[jira] [Created] (SPARK-42634) Several counter-intuitive behaviours in the TimestampAdd expression

["Chenhao Li (Jira)" <ji...@apache.org>]

Chenhao Li created SPARK-42634:
----------------------------------

             Summary: Several counter-intuitive behaviours in the TimestampAdd expression
                 Key: SPARK-42634
                 URL: https://issues.apache.org/jira/browse/SPARK-42634
             Project: Spark
          Issue Type: Bug
          Components: Spark Core, SQL
    Affects Versions: 3.3.2, 3.3.1, 3.3.0
            Reporter: Chenhao Li


# When the time is close to daylight saving time transition, the result may be discontinuous and not monotonic.

We currently have:

 

{{scala> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
scala> spark.sql("select timestampadd(second, 24 * 3600 - 1, timestamp'2011-03-12 03:00:00')").show
+------------------------------------------------------------------------+
|timestampadd(second, ((24 * 3600) - 1), TIMESTAMP '2011-03-12 03:00:00')|
+------------------------------------------------------------------------+
|                                                     2011-03-13 03:59:59|
+------------------------------------------------------------------------+
scala> spark.sql("select timestampadd(second, 24 * 3600, timestamp'2011-03-12 03:00:00')").show
+------------------------------------------------------------------+
|timestampadd(second, (24 * 3600), TIMESTAMP '2011-03-12 03:00:00')|
+------------------------------------------------------------------+
|                                               2011-03-13 03:00:00|
+------------------------------------------------------------------+}}

In the second query, adding one more second will set the time back one hour instead. Plus, there is only 23 * 3600seconds from 2011-03-12 03:00:00 to 2011-03-13 03:00:00, instead of 24 * 3600 seconds, due to the daylight saving time transition.

The root cause of the problem is the Spark code at https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala#L790 wrongly assumes every day has MICROS_PER_DAY seconds, and does the day and time-in-day split before looking at the timezone.

2. Adding month, quarter, and year silently ignores Int overflow during unit conversion.

The root cause is https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala#L1246. quantity is multiplied by 3 or MONTHS_PER_YEARwithout checking overflow. Note that we do have overflow checking in adding the amount to the timestamp, so the behavior is inconsistent.

This can cause counter-intuitive results like this:

 

{{scala> spark.sql("select timestampadd(quarter, 1431655764, timestamp'1970-01-01')").show
+------------------------------------------------------------------+
|timestampadd(quarter, 1431655764, TIMESTAMP '1970-01-01 00:00:00')|
+------------------------------------------------------------------+
|                                               1969-09-01 00:00:00|
+------------------------------------------------------------------+}}

3. Adding sub-month units (week, day, hour, minute, second, millisecond, microsecond)silently ignores Long overflow during unit conversion.

This is similar to the previous problem:

 

{{scala> spark.sql("select timestampadd(day, 106751992, timestamp'1970-01-01')").show(false)
+-------------------------------------------------------------+
|timestampadd(day, 106751992, TIMESTAMP '1970-01-01 00:00:00')|
+-------------------------------------------------------------+
|-290308-12-22 15:58:10.448384                                |
+-------------------------------------------------------------+}}

 



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