joan38 commented on code in PR #50474:
URL: https://github.com/apache/spark/pull/50474#discussion_r2044980777
##########
core/src/main/scala/org/apache/spark/storage/ShuffleBlockFetcherIterator.scala:
##########
@@ -1014,39 +1014,44 @@ final class ShuffleBlockFetcherIterator(
// a SuccessFetchResult or a FailureFetchResult.
result = null
- case PushMergedLocalMetaFetchResult(
- shuffleId, shuffleMergeId, reduceId, bitmaps, localDirs) =>
- // Fetch push-merged-local shuffle block data as multiple shuffle
chunks
- val shuffleBlockId = ShuffleMergedBlockId(shuffleId,
shuffleMergeId, reduceId)
- try {
- val bufs: Seq[ManagedBuffer] =
blockManager.getLocalMergedBlockData(shuffleBlockId,
- localDirs)
- // Since the request for local block meta completed
successfully, numBlocksToFetch
- // is decremented.
- numBlocksToFetch -= 1
- // Update total number of blocks to fetch, reflecting the
multiple local shuffle
- // chunks.
- numBlocksToFetch += bufs.size
- bufs.zipWithIndex.foreach { case (buf, chunkId) =>
- buf.retain()
- val shuffleChunkId = ShuffleBlockChunkId(shuffleId,
shuffleMergeId, reduceId,
- chunkId)
- pushBasedFetchHelper.addChunk(shuffleChunkId, bitmaps(chunkId))
- results.put(SuccessFetchResult(shuffleChunkId,
SHUFFLE_PUSH_MAP_ID,
- pushBasedFetchHelper.localShuffleMergerBlockMgrId,
buf.size(), buf,
- isNetworkReqDone = false))
- }
- } catch {
- case e: Exception =>
- // If we see an exception with reading push-merged-local index
file, we fallback
- // to fetch the original blocks. We do not report block fetch
failure
- // and will continue with the remaining local block read.
- logWarning("Error occurred while reading push-merged-local
index, " +
- "prepare to fetch the original blocks", e)
- pushBasedFetchHelper.initiateFallbackFetchForPushMergedBlock(
- shuffleBlockId,
pushBasedFetchHelper.localShuffleMergerBlockMgrId)
+ case PushMergedLocalMetaFetchResult(
Review Comment:
Ok @LuciferYang. So I'll wait for you guys to process Spark 4 and then
you'll ping back on this PR?
In the meantime I'd recommend watching [this
talk](https://youtu.be/Qeavi9M65Qw) from @bishabosha especially [the DataFrame
demo](https://youtu.be/Qeavi9M65Qw?si=tQxtybnOB9P9GMvk&t=1145) and [the example
project](https://github.com/bishabosha/scalar-2025/blob/main/dataframe/test/demo.scala).
Thanks for the help!
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