xuanyuanking commented on a change in pull request #30427:
URL: https://github.com/apache/spark/pull/30427#discussion_r528579332
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/streaming/ui/StreamingQueryStatisticsPage.scala
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@@ -126,6 +126,58 @@ private[ui] class StreamingQueryStatisticsPage(parent:
StreamingQueryTab)
<br />
}
+ def generateWatermark(
+ query: StreamingQueryUIData,
+ minBatchTime: Long,
+ maxBatchTime: Long,
+ jsCollector: JsCollector): Seq[Node] = {
+ // This is made sure on caller side but put it here to be defensive
+ require(query.lastProgress != null)
+ if (query.lastProgress.eventTime.containsKey("watermark")) {
+ val watermarkData = query.recentProgress.flatMap { p =>
+ val batchTimestamp = parseProgressTimestamp(p.timestamp)
+ val watermarkValue =
parseProgressTimestamp(p.eventTime.get("watermark"))
+ if (watermarkValue > 0L) {
+ // seconds
+ Some((batchTimestamp, ((batchTimestamp - watermarkValue) / 1000.0)))
Review comment:
`we can represent various aspect of views if we plot all points`
Yeah agree, if a multi-line chart is available here might be helpful here! :)
Actually, this idea came when I was thinking about the `ideal line` for the
historical streaming data that should be using event time to represent
processing time, not current clock time.
Anyway, just want to post a different explanation of the `watermark gap`
here, the current changes LGTM. If others think it's worth having another
event-based gap maybe we can do it at another timeline.
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