Github user kiszk commented on a diff in the pull request:
https://github.com/apache/spark/pull/17122#discussion_r104467009
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/WholeStageCodegenExec.scala
---
@@ -206,6 +206,21 @@ trait CodegenSupport extends SparkPlan {
def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode):
String = {
throw new UnsupportedOperationException
}
+
+ /**
+ * For optimization to suppress shouldStop() in a loop of
WholeStageCodegen.
+ * Returning true means we need to insert shouldStop() into the loop
producing rows, if any.
+ */
+ def isShouldStopRequired: Boolean = {
+ return shouldStopRequired && !(this.parent != null &&
!this.parent.isShouldStopRequired)
+ }
+
+ /**
+ * Set to false if this plan consumes all rows produced by children but
doesn't output row
+ * to buffer by calling append(), so the children don't require
shouldStop()
+ * in the loop of producing rows.
+ */
+ protected def shouldStopRequired: Boolean = true
--- End diff --
I cannot see performance improvement on Sort. I think there are two reasons
for this result.
One is that the loop body is too large. At the inner-most loop, a
[`insertRow`
method](https://github.com/apache/spark/blob/master/sql/catalyst/src/main/java/org/apache/spark/sql/execution/UnsafeExternalRowSorter.java#L107-L120)
is called. I think that the size of this method is too large to facilitate
loop optimizations.
The other is that the hotspot method is not here. I guess that the hotspot
method may be `sort()` at line 154.
Here is the generated code.
```java
/* 114 */ while (true) {
/* 115 */ long range_range = range_batchEnd - range_number;
/* 116 */ if (range_range != 0L) {
/* 117 */ int range_localEnd = (int)(range_range / -1L);
/* 118 */ for (int range_localIdx = 0; range_localIdx <
range_localEnd; range_localIdx++) {
/* 119 */ long range_value = ((long)range_localIdx * -1L) +
range_number;
/* 120 */
/* 121 */ range_rowWriter.write(0, range_value);
/* 122 */ sort_sorter.insertRow((UnsafeRow)range_result);
/* 123 */
/* 124 */ // shouldStop check is eliminated
/* 125 */ }
/* 126 */ range_number = range_batchEnd;
/* 127 */ }
/* 128 */
/* 129 */ if (range_taskContext.isInterrupted()) {
/* 130 */ throw new TaskKilledException();
/* 131 */ }
/* 132 */
/* 133 */ long range_nextBatchTodo;
/* 134 */ if (range_numElementsTodo > 1000L) {
/* 135 */ range_nextBatchTodo = 1000L;
/* 136 */ range_numElementsTodo -= 1000L;
/* 137 */ } else {
/* 138 */ range_nextBatchTodo = range_numElementsTodo;
/* 139 */ range_numElementsTodo = 0;
/* 140 */ if (range_nextBatchTodo == 0) break;
/* 141 */ }
/* 142 */ range_numOutputRows.add(range_nextBatchTodo);
/* 143 */ range_inputMetrics.incRecordsRead(range_nextBatchTodo);
/* 144 */
/* 145 */ range_batchEnd += range_nextBatchTodo * -1L;
/* 146 */ }
/* 147 */
/* 148 */ }
/* 149 */
/* 150 */ protected void processNext() throws java.io.IOException {
/* 151 */ if (sort_needToSort) {
/* 152 */ long sort_spillSizeBefore =
sort_metrics.memoryBytesSpilled();
/* 153 */ sort_addToSorter();
/* 154 */ sort_sortedIter = sort_sorter.sort();
/* 155 */ sort_sortTime.add(sort_sorter.getSortTimeNanos() / 1000000);
/* 156 */ sort_peakMemory.add(sort_sorter.getPeakMemoryUsage());
/* 157 */ sort_spillSize.add(sort_metrics.memoryBytesSpilled() -
sort_spillSizeBefore);
/* 158 */
sort_metrics.incPeakExecutionMemory(sort_sorter.getPeakMemoryUsage());
/* 159 */ sort_needToSort = false;
/* 160 */ }
/* 161 */
/* 162 */ while (sort_sortedIter.hasNext()) {
/* 163 */ UnsafeRow sort_outputRow =
(UnsafeRow)sort_sortedIter.next();
/* 164 */
/* 165 */ append(sort_outputRow);
/* 166 */
/* 167 */ if (shouldStop()) return;
/* 168 */ }
/* 169 */ }
```
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