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https://issues.apache.org/jira/browse/FLINK-7782?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16197108#comment-16197108
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Kostas Kloudas commented on FLINK-7782:
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Hi [~ajkrishna], as seen in the JIRA that you posted
(https://issues.apache.org/jira/browse/FLINK-7606) the problem there was that
there are no more elements in the stream, so the watermark was not advancing.
This lead to the elements being buffered inside Flink, and waiting for the
watermark to advace, so that their "window" expires.
Can this be the case also for you? You can monitor the size of you state, or
you can write you own timestamp extractor and print every watermark you send.
With parallelism of 1 you can see if the watermark is greater than that of your
last element, or not (which would mean that your data is buffered).
> Flink CEP not recognizing pattern
> ---------------------------------
>
> Key: FLINK-7782
> URL: https://issues.apache.org/jira/browse/FLINK-7782
> Project: Flink
> Issue Type: Bug
> Reporter: Ajay
>
> I am using flink version 1.3.2. Flink has a kafka source. I am using
> KafkaSource9. I am running Flink on a 3 node AWS cluster with 8G of RAM
> running Ubuntu 16.04. From the flink dashboard, I see that I have 2
> Taskmanagers & 4 Task slots
> What I observe is the following. The input to Kafka is a json string and when
> parsed on the flink side, it looks like this
> {code:java}
> (101,Sun Sep 24 23:18:53 UTC 2017,complex
> event,High,37.75142,-122.39458,12.0,20.0)
> {code}
> I use a Tuple8 to capture the parsed data. The first field is home_id. The
> time characteristic is set to EventTime and I have an
> AscendingTimestampExtractor using the timestamp field. I have parallelism for
> the execution environment is set to 4. I have a rather simple event that I am
> trying to capture
> {code:java}
> DataStream<Tuple8<Integer,Date,String,String,Float,Float,Float, Float>>
> cepMapByHomeId = cepMap.keyBy(0);
> //cepMapByHomeId.print();
>
> Pattern<Tuple8<Integer,Date,String,String,Float,Float,Float,Float>, ?> cep1 =
>
> Pattern.<Tuple8<Integer,Date,String,String,Float,Float,Float,Float>>begin("start")
> .where(new OverLowThreshold())
> .followedBy("end")
> .where(new OverHighThreshold());
> PatternStream<Tuple8<Integer, Date, String, String, Float, Float,
> Float, Float>> patternStream = CEP.pattern(cepMapByHomeId, cep1);
> DataStream<Tuple7<Integer, Date, Date, String, String, Float,
> Float>> alerts = patternStream.select(new PackageCapturedEvents());
> {code}
> The pattern checks if the 7th field in the tuple8 goes over 12 and then over
> 16. The output of the pattern is like this
> {code:java}
> (201,Tue Sep 26 14:56:09 UTC 2017,Tue Sep 26 15:11:59 UTC 2017,complex
> event,Non-event,37.75837,-122.41467)
> {code}
> On the Kafka producer side, I am trying send simulated data for around 100
> homes, so the home_id would go from 0-100 and the input is keyed by home_id.
> I have about 10 partitions in kafka. The producer just loops going through a
> csv file with a delay of about 100 ms between 2 rows of the csv file. The
> data is exactly the same for all 100 of the csv files except for home_id and
> the lat & long information. The timestamp is incremented by a step of 1 sec.
> I start multiple processes to simulate data form different homes.
> THE PROBLEM:
> Flink completely misses capturing events for a large subset of the input
> data. I barely see the events for about 4-5 of the home_id values. I do a
> print before applying the pattern and after and I see all home_ids before and
> only a tiny subset after. Since the data is exactly the same, I expect all
> homeid to be captured and written to my sink.
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