tianhanhu-db commented on code in PR #40678:
URL: https://github.com/apache/spark/pull/40678#discussion_r1160368163
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sql/core/src/main/scala/org/apache/spark/sql/jdbc/PostgresDialect.scala:
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@@ -98,6 +100,14 @@ private object PostgresDialect extends JdbcDialect with
SQLConfHelper {
case _ => None
}
+ override def convertJavaTimestampToTimestampNTZ(t: Timestamp): Long = {
+ DateTimeUtils.localDateTimeToMicros(t.toLocalDateTime)
Review Comment:
I will give a concrete Postgres read example where the general
implementation would fail.
Say there is a Timestamp of "2023-04-05 08:00:00" stored in Postgres
database and we want to read it as Spark TimestampNTZType from a TimeZone of
America/Los_Angeles. The expected results would be "2023-04-05 08:00:00".
When we do `PostgresDriver.getTimestamp`, what happens under the hood is
that Postgres would use the default JVM TimeZone and create a Timestamp
representing an instant of the wall clock in that time zone. Thus, the Java
Timestamp effectively represents "2023-04-05 08:00:00 America/Los_Angeles".
With our general conversion, we will just store the underlining microseconds
from epoch to represent the TimestampNTZType. This is problematic as when
displaying the TimestampNTZType, we convert to a LocalDateTime using UTC as the
time zone. This will give as an erroneous result of "2023-04-05 15:00:00".
The Postgres specific conversion first convert the Java Timestamp to
LocalDateTime before getting its underlining microseconds from epoch. This
basically restores the Timestamp to represent "2023-04-05 08:00:00 UTC". Thus
when converting back we get the correct result.
For write it is the similar story. @cloud-fan @beliefer
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