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https://issues.apache.org/jira/browse/SPARK-20771?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-20771:
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Description:
The weekofyear() implementation follows HIVE / ISO 8601 week number. However it
is not useful because it doesn't return the year of the week start. For example,
weekofyear("2017-01-01") returns 52
Anyone using this with groupBy('week) might do the aggregation or ordering
wrong. A better implementation should return the year number of the week as
well.
MySQL's yearweek() is much better in this sense:
https://dev.mysql.com/doc/refman/5.5/en/date-and-time-functions.html#function_yearweek.
Maybe we should implement that in Spark.
was:
The weekofyear() implementation follows HIVE / ISO 8601 week number. However it
is not useful because it doesn't return the year of the week start. For example,
weekofyear("2017-01-01") returns 52
Anyone using this with groupBy('week) might do the aggregation wrong. A better
implementation should return the year number of the week as well.
MySQL's yearweek() is much better in this sense:
https://dev.mysql.com/doc/refman/5.5/en/date-and-time-functions.html#function_yearweek.
Maybe we should implement that in Spark.
> Usability issues with weekofyear()
> ----------------------------------
>
> Key: SPARK-20771
> URL: https://issues.apache.org/jira/browse/SPARK-20771
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.2.0
> Reporter: Xiangrui Meng
> Priority: Minor
>
> The weekofyear() implementation follows HIVE / ISO 8601 week number. However
> it is not useful because it doesn't return the year of the week start. For
> example,
> weekofyear("2017-01-01") returns 52
> Anyone using this with groupBy('week) might do the aggregation or ordering
> wrong. A better implementation should return the year number of the week as
> well.
> MySQL's yearweek() is much better in this sense:
> https://dev.mysql.com/doc/refman/5.5/en/date-and-time-functions.html#function_yearweek.
> Maybe we should implement that in Spark.
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