HyukjinKwon commented on code in PR #39239:
URL: https://github.com/apache/spark/pull/39239#discussion_r1058277577
##########
python/pyspark/sql/types.py:
##########
@@ -276,7 +276,15 @@ def toInternal(self, dt: datetime.datetime) -> int:
def fromInternal(self, ts: int) -> datetime.datetime:
if ts is not None:
# using int to avoid precision loss in float
- return datetime.datetime.fromtimestamp(ts //
1000000).replace(microsecond=ts % 1000000)
+ return (
+ datetime.datetime
+ # Set the time zone to UTC because the TIMESTAMP type stores
timestamps
+ # as the number of microseconds from the epoch of
1970-01-01T00:00:00.000000Z
+ # in the UTC time zone.
+ .fromtimestamp(ts // 1000000,
tz=datetime.timezone.utc).replace(
Review Comment:
I think there're implementation details that we should fix. My take was (see
quotes from
https://docs.python.org/3/library/datetime.html#aware-and-naive-objects)
> ... Whether a naive object represents Coordinated Universal Time (UTC),
local time, or time in some other timezone is purely up to the program ...
With `TimestampType`, naive datetimes are interpreted as a local time.
Meaning that `java.time.LocalDateTime`
With `TimestampNTZType`, naive datetimes are interpreted as a UTC time.
Meaning that `java.time.Instant`
But reading that codes, SQL side is opposite. My understanding was that
`TimestampType` is a local time per Spark's session timezone, and
`TimestampNTZType` is a UTC time.
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