FYI I've opened an issue: https://issues.apache.org/jira/browse/BEAM-315
2016-05-31 14:51 GMT+02:00 Pawel Szczur <[email protected]>: > I've also added the test for GroupByKey. It fails. It kind of makes Flink > broken at the moment, isn't it? > > I'm wondering.. may it be related to some Windowing issue? > > 2016-05-31 14:40 GMT+02:00 Pawel Szczur <[email protected]>: > >> I've just tested it. It fails. >> >> Also added the test to the repo: >> https://github.com/orian/cogroup-wrong-grouping >> >> I reason, this means that GroupByKey is flawed? If you open an official >> issue, please add it to discussion. >> >> 2016-05-31 11:55 GMT+02:00 Aljoscha Krettek <[email protected]>: >> >>> Does 2. work for the cases where CoGroupByKey fails? Reason I'm asking >>> is that CoGroupByKey is essentially implemented like that internally: >>> create tagged union -> flatten -> GroupByKey. >>> >>> On Tue, 31 May 2016 at 01:16 Pawel Szczur <[email protected]> wrote: >>> >>>> I've naively tried few other key types, it seems to be unrelated to key >>>> type. >>>> >>>> As for now I have two workarounds and ignorance: >>>> 1. If there is one dominant dataset and other datasets are small (size >>>> << GB) then I use SideInput. >>>> 2. If I have multiple datasets of similar size I enclose it in a >>>> common container, flatten it and GroupByKey. >>>> 3. I measure occurrences and ignore the bug for now. >>>> >>>> Do you have an idea how a test for this may be constructed? It seems >>>> handy, I think. >>>> >>>> I also found two things, maybe they help you: >>>> 1. issue doesn't appear without parallelism >>>> 2. issue doesn't appear with a tiny datasets >>>> >>>> 2016-05-30 17:13 GMT+02:00 Aljoscha Krettek <[email protected]>: >>>> >>>>> You're right. I'm still looking into this, unfortunately I haven't >>>>> made progress so far. I'll keep you posted. >>>>> >>>>> On Sun, 29 May 2016 at 18:20 Pawel Szczur <[email protected]> >>>>> wrote: >>>>> >>>>>> Hi, >>>>>> >>>>>> I used the config as in the repo. >>>>>> Please grep the the log for "hereGoesLongStringID0,2", you will see >>>>>> that this key is processed multiple times. >>>>>> >>>>>> This is how I understand CoGroupByKey: one has two (or more) >>>>>> PCollection<KV<K,?>>. Both sets are grouped by key. For each unique key a >>>>>> KV<K, CoGbkResult> is produced, a given CoGbkResult contains all values >>>>>> from all input PCollections which have the given key. >>>>>> >>>>>> But from the log it seems that each key produced more than one >>>>>> CoGbkResult. >>>>>> >>>>>> The final counters didn't catch the bug because in your case, the >>>>>> value from dataset1 was replicated for each key. >>>>>> >>>>>> Cheers, Pawel >>>>>> >>>>>> 2016-05-29 15:59 GMT+02:00 Aljoscha Krettek <[email protected]>: >>>>>> >>>>>>> Hi, >>>>>>> I ran your data generator with these configs: >>>>>>> p.apply(Create.of(new Config(3, 5, 600_000, 1))) >>>>>>> .apply(ParDo.of(new Generator())).apply( >>>>>>> AvroIO.Write.to >>>>>>> ("/tmp/dataset1").withSchema(DumbData.class).withNumShards(6)); >>>>>>> >>>>>>> p.apply(Create.of(new Config(3, 5, 600_000, 2))). >>>>>>> apply(ParDo.of(new Generator())).apply( >>>>>>> AvroIO.Write.to >>>>>>> ("/tmp/dataset2").withSchema(DumbData.class).withNumShards(6)); >>>>>>> >>>>>>> Then I ran the job with parallelism=6. I couldn't reproduce the >>>>>>> problem, this is the log file from one of several runs: >>>>>>> https://gist.github.com/aljoscha/ef1d804f57671cd472c75b92b4aee51b >>>>>>> >>>>>>> Could you please send me the exact config that you used. Btw, I ran >>>>>>> it inside an IDE, do the problems also occur in the IDE for you or only >>>>>>> when you execute on a cluster? >>>>>>> >>>>>>> Cheers, >>>>>>> Aljoscha >>>>>>> >>>>>>> On Sun, 29 May 2016 at 01:51 Pawel Szczur <[email protected]> >>>>>>> wrote: >>>>>>> >>>>>>>> Hi Aljoscha. >>>>>>>> >>>>>>>> I've created a repo with fake dataset to allow easily reproduce the >>>>>>>> problem: >>>>>>>> https://github.com/orian/cogroup-wrong-grouping >>>>>>>> >>>>>>>> What I noticed: if the dataset is too small the bug doesn't appear. >>>>>>>> >>>>>>>> You can modify the size of dataset, but in ideal case it should be >>>>>>>> few hundred thousands records per key (I guess it depends on the >>>>>>>> machine >>>>>>>> you run it). >>>>>>>> >>>>>>>> Cheers, Pawel >>>>>>>> >>>>>>>> 2016-05-28 12:45 GMT+02:00 Aljoscha Krettek <[email protected]>: >>>>>>>> >>>>>>>>> Hi, >>>>>>>>> which version of Beam/Flink are you using. >>>>>>>>> >>>>>>>>> Could you maybe also provide example data and code that showcases >>>>>>>>> the problem? If you have concerns about sending it to a public list >>>>>>>>> you can >>>>>>>>> also send it to me directly. >>>>>>>>> >>>>>>>>> Cheers, >>>>>>>>> Aljoscha >>>>>>>>> >>>>>>>>> On Fri, 27 May 2016 at 20:53 Pawel Szczur <[email protected]> >>>>>>>>> wrote: >>>>>>>>> >>>>>>>>>> *Data description.* >>>>>>>>>> >>>>>>>>>> I have two datasets. >>>>>>>>>> >>>>>>>>>> Records - the first, containes around 0.5-1M of records per >>>>>>>>>> (key,day). For testing I use 2-3 keys and 5-10 days of data. What I >>>>>>>>>> shoot >>>>>>>>>> for is 1000+ keys. Each record contains key, timestamp in μ-seconds >>>>>>>>>> and >>>>>>>>>> some other data. >>>>>>>>>> Configs - the second, is rather small. It describes the key in >>>>>>>>>> time, e.g. you can think about it as a list of tuples: (key, start >>>>>>>>>> date, >>>>>>>>>> end date, description). >>>>>>>>>> >>>>>>>>>> For the exploration I've encoded the data as files of >>>>>>>>>> length-prefixed Protocol Buffer binary encoded messages. >>>>>>>>>> Additionally the >>>>>>>>>> files are packed with gzip. Data is sharded by date. Each file is >>>>>>>>>> around >>>>>>>>>> 10MB. >>>>>>>>>> >>>>>>>>>> *Pipeline* >>>>>>>>>> >>>>>>>>>> First I add keys to both datasets. For Records dataset it's (key, >>>>>>>>>> day rounded timestamp). For Configs a key is (key, day), where day >>>>>>>>>> is each >>>>>>>>>> timestamp value between start date and end date (pointing midnight). >>>>>>>>>> The datasets are merged using CoGroupByKey. >>>>>>>>>> >>>>>>>>>> As a key type I use import org.apache.flink.api.java.tuple.Tuple2 >>>>>>>>>> with a Tuple2Coder from this repo. >>>>>>>>>> >>>>>>>>>> *The problem* >>>>>>>>>> >>>>>>>>>> If the Records dataset is tiny like 5 days, everything seems fine >>>>>>>>>> (check normal_run.log). >>>>>>>>>> >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:124) - Final aggregator >>>>>>>>>> values: >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - item count : 4322332 >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - missing val1 : 0 >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - multiple val1 : 0 >>>>>>>>>> >>>>>>>>>> When I run the pipeline against 10+ days I encounter an error >>>>>>>>>> pointing that for some Records there's no Config (wrong_run.log). >>>>>>>>>> >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:124) - Final aggregator >>>>>>>>>> values: >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - item count : 8577197 >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - missing val1 : 6 >>>>>>>>>> INFO [main] (FlinkPipelineRunner.java:127) - multiple val1 : 0 >>>>>>>>>> >>>>>>>>>> Then I've added some extra logging messages: >>>>>>>>>> >>>>>>>>>> (ConvertToItem.java:144) - 68643 items for KeyValue3 on: >>>>>>>>>> 1462665600000000 >>>>>>>>>> (ConvertToItem.java:140) - no items for KeyValue3 on: >>>>>>>>>> 1463184000000000 >>>>>>>>>> (ConvertToItem.java:123) - missing for KeyValue3 on: >>>>>>>>>> 1462924800000000 >>>>>>>>>> (ConvertToItem.java:142) - 753707 items for KeyValue3 on: >>>>>>>>>> 1462924800000000 marked as no-loc >>>>>>>>>> (ConvertToItem.java:123) - missing for KeyValue3 on: >>>>>>>>>> 1462752000000000 >>>>>>>>>> (ConvertToItem.java:142) - 749901 items for KeyValue3 on: >>>>>>>>>> 1462752000000000 marked as no-loc >>>>>>>>>> (ConvertToItem.java:144) - 754578 items for KeyValue3 on: >>>>>>>>>> 1462406400000000 >>>>>>>>>> (ConvertToItem.java:144) - 751574 items for KeyValue3 on: >>>>>>>>>> 1463011200000000 >>>>>>>>>> (ConvertToItem.java:123) - missing for KeyValue3 on: >>>>>>>>>> 1462665600000000 >>>>>>>>>> (ConvertToItem.java:142) - 754758 items for KeyValue3 on: >>>>>>>>>> 1462665600000000 marked as no-loc >>>>>>>>>> (ConvertToItem.java:123) - missing for KeyValue3 on: >>>>>>>>>> 1463184000000000 >>>>>>>>>> (ConvertToItem.java:142) - 694372 items for KeyValue3 on: >>>>>>>>>> 1463184000000000 marked as no-loc >>>>>>>>>> >>>>>>>>>> You can spot that in first line 68643 items were processed for >>>>>>>>>> KeyValue3 and time 1462665600000000. >>>>>>>>>> Later on in line 9 it seems the operation processes the same key >>>>>>>>>> again, but it reports that no Config was available for these Records. >>>>>>>>>> The line 10 informs they've been marked as no-loc. >>>>>>>>>> >>>>>>>>>> The line 2 is saying that there were no items for KeyValue3 and >>>>>>>>>> time 1463184000000000, but in line 11 you can read that the items >>>>>>>>>> for this >>>>>>>>>> (key,day) pair were processed later and they've lacked a Config. >>>>>>>>>> >>>>>>>>>> *Work-around (after more testing, doesn't work, staying with >>>>>>>>>> Tuple2)* >>>>>>>>>> >>>>>>>>>> I've switched from using Tuple2 to a Protocol Buffer message: >>>>>>>>>> >>>>>>>>>> message KeyDay { >>>>>>>>>> optional ByteString key = 1; >>>>>>>>>> optional int64 timestamp_usec = 2; >>>>>>>>>> } >>>>>>>>>> >>>>>>>>>> But using Tuple2.of() was just easier than: >>>>>>>>>> KeyDay.newBuilder().setKey(...).setTimestampUsec(...).build(). >>>>>>>>>> >>>>>>>>>> // The original description comes from: >>>>>>>>>> http://stackoverflow.com/questions/37473682/items-not-groupped-correctly-cogroupbykey >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>> >>>> >> >
