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Thanks! Merging to master and 2.2.
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According to [this
material](http://s3-eu-west-1.amazonaws.com/presentations2013/10_presentation.pdf#pages=42),
`Unsafe.allocateMemory` uses 8-byte boundary.
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@cloud-fan could you please review this again? Now, this PR does not apply
any change to `ColumnVector` and `WritableColumnVector`.
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This function is not reported in the API documentation:
https://spark.apache.org/docs/2.2.0/api/java/index.html?org/apache/spark/sql/functions.html
or
https://spark.apache.org/docs/2.1.0/a
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@sergiobilello-eb This is just a SQL function. You can call it in SQL
interface or using the `df.select(expr("dayofweek('2009-07-30')"))`. It is not
part of the DataFrame functions. You can submi
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thanks @gatorsmile :)
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```spark-sql> select dayofweek('2007-02-03'), dayofweek('2009-07-30'),
dayofweek('2017-05-27'), dayofweek(null), dayofweek('1582-10-15 13:10:15');
17/11/10 16:08:23 INFO SparkSqlParser:
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https://issues.apache.org/jira/browse/SPARK-20909
This is not part of 2.2. Based on JIRA, it will be included in 2.3
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thanks @gatorsmile! Do you suggest any workaround until then? I mean not
rebuilding spark with that patch Can I register my UDF that contains that
logic?
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Yeah, you always can implement such a UDF.
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make it an AlternativeConfig maybe better
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@cloud-fan For the UI part, how about this PR:
https://github.com/apache/spark/pull/14577
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I think it makes sense, but the implementation is too hacky. We should
extract the time epoch and convert it to timestamp string with local timezone.
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Thanks for the updates! My only remaining comments are about:
* Default arguments for readImages in Scala not being Java-friendly (I'd
still recommend taking the easy route by having 1 simple
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