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--- Diff:
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---
@@ -43,11 +43,20 @@ import
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---
@@ -125,7 +129,7 @@ case class DescribeFunctionCommand(
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Github user kiszk commented on the issue:
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@davies @rxin, would it be possible to review this?
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Github user Astralidea commented on the issue:
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@lw-lin
Thanks for you reply. In my private cluster running spark is a little
different. (I start drivr & executor by myself)
I had try maxRegisteredWaitingTime, but I had not try
Github user gatorsmile commented on the issue:
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LGTM, since the scope of this PR is just refactoring.
Let me first post the existing code for `outputPartitioning` in
`ExpandExec`:
```Scala
// The GroupExpressions can output
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@@ -91,6 +91,16 @@ class OrcQuerySuite extends QueryTest with
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@@ -246,6 +246,9 @@ private[hive] trait HiveInspectors {
*
Github user ajbozarth commented on the issue:
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@srowen I addressed most of your comments except the one about the
try-finally I commented on above
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Spark Streaming would do a very simple dummy job ensure that all slaves
have registered before scheduling the `Receiver`s; please see
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--- Diff: core/src/test/scala/org/apache/spark/ui/UISeleniumSuite.scala ---
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Github user jiangxb1987 commented on the issue:
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@tejasapatil @rxin I've addressed most of your comments, thanks for
reviewing this!
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*
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closing this in favor of SPARK-17924
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Merging to master! Thanks!
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--- Diff:
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+/*
+ * Licensed to the Apache
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Github user rxin commented on the issue:
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It'd be great to move those as well!
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Github user viirya commented on the issue:
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In practice, setting the `outputPartitioning` of a physical plan like
`ExpandExec` to `child.outputPartitioning` doesn't cause any real problem, even
this physical plan doesn't keep the same row
Github user Astralidea commented on a diff in the pull request:
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@@ -440,7 +430,10 @@ class ReceiverTracker(ssc:
Github user Astralidea commented on the issue:
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@srowen But in my cluster I tested 10 times. 9 times successed, 1 time
failed.
Why not necessary? receiver balance scheduler affect performance.
If new executor delay add to driver.
Github user marmbrus commented on the issue:
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It would be really nice to fail in analysis rather than execution. What if
it only fails after hours of computation? As a user I'd be upset. I'm also
concerned they will think it's a spark bug.
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*
Github user viirya commented on the issue:
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@tejasapatil yeah, that is correct. however, I am wondering if we can say
this `ExpandExec` have the same distribution of rows as its child...because it
even doesn't have the `col`...
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@rxin I've moved the testcases added in this PR to an query file test, do
we need to move other test cases for `ROLLUP/CUBE/GROUPING-SETS` too? Currently
in `SQLQuerySuite` we have the
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Github user tejasapatil commented on the issue:
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@viirya : As per my understanding, if the child operator emits `col`, after
applying `ExpandExec`, the output is `col'`. The original child partitioning
being over `col`, `ExpandExec` does not
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LGTM pending test
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Github user viirya commented on the issue:
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@rxin yeah, I am curious why `ExpandExec` and `GenerateExec` have different
`outputPartitioning`...
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Github user viirya commented on the issue:
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@tejasapatil I see there is 1:1 mapping among output partition of child
operator and output partition of `ExpandExec`.
For example we have an Expand applying on a data set like col: [1, 2,
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@viirya
>> However, if its child has certain partition such as HashPartition, after
ExpandExec it becomes a UnknownPartitioning
The notion of `Partitioning` in Spark is the
Github user rxin commented on the issue:
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The current thing LGTM.
cc @yhuai do you have any other feedback?
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Github user tejasapatil commented on the issue:
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@viirya
>> In the table in the description, CoalesceExec output UnknownPartitioning
Yes. Since partitions == 1 is a corner case, I did not put that in the
table. If you look
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Thanks - merging in. Can you close this?
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Github user shivaram commented on the issue:
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Jenkins, retest this please
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Github user rxin commented on the issue:
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@viirya of course if you say coalesce(1) it is a single partition -- any
operator that changes partition to 1 partition is single partition.
For Expand isn't it just the same as Generate?
Github user viirya commented on the issue:
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@rxin In the table in the description, `CoalesceExec` output
`UnknownPartitioning`, actually it can be `SinglePartition` if what you do is
`coalesce(1)`.
`ExpandExec` doesn't actually move
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Github user Yunni commented on the issue:
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Thanks @jkbradley. I have removed BitSampling and SignRandomProjection for
a follow-up PR.
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Thanks, I am closing this!
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Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/15484#discussion_r84585794
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -255,98 +265,125 @@ class Analyzer(
expr
Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/15484#discussion_r84585577
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -216,10 +216,16 @@ class Analyzer(
*
Github user tejasapatil commented on a diff in the pull request:
https://github.com/apache/spark/pull/15484#discussion_r84585881
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/Analyzer.scala
---
@@ -255,98 +265,125 @@ class Analyzer(
expr
Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/15600
**[Test build #67396 has
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Github user tejasapatil commented on the issue:
https://github.com/apache/spark/pull/15272
@rxin : Here is the backport for 2.0 branch:
https://github.com/apache/spark/pull/15600
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GitHub user tejasapatil opened a pull request:
https://github.com/apache/spark/pull/15600
[SPARK-17698] [SQL] Join predicates should not contain filter clauses
## What changes were proposed in this pull request?
This is a backport of
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