github-actions[bot] commented on code in PR #65219:
URL: https://github.com/apache/doris/pull/65219#discussion_r3549232676
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fe/fe-core/src/main/java/org/apache/doris/common/util/PropertyAnalyzer.java:
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@@ -409,10 +411,25 @@ public static DataProperty
analyzeDataProperty(Map<String, String> properties, f
}
} else if (key.equalsIgnoreCase(PROPERTIES_STORAGE_COOLDOWN_TIME))
{
try {
- DateLiteral dateLiteral =
DateLiteralUtils.createDateLiteral(value,
- ScalarType.getDefaultDateType(Type.DATETIME));
- cooldownTimestamp =
dateLiteral.unixTimestamp(TimeUtils.getTimeZone());
- } catch (AnalysisException e) {
+ // A value with an explicit timezone offset (e.g.
+ // "2027-01-01 00:00:00+00:00") represents an unambiguous
+ // UTC instant. Parse it directly as a ZonedDateTime and
+ // convert to epoch millis, bypassing the DATETIME-based
+ // DateLiteralUtils.createDateLiteral() path which uses
+ // Instant.now() for the initial offset computation and
+ // produces incorrect results across DST boundaries.
+ if (value.length() > 6
+ && (value.charAt(value.length() - 6) == '+'
+ || value.charAt(value.length() - 6) == '-')) {
+ cooldownTimestamp = ZonedDateTime.parse(value,
Review Comment:
This offset-aware branch still rejects cooldown strings that include
fractional seconds. `DynamicPartitionScheduler.convertToUtcTimestamp()`
preserves the partition column scale when it calls
`TimeStampTzType.of(((ScalarType) column.getType()).getScalarScale())`, and
`TimestampTzLiteral.getStringValue()` renders TIMESTAMPTZ values with that
scale before appending `+00:00`. For a `TIMESTAMPTZ(6)` dynamic partition, the
generated hot-partition cooldown is therefore shaped like `2027-01-01
00:00:00.000000+00:00`, but this formatter only accepts `yyyy-MM-dd
HH:mm:ssXXX`. The parse failure is caught below and converted to
`DataProperty.MAX_COOLDOWN_TIME_MS`, so the SSD partition silently loses its
intended cooldown. Please accept optional fractional seconds here, or reuse a
TIMESTAMPTZ/offset parser that preserves the instant, and add a
`TIMESTAMPTZ(6)` cooldown regression.
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