MaxGekk commented on a change in pull request #35502:
URL: https://github.com/apache/spark/pull/35502#discussion_r810638665
##########
File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/util/DateTimeUtils.scala
##########
@@ -1163,4 +1163,40 @@ object DateTimeUtils {
val localStartTs = getLocalDateTime(startMicros, zoneId)
ChronoUnit.MICROS.between(localStartTs, localEndTs)
}
+
+ /**
+ * Adds the specified number of units to a timestamp.
+ *
+ * @param unit A keyword that specifies the interval units to add to the
input timestamp.
+ * @param quantity The amount of `unit`s to add. It can be positive or
negative.
+ * @param micros The input timestamp value, expressed in microseconds since
1970-01-01 00:00:00Z.
+ * @param zoneId The time zone ID at which the operation is performed.
+ * @return A timestamp value, expressed in microseconds since 1970-01-01
00:00:00Z.
+ */
+ def timestampAdd(unit: String, quantity: Int, micros: Long, zoneId: ZoneId):
Long = {
+ unit.toUpperCase(Locale.ROOT) match {
+ case "MICROSECOND" =>
+ timestampAddDayTime(micros, quantity, zoneId)
+ case "MILLISECOND" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_MILLIS, zoneId)
+ case "SECOND" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_SECOND, zoneId)
+ case "MINUTE" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_MINUTE, zoneId)
+ case "HOUR" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_HOUR, zoneId)
+ case "DAY" | "DAYOFYEAR" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_DAY, zoneId)
+ case "WEEK" =>
+ timestampAddDayTime(micros, quantity * MICROS_PER_DAY * DAYS_PER_WEEK,
zoneId)
+ case "MONTH" =>
+ timestampAddMonths(micros, quantity, zoneId)
+ case "QUARTER" =>
+ timestampAddMonths(micros, quantity * 3, zoneId)
+ case "YEAR" =>
+ timestampAddMonths(micros, quantity * MONTHS_PER_YEAR, zoneId)
+ case _ =>
+ throw QueryExecutionErrors.invalidUnitInTimestampAdd(unit)
Review comment:
> the first argument, unit, should be a datetime **interval type**
I didn't get your point. How it could be the interval type?
> ... as in what's used with EXTRACT
Just wonder why do you linked `TIMESTAMPADD` to `EXTRACT` but not to
`TIMESTAMPDIFF`, for example. Anyway technically specking the type of the
first argument is the same - string type.
> ... makes things any simpler.
This PR achieve this goal, I believe. It makes the migration process to
Spark SQL simpler, and gives additional benefits of using Spark SQL in the real
production (see my comment above).
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