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Github user dilipbiswal commented on the issue:
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@viirya You know, as I said above, both ways are not perfect. My inputs are
just based on my previous design experiences. All the design decisions I made
are based on usage scenarios.
Github user dilipbiswal commented on the issue:
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@gatorsmile I am not stand for it due to actual use cases, but the API
behavior consistency. If we disallow duplicate group by columns, then we should
do filtering them all. Let
df.
Github user dilipbiswal commented on the issue:
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@viirya I am just wondering why users need a dataframe with duplicate
column names? Could you give me a usage scenario?
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Github user dilipbiswal commented on the issue:
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@gatorsmile Your last example just shows the inconsistency. Given two
different parameters, `$"col1", count("*")` and `count("*")`, you get the same
output. I think this confuses users. In contr
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Github user dilipbiswal commented on the issue:
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@viirya This is a design decision. So far, both ways are not perfect.
In my mind, we have to consider the use cases here. If users want to have
duplicate columns, they should not use th
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ok to test
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Github user tmnd1991 commented on the issue:
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I noticed a scala style error, wait till new commit before triggering a
jenkins build.
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Github user asfgit closed the pull request at:
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Merged to master/2.0
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[MINOR] [BUILD] Add modernizr MIT license; specify "2014 and onwards" in
license copyright
## What changes were proposed in this pull request?
Per conversation on dev list, add missing mode
Github user tmnd1991 commented on the issue:
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(Fix the title please)
https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark
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Github user viirya commented on the issue:
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@gatorsmile this automatically deduplicate the group by columns will cause
confusion. Using your example:
df.groupBy("col1").agg(count("*"))
When users try the above API call, th
Github user viirya commented on the issue:
https://github.com/apache/spark/pull/13496
also cc @rxin
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Github user lianhuiwang commented on the issue:
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@rxin I have writed a design doc:
https://docs.google.com/document/d/1Bmi4-PkTaBQ0HVaGjIqa3eA12toKX52QaiUyhb6WQiM/edit?usp=sharing.
Glad to get your comments. Thanks.
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Github user AmplabJenkins commented on the issue:
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Can one of the admins verify this patch?
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SPARK-15740
## What changes were proposed in this pull request?
"test big model load / save" in Word2VecSuite, lately resulted into OOM.
Therefore we decided to make the partitioning adapti
Github user dongjoon-hyun commented on a diff in the pull request:
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--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/PartitioningUtils.scala
---
@@ -350,6 +350,10 @@ private[sql] object Part
Github user wangyang1992 commented on a diff in the pull request:
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--- Diff:
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@@ -350,6 +350,10 @@ private[sql] object Parti
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--- Diff:
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Github user wangyang1992 commented on a diff in the pull request:
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--- Diff:
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Github user zhonghaihua commented on the issue:
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Hi, @vanzin , the code has changed, could you review it, please?
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--- Diff: core/src/main/scala/org/apache/spark/SparkContext.scala ---
@@ -1441,6 +1441,32 @@ class SparkContext(config: SparkConf) extends
Logging with Exe
Github user vectorijk commented on the issue:
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@praveendareddy21 For generating documentation for this API correctly, you
could include this in `spark/python/docs/pyspark.ml.rst`
```
pyspark.ml.stat module
--
Github user vectorijk commented on a diff in the pull request:
https://github.com/apache/spark/pull/13248#discussion_r65798896
--- Diff: python/pyspark/ml/stat/distribution.py ---
@@ -0,0 +1,267 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
Github user vectorijk commented on a diff in the pull request:
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--- Diff: python/pyspark/ml/stat/distribution.py ---
@@ -0,0 +1,267 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
Github user rxin commented on the issue:
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lgtm
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