Hi Kostas, Here is an example of a simple event I am trying to detect. The first and last line are the interesting points/events. The CEP library is not able to detect something like that.
4> (96,Sat Sep 30 22:30:25 UTC 2017,complex event,Low,32.781082,-117.01864,12.0,20.0) 4> (96,Sat Sep 30 22:30:26 UTC 2017,complex event,High,32.781082,-117.01864,0.0235,20.0) 4> (96,Sat Sep 30 22:30:27 UTC 2017,complex event,High,32.781082,-117.01864,0.02319611,20.0) 4> (96,Sat Sep 30 22:30:28 UTC 2017,complex event,Medium,32.781082,-117.01864,0.023357224,20.0) 4> (96,Sat Sep 30 22:30:29 UTC 2017,complex event,Low,32.781082,-117.01864,0.060904443,20.0) 4> (96,Sat Sep 30 22:30:30 UTC 2017,complex event,Medium,32.781082,-117.01864,0.100115,20.0) 4> (96,Sat Sep 30 22:30:31 UTC 2017,complex event,High,32.781082,-117.01864,0.12398389,20.0) 4> (96,Sat Sep 30 22:30:32 UTC 2017,complex event,Medium,32.781082,-117.01864,0.15611167,20.0) 4> (96,Sat Sep 30 22:30:33 UTC 2017,complex event,Low,32.781082,-117.01864,0.15817556,20.0) 4> (96,Sat Sep 30 22:30:34 UTC 2017,complex event,Low,32.781082,-117.01864,0.09934334,20.0) 4> (96,Sat Sep 30 22:30:35 UTC 2017,complex event,High,32.781082,-117.01864,16.0,20.0) Notes about this experiment. 1. Only one kafka partition and just one topic 2. Flink env parallelism set to 4 and I am using AscendingTimestampExtractor on KafkaSource09. 3. In the data above, the first element is the id that I use for keyBy 4. I started 4 Kafka producers in parallel with a random delay between them 5. Each producer sends 10000 rows from a csv at an average of 18 seconds. Of the data from 4 producers, the events for only one was detected. 6. Looking at the log files, I print on the stream and see all 40000 lines where each id is associated with one process number. In the above data 96 is only associated with 4. In this case there is just one partition in Kafka. If I were to increase the number of partitions each id is spread across multiple processes. 7. I had ran another test with a different set of 4 ids just before the one I've presented above and I expected to see 148 events for 4 ids and I saw all of them being captured. I did not change anything as far as delays in the producer. The behavior is quite arbitrary and I am suspecting the cause is because of bugs FLINK-7549 <https://issues.apache.org/jira/browse/FLINK-7549> and FLINK-7606 <https://issues.apache.org/jira/browse/FLINK-7606>. Could you help understand further. Best regards, Ajay On Thu, Sep 28, 2017 at 8:39 AM, Kostas Kloudas <k.klou...@data-artisans.com > wrote: > Hi Ajay, > > After reading all the data from your source, could you somehow tell your > sources to send > a watermark of Long.MaxValue (or a high value)?? > > I am asking this, just to see if the problem is that the data is simply > buffered inside Flink because > there is a problem with the timestamps and the watermarks. > You could also see this from the WebUi, but seeing the size of your > checkpointed state. > If the size increases, it means that something is stored there. > > I will also have a deeper look. > > Kostas > > On Sep 28, 2017, at 5:17 PM, Ajay Krishna <ajaykris...@gmail.com> wrote: > > Hi Kostas, > > Thank you for reaching out and for the suggestions. Here are the results > > 1. Using an env parallelism of 1 performed similar with the additional > problem that there was significant lag in the kafka topic > 2. I removed the additional keyBy(0) but that did not change anything > 3. I also tried only to check for the start only pattern and it was > exactly the same where I saw one of the homes going through but 3 others > just getting dropped. > 4. I also tried slowing down the rate from 5000/second into Kafka to about > 1000/second but I see similar results. > > I was wondering if you had any other solutions to the problem. I am > specially concerned about 1 and 3. Is this library under active development > ? Is there a JIRA open on this issue and could be open one to track this ? > > > I was trying read on Stackoverlfow and found a user had a very very > similar issue in Aug'16. So I also contacted him to discuss the issue and > learn't that the pattern of failure was exactly the same. > > https://stackoverflow.com/questions/38870819/flink-cep-is- > not-deterministic > > > Before I found the above post, I created a post for this issue > https://stackoverflow.com/questions/46458873/flink-cep- > not-recognizing-pattern > > > > I would really appreciate your guidance on this. > > Best regards, > Ajay > > > > > > On Thu, Sep 28, 2017 at 1:38 AM, Kostas Kloudas < > k.klou...@data-artisans.com> wrote: > >> 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 >> >> >> > >
