gengliangwang commented on code in PR #40561: URL: https://github.com/apache/spark/pull/40561#discussion_r1153965249
########## sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/statefulOperators.scala: ########## @@ -980,3 +1022,65 @@ object StreamingDeduplicateExec { private val EMPTY_ROW = UnsafeProjection.create(Array[DataType](NullType)).apply(InternalRow.apply(null)) } + +case class StreamingDeduplicateWithinWatermarkExec( + keyExpressions: Seq[Attribute], + child: SparkPlan, + stateInfo: Option[StatefulOperatorStateInfo] = None, + eventTimeWatermarkForLateEvents: Option[Long] = None, + eventTimeWatermarkForEviction: Option[Long] = None) + extends BaseStreamingDeduplicateExec { + + protected val schemaForValueRow: StructType = StructType( + Array(StructField("expiresAt", LongType, nullable = false))) Review Comment: @HeartSaVioR both TimestampType and TimestampNTZType are based on epoch. However, for TimestampType, the result will be adjusted based on the SQL conf spark.sql.session.timeZone. For example, we stored a timestamp of 2023-03-30 20:00:00 as TimestampType in Los Angeles time. If users set the spark.sql.session.timeZone as Beijing time(+08:00), the result will be 2023-03-31 11:00:00. If we store it as TimestampNTZ type, the read result will always be 2023-03-30 20:00:00 -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org