Thanks for the explanation.
We are doing something like this.
The *first* watermark is to eliminate the late events from *kafka*
The *second* watermark is to eliminate older aggregated metrics across
*sessions*
I know I can replace the second one with *window* but I was not able to come up
with a solution.
val sessionLevelMetrics: Dataset[Metric] = kafkaEvents
.withWatermark("timestamp", "30 minutes")
.groupByKey(e => e._2.getSiteIdentifierConcatenatedSessionId)
.flatMapGroupsWithState(OutputMode.Append(),
GroupStateTimeout.EventTimeTimeout())(updateSessionState(broadcastWrapper))
val aggMetrics: Dataset[AggregatedMetric] =
sessionLevelMetrics.withColumn("ts", conversion(col("timestamp")))
.withWatermark("ts", "30 minutes")
.groupByKey(m => m.getAs[String]("name") + "." + m.getAs[Long]("timestamp"))
.flatMapGroupsWithState(OutputMode.Append(),
GroupStateTimeout.EventTimeTimeout())(updateAggregateMetricsState)
aggMetrics
.writeStream
.format("com.walmart.cxtools.expo.kairos.KairosSinkProvider")
.option("checkpointLocation", checkpointLocation)
.outputMode(OutputMode.Append())
.trigger(Trigger.ProcessingTime(60.seconds))
.start()
.awaitTermination()
def updateAggregateMetricsState(metricKey: String, sessionLevelMetrics:
Iterator[Row], state: GroupState[AggregatedMetric]): Iterator[AggregatedMetric]
= {
if (state.hasTimedOut) {
state.remove()
Iterator()
} else if (!sessionLevelMetrics.hasNext) {
Iterator()
} else {
val prev = state.getOption
var sum = sessionLevelMetrics.map(_.getAs[Int]("count")).sum
if (prev.isDefined) {
sum += prev.get.count
}
val aggMetric = AggregatedMetric(metricKey, sum)
state.update(aggMetric)
state.setTimeoutTimestamp(metricKey.substring(metricKey.lastIndexOf(".") +
1).toLong + (30 * 60000))
Iterator(aggMetric)
}
}
From: Tathagata Das <tathagata.das1...@gmail.com>
Date: Friday, August 10, 2018 at 4:16 PM
To: subramgr <subramanian.gir...@gmail.com>
Cc: user <user@spark.apache.org>
Subject: EXT: Re: [Structured Streaming] Two watermarks and
StreamingQueryListener
Structured Streaming internally maintains one global watermark by taking a min
of the two watermarks. Thats why one gets reported. In Spark 2.4, there will be
the option of choosing max instead of min.
Just curious. Why do you have to two watermarks? Whats the query like.
TD
On Thu, Aug 9, 2018 at 3:15 PM, subramgr
<subramanian.gir...@gmail.com<mailto:subramanian.gir...@gmail.com>> wrote:
Hi,
We have two *flatMapGroupWithState* in our job and we have two
*withWatermark*
We are getting the event max time, event time and watermarks from
*QueryProgressEvent*.
Right now it just returns one *watermark* value.
Are two watermarks maintained by Spark or just one.
If one which one
If one watermark is maintained per *Dataframe* how do I get the values for
them ?
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