Major Description
- 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 are only 23 * 3600 seconds 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_YEAR without 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 | +-------------------------------------------------------------+
