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You can have `select * from (select a, a as c from table1 where a > 10) t
where a > c`
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> While I say these performance differences, I do not understand why these
difference occurs completely. That is why I said "I have not found the root
cause".
Yes, I am in the same
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Github user tgravescs commented on the issue:
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yeah we should file a separate jira to look at the shuffle output.I'm
running a few stress tests and will let you know how those go.
could you file a jira for that and link to this
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[SPARK-25132][SQL][FOLLOW-UP] The behavior must be consistent to do the
conversion
## What changes were proposed in this pull request?
parquet data source tables and hive parquet tables have
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Also, cc @gatorsmile .
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@kiszk yes, I know. Moving `checkedCast` makes a big difference, but if you
just move it, without the other changes, there is no perf gain (at least this
is what I found in my experiments).
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@@ -1208,9 +1208,38 @@ object
Github user mgaido91 commented on the issue:
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>This does fix the regression, will we also lose the perf speed up too?
IIUC we did observe significant perf boost when introducing the memory block.
@cloud-fan I checked the original PR
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LGTM pending tests.
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@mgaido91 thanks, interestingly I did experiments with similar code in my
box.
While I am using the linux box, I can confirm the performance improvement
(or performance recover).
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gentle ping @maropu, could you help to review this? I'll keep follow up
this.
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LGTM
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--- Diff: docs/running-on-kubernetes.md ---
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spark.kubernetes.driver.volumes.persistentVolumeClaim.checkpointpvc.options.clai
Github user kiszk commented on the issue:
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Thank you for adding a description for the conf. Would it be possible to
add missing descriptions for other confs in `spark.network.crypto.*`?
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--- Diff: docs/monitoring.md ---
@@ -388,6 +388,163 @@ value triggering garbage collection on jobs, and
`spark.ui.retainedStages` that
Note that the
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[WIP][SPARK-23866][SQL] Support partition filters in ALTER TABLE DROP
PARTITION
## What changes were proposed in this pull request?
Hive has been supporting for a while the ability of
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+/*
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@@ -414,17 +468,37 @@ private[kafka010]
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@@ -754,11 +754,14 @@ class Analyzer(
* a
Github user kiszk commented on the issue:
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In addition to [your
commit](https://github.com/apache/spark/pull/22338/commits/91adce590461dda885d88319a700a775e63f9ce6),
I applied the following change, basically use `MemoryBlock` in
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retest this please
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you're right @kiszk. Let me update the PR accordingly in order to narrow
down the change/problem, thanks.
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I think it should be fine to make `ContextBarrierState` private[spark] to
test it, WDYT @mengxr ?
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@@ -65,7 +65,7 @@ private[spark] class BarrierCoordinator(
// Record
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--- Diff: python/pyspark/sql/functions.py ---
@@ -2804,6 +2804,22 @@ def pandas_udf(f=None, returnType=None,
functionType=None):
| 1|1.5|
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While I say these performance differences, I do not understand why these
difference occurs completely. That is why I said "I have not found the root
cause".
Let us narrow down the problem and
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@cloud-fan I've just sent a PR (#22343) for this.
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I like to add description for metrics.
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@maropu I think, this will be considered post 2.4. So we have some time. I
am now exploring options to plan better at physical level.
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`HiveExternalCatalogVersionsSuite` downloads and executes old Spark in
their directory. However, it gave the child the followings. None of them is
expected here. Especially,
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https://github.com/apache/spark/pull/22338
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Github user gaborgsomogyi commented on a diff in the pull request:
https://github.com/apache/spark/pull/22138#discussion_r215275456
--- Diff:
external/kafka-0-10-sql/src/main/scala/org/apache/spark/sql/kafka010/InternalKafkaConsumerPool.scala
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@@ -0,0 +1,241 @@
+/*
+ *
Github user asfgit closed the pull request at:
https://github.com/apache/spark/pull/22218
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