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Merged to master.
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Github user HyukjinKwon commented on the issue:
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I am not sure. ML is not my area but I am pretty sure who you know are
basically who I know .. ping me if that's minor or trivial like this. I can
review and merge.
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Github user viirya commented on the issue:
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The regression happens at writing. Looks like when benchmarking writing
time, we don't use `df.count`?
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I noticed that the benchmark uses `df.count`, is it possible that column
pruning has some issues in master?
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Jenkins, retest this please.
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Github user cloud-fan commented on the issue:
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@rdblue I have documented the object reuse behavior and ask data source to
handle it, please take a look, thanks!
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> Also, if there shouldn't exist two active attempts at the same time for a
barrier stage, maybe we should store attemptId as a state variable. Basically,
if we see a new attempt ID, we should
Github user hhbyyh commented on the issue:
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BTW, @HyukjinKwon, do you know who's still reviewing the ML PRs? I have a
few old PRs and I really want to know which're considered meaningful.
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LGTM
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Also, if there shouldn't exist two active attempts at the same time for a
barrier stage, maybe we should store attemptId as a state variable. Basically,
if we see a new attempt ID, we should abort
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Thanks for the review @HyukjinKwon.
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Here is what I mean:
~~~scala
case class ContextBarrierId(stageId: Int, stageAttemptId: Int)
class ContextBarrierState(val numTasks: Int) {
private var epoch: Int = 0
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Ah, finally I can reproduce this. It needs to allocate the array feature
with length 16000. I was reducing it to 1600 and it largely relieve the
regression. `com.databricks.spark.avro` is faster
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