TJX2014 commented on a change in pull request #28856:
URL: https://github.com/apache/spark/pull/28856#discussion_r444045788
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/collectionOperations.scala
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@@ -2589,6 +2589,8 @@ object Sequence {
}
}
+ // To generate time sequences, we use scale 1 in TemporalSequenceImpl
+ // for `TimestampType`, while MICROS_PER_DAY for `DateType`
Review comment:
Yes, seems we can, the result as follows：
`scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 hour))").count
res19: Long = 1465
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 minute))").count
res20: Long = 87841
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 second))").count
res21: Long = 5270401
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 minute))").head(3)
res25: Array[org.apache.spark.sql.Row] = Array([2011-03-01], [2011-03-01],
[2011-03-01])
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 second))").head(3)
res26: Array[org.apache.spark.sql.Row] = Array([2011-03-01], [2011-03-01],
[2011-03-01])
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 minute))").head(3)
res27: Array[org.apache.spark.sql.Row] = Array([2011-03-01], [2011-03-01],
[2011-03-01])
scala> sql("select explode(sequence(cast('2011-03-01' as date),
cast('2011-05-01' as date), interval 1 hour))").head(3)
res28: Array[org.apache.spark.sql.Row] = Array([2011-03-01], [2011-03-01],
[2011-03-01])
`
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