Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/18031#discussion_r117606196
--- Diff: core/src/main/scala/org/apache/spark/scheduler/MapStatus.scala ---
@@ -121,48 +126,69 @@ private[spark] class CompressedMapStatus(
}
/**
- * A [[MapStatus]] implementation that only stores the average size of
non-empty blocks,
- * plus a bitmap for tracking which blocks are empty.
+ * A [[MapStatus]] implementation that stores the accurate size of huge
blocks, which are larger
+ * than both spark.shuffle.accurateBlockThreshold and
+ * spark.shuffle.accurateBlockThresholdByTimesAverage * averageSize. It
stores the
+ * average size of other non-empty blocks, plus a bitmap for tracking
which blocks are empty.
*
* @param loc location where the task is being executed
* @param numNonEmptyBlocks the number of non-empty blocks
* @param emptyBlocks a bitmap tracking which blocks are empty
* @param avgSize average size of the non-empty blocks
+ * @param hugeBlockSizes sizes of huge blocks by their reduceId.
*/
private[spark] class HighlyCompressedMapStatus private (
private[this] var loc: BlockManagerId,
private[this] var numNonEmptyBlocks: Int,
private[this] var emptyBlocks: RoaringBitmap,
- private[this] var avgSize: Long)
+ private[this] var avgSize: Long,
+ @transient private var hugeBlockSizes: Map[Int, Byte])
--- End diff --
I go through part of codes in #16989. It seems to me that If we want is to
know which shuffle request should be go to disk instead of memory, do we need
to record the mapping of block ids and accurate sizes?
A simpler approach can be adding a bitmap for hugeBlocks. And we can simply
fetch those blocks into disk. Another benefit by doing this is to avoid
introducing another config `REDUCER_MAX_REQ_SIZE_SHUFFLE_TO_MEM` to decide
which blocks going to disk.
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