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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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@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 a
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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 th
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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 only
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Ah, this triggers the doc check. Updating.
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we can keep investigating the perf regression, this patch itself LGTM
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Looks we should fix the doc in PrefixSpan too. LGTM if the check pass.
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cc @hvanhovell
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Thanks a lot @gatorsmile
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+ * Licensed to the Apache Software Foundation (ASF)
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extends TaskContextImpl(stageI
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cc @kiszk,Can you review it again if you have some time?
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Ping @mridulm , would you please also take a review, thanks!
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thanks, merging to master!
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[SAPRK-25011][ML]add prefix to __all__ in fpm.py
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-25011
add prefix to __all__ in
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LGTM
Thanks! Merged to master.
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> Anybody else able to reproduce this failure? It succeeded on my developer
machine.
It worked for me, too. Let's see what a retest does.
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retest this please
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cc @BryanCutler and @icexelloss too since we recently discussed about
memory issue.
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retest this please
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Github user HyukjinKwon commented on the issue:
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retest this please
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Github user HyukjinKwon commented on the issue:
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Yup, looks the resent Kafka upgrade has an issue.
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