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--- Diff: python/pyspark/sql/column.py ---
@@ -180,7 +180,9 @@ def __init__(self, jc):
__ror__ = _bin_op("or")
# container
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--- Diff: python/pyspark/sql/column.py ---
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# container
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--- Diff:
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@@ -60,6 +60,12 @@ trait CatalystConf {
* Enables CBO for
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* Enables CBO for
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+/*
+ * Licensed to
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+ * Licensed to the Apache Software
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Github user jiangxb1987 commented on the issue:
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@gatorsmile Currently we don't perform recursive resolution over a
temporary view, so perhaps that won't trigger a cyclic view reference. For
example:
```
scala> spark.sql("CREATE
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Github user cloud-fan commented on the issue:
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what if we just remove `__contains__`, `__nonzero__ ` and `__bool__`?
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LGTM except 2 questions
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Github user zhzhan commented on the issue:
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@gatorsmile Thanks for reviewing this. I am thinking the logic again. On
the surface, the logic may be correct. Since in the join, the left and right
key should be the same type. Will close the PR.
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Github user HyukjinKwon commented on the issue:
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Oh, no. It does not need to.
I just meant to de-duplicate some logics by
https://github.com/apache/spark/pull/16854#discussion_r103894945. Let me just
remove that part and leave only
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Add a test case to
https://github.com/apache/spark/blob/master/sql/core/src/test/scala/org/apache/spark/sql/JoinSuite.scala?
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Thank you so much. Let me clean up.
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--- Diff: python/pyspark/sql/column.py ---
@@ -180,7 +180,9 @@ def __init__(self, jc):
__ror__ = _bin_op("or")
# container
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@@ -566,3 +586,17 @@ case class StructToJson(
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cc @cloud-fan, @davies and @holdenk.
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[SPARK-19701][SQL][PYTHON] Throws a correct exception for 'in' operator
against column
## What changes were proposed in this pull request?
This PR proposes to remove incorrect
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Github user brkyvz commented on the issue:
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@HyukjinKwon Implementation seems fine. Just left a cosmetic comment on
your unit tests. Otherwise LGTM
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@@ -372,6 +372,58 @@ class
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Github user jinxing64 commented on the issue:
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@squito
Yes, some of machine learning jobs which do cartesian product in my cluster
have over than 100k tasks in the `TaskSetManager`.
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Github user jinxing64 commented on the issue:
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@kayousterhout @squito
Thanks a lot for your comments, really helpful :)
I really think median heap is a good idea. `slice` is `O(n)` and is not
most efficient.
I'm doing
Github user kiszk commented on the issue:
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Jenkins, retest this please
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merged to branch-2.1
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Let's also merge this to branch-2.1.
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Github user asfgit closed the pull request at:
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Github user SparkQA commented on the issue:
https://github.com/apache/spark/pull/17112
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Github user actuaryzhang commented on the issue:
https://github.com/apache/spark/pull/17159
The current implementation accepts data frames with different schemas. See
issues below:
```
df <- createDataFrame(data.frame(name = c("Michael", "Andy", "Justin"), age
= c(1, 30,
GitHub user actuaryzhang opened a pull request:
https://github.com/apache/spark/pull/17159
[SPARK-19818][SparkR] union should check for name consistency of input data
frames
## What changes were proposed in this pull request?
Added checks for name consistency of input data
Github user gatorsmile commented on the issue:
https://github.com/apache/spark/pull/17156
Thanks! Merging to master.
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