Github user mridulm commented on a diff in the pull request:
https://github.com/apache/spark/pull/18031#discussion_r117625287
--- 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 --
@viirya We had this discussion before in the earlier PR (which this is
split from).
maxSizeInFlight meant to control how much data can be fetched in parallel
and tuned based on network throughput and not memory (though currently, they
are directly dependent due to implementation detail).
In reality, it is fairly small compared to what can be held in memory (48mb
is default iirc) - since the memory and IO subsystems have different
characteristics, using same config to control behavior in both will lead to
suboptimal behavior (for example, large memory systems where large amounts can
be held in memory, but network bandwidth is not propotionally higher).
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