GitHub user cloud-fan opened a pull request:
https://github.com/apache/spark/pull/16647
[SPARK-19292][SQL] filter with partition columns should be case-insensitive
on Hive tables
## What changes were proposed in this pull request?
When we query a table with a filter on
Github user maropu commented on the issue:
https://github.com/apache/spark/pull/16605
The workaround @cloud-fan said is good to me though, IMO the most critical
issue here is that this cast exception happens not in analysis phases but in
runtime. So, at least I think we should modify
Github user squito commented on a diff in the pull request:
https://github.com/apache/spark/pull/15505#discussion_r96883996
--- Diff:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
---
@@ -244,32 +245,45 @@ class
Github user yanboliang commented on the issue:
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cc @felixcheung
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Github user scwf commented on the issue:
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I think the local limit cost is important, we assume recompute partions
number: m, all the partitions: n
m = 1, n =100 is a positive case, but there also cases that m very close to
n(even m = n).
Github user squito commented on the issue:
https://github.com/apache/spark/pull/16346
merged to master, thanks @jsoltren!
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Github user asfgit closed the pull request at:
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Github user scwf commented on the issue:
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Your proposal avoid the cost of all partitions compute and shuffle for
local limit but introduce some partitions recompute for local limit stage.
We can not decide which cost is cheaper(in
Github user viirya commented on the issue:
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@scwf You still don't get the point. Although few partitions need to
recompute in local limit, most of other partitions are saved from computation.
In most cases, it is worth. You can refer to my
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GitHub user yanboliang opened a pull request:
https://github.com/apache/spark/pull/16646
[SPARK-19291][SPARKR][ML] spark.gaussianMixture supports output
log-likelihood.
## What changes were proposed in this pull request?
```spark.gaussianMixture``` supports output total
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Github user viirya commented on the issue:
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@scwf it is fair. anyway, i don't think a proposal can't improve any point
of the issues is worth so many requested changing...
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Github user scwf commented on the issue:
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I think before compare our proposals , we should first make sure our
proposal will not bring performance regression.
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Github user cloud-fan commented on the issue:
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cc @yhuai @gatorsmile
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GitHub user cloud-fan opened a pull request:
https://github.com/apache/spark/pull/16645
[SPARK-19290][SQL] add post-hoc resolution
## What changes were proposed in this pull request?
To implement DDL commands, we added several analyzer rules in sql/hive
module to analyze
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Github user srowen commented on the issue:
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Yes it is. What error do you get? these examples are compiled with the code
and compiles correctly in master and previous 2.x builds.
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Github user ganeshkrishnan1 commented on the issue:
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Fails to compile on Spark 2.1.0
Shouldn't mlib be backward compatible?
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Github user srowen commented on the issue:
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Please see http://spark.apache.org/contributing.html first
This is an example for .mllib. While it works with 2.x, or should, it is
not intended to use SparkSession, as I understand.
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Can one of the admins verify this patch?
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GitHub user getintouchapp opened a pull request:
https://github.com/apache/spark/pull/16644
Update LDAExample with Spark 2.1.0
Changes: Updated code with new Spark 2.x changes
Removed SparkContext and replaced with Spark Session
Fixed compile time errors in new Spark
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Github user cloud-fan commented on the issue:
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good catch! introducing a new analyzer rule SGTM
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Github user zhengruifeng commented on the issue:
https://github.com/apache/spark/pull/16571
In the jira, we decide to optimize MultivariateOnlineSummarizer first, so
this pr will be closed.
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Github user lins05 commented on the issue:
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I just found "create table using hive " (without "select ... from", i.e.
the non-CTAS form) is handled by `CreateTableCommand`
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Github user windpiger commented on the issue:
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retest this please
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Github user cloud-fan commented on a diff in the pull request:
https://github.com/apache/spark/pull/16593#discussion_r96849182
--- Diff:
sql/hive/src/test/scala/org/apache/spark/sql/hive/execution/HiveDDLSuite.scala
---
@@ -1384,4 +1394,96 @@ class HiveDDLSuite
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Github user cloud-fan commented on the issue:
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thanks, merging to master!
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Github user HyukjinKwon commented on the issue:
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@cloud-fan, could you take a look please?
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Github user asfgit closed the pull request at:
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LGTM, merged into master. Thanks!
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Github user wzhfy commented on the issue:
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@gatorsmile That would be great, thanks
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https://github.com/apache/spark/pull/16635
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Github user viirya commented on the issue:
https://github.com/apache/spark/pull/16633
@scwf I understand your point. But the main issue is, you can't save the
local limit cost and the shuffling cost. You still need to process all rows in
all partitions and shuffle (some of) them to
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