Hi Ajay,
I will look a bit more on the issue.
But in the meantime, could you run your job with parallelism of 1, to see if
the results are the expected?
Also could you change the pattern, for example check only for the start, to see
if all keys pass through.
As for the code, you apply keyBy(0) the cepMap stream twice, which is redundant
and introduces latency.
You could remove that to also see the impact.
Kostas
> On Sep 28, 2017, at 2:57 AM, Ajay Krishna <ajaykris...@gmail.com> wrote:
>
> Hi,
>
> I've been only working with flink for the past 2 weeks on a project and am
> trying using the CEP library on sensor data. 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
>
> (101,Sun Sep 24 23:18:53 UTC 2017,complex
> event,High,37.75142,-122.39458,12.0,20.0)
> 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
>
> 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.keyBy(0), cep1);
>
>
> DataStream<Tuple7<Integer, Date, Date, String, String, Float,
> Float>> alerts = patternStream.select(new PackageCapturedEvents());
> 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
>
> (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)
> 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 which is cassandra in this case.
> I've looked through all available docs and examples but cannot seem to get a
> fix for the problem.
>
> I would really appreciate some guidance how to understand fix this.
>
>
>
> Thank you,
>
> Ajay
>
