -21,7 +21,7 @@ import java.io.File
 
 import scala.collection.mutable.ArrayBuffer
 
-import org.apache.spark.{SparkEnv, TaskContext}
+import org.apache.spark.{ContextAwareIterator, SparkEnv, TaskContext}
 import org.apache.spark.api.python.ChainedPythonFunctions
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -88,6 +88,7 @@ abstract class EvalPythonExec(udfs: Seq[PythonUDF], resultAttrs: Seq[Attribute],
 
     inputRDD.mapPartitions { iter =>
       val context = TaskContext.get()
+      val contextAwareIterator = new ContextAwareIterator(context, iter)
 
       // The queue used to buffer input rows so we can drain it to
       // combine input with output from Python.
@@ -119,7 +120,7 @@ abstract class EvalPythonExec(udfs: Seq[PythonUDF], resultAttrs: Seq[Attribute],
       })
 
       // Add rows to queue to join later with the result.
-      val projectedRowIter = iter.map { inputRow =>
+      val projectedRowIter = contextAwareIterator.map { inputRow =>
         queue.add(inputRow.asInstanceOf[UnsafeRow])
         projection(inputRow)
       }

sql/core/src/main/scala/org/apache/spark/sql/execution/python/MapInPandasExec.scala

@@ -19,7 +19,7 @@ package org.apache.spark.sql.execution.python
 
 import scala.collection.JavaConverters._
 
-import org.apache.spark.TaskContext
+import org.apache.spark.{ContextAwareIterator, TaskContext}
 import org.apache.spark.api.python.{ChainedPythonFunctions, PythonEvalType}
 import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.catalyst.InternalRow
@@ -61,16 +61,17 @@ case class MapInPandasExec(
       val pythonRunnerConf = ArrowUtils.getPythonRunnerConfMap(conf)
       val outputTypes = child.schema
 
+      val context = TaskContext.get()
+      val contextAwareIterator = new ContextAwareIterator(context, inputIter)
+
       // Here we wrap it via another row so that Python sides understand it
       // as a DataFrame.
-      val wrappedIter = inputIter.map(InternalRow(_))
+      val wrappedIter = contextAwareIterator.map(InternalRow(_))
 
       // DO NOT use iter.grouped(). See BatchIterator.
       val batchIter =
         if (batchSize > 0) new BatchIterator(wrappedIter, batchSize) else Iterator(wrappedIter)
 
-      val context = TaskContext.get()
-
       val columnarBatchIter = new ArrowPythonRunner(
         chainedFunc,
         PythonEvalType.SQL_MAP_PANDAS_ITER_UDF,