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* @see
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--- Diff: python/pyspark/sql/tests.py ---
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Ah, I got the point. So, you mean a dictionary-like approach -
`get("non-existant-key")` should return `None` always instead of throwing an
error, language-specifically? I think we might
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After merging this PR, I'm going to make a PR on SQL part for Apache Spark
2.4.
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@gatorsmile . Yes. It's officially supported Hive like the following
compatible cases which means upgrading wider types. As you see in my PR, Apache
Spark 2.3 also supports, but the degree of
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Retest this please.
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Yeah, true that is how the Scala side works so I suppose that is best, but
I kind of view this api similar to the python `dict.get` which returns None and
won't raise an error, and that would
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I added more specific comments about ORC parts, @wangyum .
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package
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package
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package
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@wangyum .
Please read again my previous
[comment](https://github.com/apache/spark/pull/20659/files#r172587976).
Although I support your efforts, Spark can provide ORC data
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@liutang123 , Spark should not do this kind of risky thing. Hive 2.3.2 also
disallows incompatible schema changes like the following.
```sql
hive> CREATE TABLE test_par(a string)
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@liutang123 . Did you test this with the latest Apache Spark 2.3? Apache
Spark 2.3 works without any problem with your example.
```scala
scala> sql("create table test_par(a string)
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ok to test
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I think we should better match Python side behaviour to Scala side in
general and throw a better exception if possible. I think the application conf
was done like that because there wasn't
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Thanks everyone, we can move to
[SPARK-23710](https://issues.apache.org/jira/browse/SPARK-23710) to discuss.
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So between https://github.com/apache/spark/pull/20410 & this one which path
do we want to go down?
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@BryanCutler I think it would be an OK assumption to make that the default
values should be the same between Python and Scala provided we maybe added it
to the params docs? What does @HyukjinKwon /
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--- Diff: project/MimaExcludes.scala ---
@@ -36,6 +36,11 @@ object MimaExcludes {
// Exclude rules for 2.4.x
lazy val v24excludes =
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Github user tdas commented on the issue:
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Just to be clear, I am not saying that we *have to* move to this pool
stuff. I am just saying that if we want to make this more robust, then we
should try to use existing tools (after careful
Github user tedyu commented on the issue:
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Interesting.
https://commons.apache.org/proper/commons-pool/apidocs/org/apache/commons/pool2/impl/BaseGenericObjectPool.html#getBorrowedCount()
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@tedyu It was indeed hard to find :) But apache commons pool does expose
metrics on idle/active counts. See
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Results from integration testing:
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Discovery starting.
Discovery completed in 123 milliseconds.
Run starting. Expected test count is: 8
KubernetesSuite:
- Run SparkPi
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[SPARK-23623][SS] Avoid concurrent use of cached consumers in
CachedKafkaConsumer (branch-2.3)
This is a backport of #20767 to branch 2.3
## What changes were proposed in this pull request?
Github user dilipbiswal commented on the issue:
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@cloud-fan OK.. i was thinking of adding this check in each built in
datasource like Text, CSV, Parquet, ORC, JSON etc. Just like we check it in
Parquet with this PR. Would you have any
Github user BryanCutler commented on the issue:
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Thanks @holdenk!
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--- Diff: python/pyspark/ml/tests.py ---
@@ -1980,8 +1997,8 @@ def test_java_params(self):
pyspark.ml.regression]
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Shouldn't it be the same as in the application conf here
https://github.com/apache/spark/blob/master/python/pyspark/conf.py#L174?
Here the default is `None` and it returns `None` if
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Github user cloud-fan commented on the issue:
https://github.com/apache/spark/pull/20824
thanks, merging to master!
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Github user squito commented on the issue:
https://github.com/apache/spark/pull/20770
took a quick look, agree with shivaram's observations, you've got to handle
`shuffleIdToMapStage` which will not be so easy.
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https://github.com/apache/spark/pull/20742
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