Does every record in a Flink EventTime application need a timestamp?

Does every record in a Flink EventTime application need a timestamp?

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I hope this helps . Generally speaking, the best approach is to have proper event-time timestamps on every event, and to use event-time everywhere. This has the advantage of being able to use the exact same code for both live data and historic data -- which is very valuable when the need arises to re-process historic data in order to fix bugs or upgrade your pipeline. With this in mind, it's typically possible to do backfill by simply running a second copy of the application -- one that's processing historic data rather than live data.
As for using a mix of historic and live data in the same application, and whether you need to have timestamps and watermarks for the historic events -- it depends on the details. For example, if you are going to connect the two streams, the watermarks (or lack of watermarks) on the historic stream will hold back the watermarks on the connected stream. This will matter if you try to use event-time timers (or windows, which depend on timers) on the connected stream.

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Unix: Extract timestamp from first record in xml file and it checks if not it will replace first record timestamp

Tag : xml , By : Tom D
Date : March 29 2020, 07:55 AM
it helps some times I have test.xml , i used AWK
awk -F '</?date>' '
                 #{printf("%s \"%s\"\n", substr($0, 1, 2), $2)}
                 /^<emp>/ { ed = $2
                        cd = substr($2, 7, 2) substr($2, 1, 2) substr($2, 4, 2) substr($2, 10)
                        print next }
                /^<Join>/ {
                        if(cd > (substr($2, 7, 2) substr($2, 1, 2) substr($2, 4, 2) substr($2, 10)))
                        $0 = $1 "<date>" ed "</date>" $3 } 1' test.xml

EventTime windowing from Kafka stream causing "Timestamp monotony violated" error

Tag : development , By : Frank Bradley
Date : March 29 2020, 07:55 AM
To fix the issue you can do I believe that unless you can guarantee forward progress in timestamps across all partitions, because you are extracting timestamps and watermarks outside of your source, you will get this error.
What you can potentially do is use your SeriesMap class as a Kafka DeserializationSchema and then do the assignTimestampsAndWatermarks against your Kafka source. Kafka will then have no issues with your timestamps moving forward separately within each partition, and the global watermark it emits will be the minimum of the watermark encountered across all partitions.

Tag : java , By : Vijayant Singh
Date : March 29 2020, 07:55 AM
Does that help Flink allows the use of processing time windows with event time streams, because there are legitimate use cases for that. But if you do want event time windowing, you need to ask for it. In this case you should be using TumblingEventTimeWindows.

Timestamp & Watermark assigning for two input streams, later connected for dynamic alerting using 'EventTime'

Tag : java , By : UpperLuck
Date : March 29 2020, 07:55 AM
I hope this helps . This exercise in the Flink training covers exactly this case: https://training.ververica.com/exercises/taxiQuery.html. See the hints and the solution for details, but the approach taken there is to use this timestamp extractor / watermark generator on the stream with the rules:
// Once the two streams are connected, the Watermark of the KeyedBroadcastProcessFunction operator
// will be the minimum of the Watermarks of the two connected streams. Our query stream has a default
// Watermark at Long.MIN_VALUE, and this will hold back the event time clock of the
// KeyedBroadcastProcessFunction, unless we do something about it.
public static class QueryStreamAssigner implements AssignerWithPeriodicWatermarks<String> {
    public Watermark getCurrentWatermark() {
        return Watermark.MAX_WATERMARK;

    public long extractTimestamp(String element, long previousElementTimestamp) {
        return 0;

Tag : development , By : Jay Crockett
Date : March 29 2020, 07:55 AM
I wish did fix the issue. One way to solve this problem is to generate watermarks after mixing together events from all of the partitions, so that the slow/idle partition doesn't hold back the overall watermark:
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