ulysses-you commented on code in PR #57742:
URL: https://github.com/apache/spark/pull/57742#discussion_r3727582575
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/aggregate/HashAggregateExec.scala:
##########
@@ -845,29 +1158,56 @@ case class HashAggregateExec(
}
}
- val declareRowBuffer: String = if (isFastHashMapEnabled) {
- val fastRowType = if (isVectorizedHashMapEnabled) {
- classOf[MutableColumnarRow].getName
+ val declareRowBuffer: String = {
+ val declareBuffers = if (isFastHashMapEnabled) {
+ val fastRowType = if (isVectorizedHashMapEnabled) {
+ classOf[MutableColumnarRow].getName
+ } else {
+ "UnsafeRow"
+ }
+ s"""
+ |UnsafeRow $unsafeRowBuffer = null;
+ |$fastRowType $fastRowBuffer = null;
+ """.stripMargin
+ } else {
+ s"UnsafeRow $unsafeRowBuffer = null;"
+ }
+ val declareBypassed = if (adaptivePartialAggEnabled) {
+ s"boolean $adaptiveRowBypassedTerm = false;"
} else {
- "UnsafeRow"
+ ""
}
s"""
- |UnsafeRow $unsafeRowBuffer = null;
- |$fastRowType $fastRowBuffer = null;
+ |$declareBuffers
+ |$declareBypassed
""".stripMargin
- } else {
- s"UnsafeRow $unsafeRowBuffer = null;"
}
// We try to do hash map based in-memory aggregation first. If there is
not enough memory (the
// hash map will return null for new key), we spill the hash map to disk
to free memory, then
// continue to do in-memory aggregation and spilling until all the rows
had been processed.
// Finally, sort the spilled aggregate buffers by key, and merge them
together for same key.
+ //
+ // With adaptive partial aggregation, once pass-through is active
`updateRowInHashMap` fills the
+ // single-row buffer built above; we then emit `key ++ buffer` straight to
the parent so the row
+ // skips both the fast map and the regular map.
+ val emitPassThroughRow = if (adaptivePartialAggEnabled) {
+ val numBypassingRows = metricTerm(ctx, "numBypassingRows")
+ s"""
+ |if ($adaptiveRowBypassedTerm) {
+ | $numBypassingRows.add(1);
+ | $outputFunc(${unsafeRowKeyCode.value}, $unsafeRowBuffer);
Review Comment:
I'm not sure if we should promise such ordering. IMO, this case also can
happen during partial -> final, which is based on the shuffle read data
ordering when doing final merge, and the shuffle read data ordering is random.
also cc @cloud-fan what do you think ?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
