Usually just to alter the default execution plan. In this case where the crunch bug counteracted our own bug we used it to end up with the desired (*) output (in fact triggering the crunch bug). We now fixed our bug and are no longer pursuing the crunch bug.
In general I would not expect the output to be dependent on the execution plan. Cheers, Tim *) Here we had a discrepancy between desired and (technically) correct output. On Wed, Feb 6, 2013 at 2:07 PM, Gabriel Reid <[email protected]> wrote: > Thanks for all the info Tim. I've posted a bit more information on > CRUNCH-163, and will look into it more this evening. > > About calling materialize within pipelines, just to clarify: are you doing > this both to get a more efficient execution (i.e. alter the default > execution plan) as well as to get the correct output, or just one of those > two? > > Thanks, > > Gabriel > > > On Wed, Feb 6, 2013 at 11:53 AM, Tim van Heugten <[email protected]> wrote: > >> To summarize: >> - When we saw data duplication, that was what we should have been >> expecting, given our implementation. That is not the issue. >> - Sometimes we didn't see data duplication. That is an issue: >> *Union sometimes ignores one of the input branches.* >> >> I created https://issues.apache.org/jira/browse/CRUNCH-163 for this >> issue. The tests singleUnion and doubleUnionWithoutMaterializeInbetween >> pass in my environment (0.4), the others fail. >> Besides breaking a union by adding a materialize after it I could also >> break it by performing a parallelDo after it or by just joining two read >> pCollections. >> >> >> Cheers, >> >> Tim >> >> >> >> On Tue, Feb 5, 2013 at 3:38 PM, Tim van Heugten <[email protected]>wrote: >> >>> Hmmm, >>> >>> So we had a mistake in our code that emitted the data in both branches >>> before union2. >>> *And*, the crunch union also *failed to merge the data* in some >>> circumstance. My side-remark about not seeing the join happen was actually >>> bang on.. :-/ >>> >>> So the question now becomes, when does a union ignore one of its >>> incoming branches? >>> Apparently with materialization in the right spots we can force the >>> correct pipeline(*). >>> >>> Cheers, >>> >>> Tim van Heugten >>> >>> >>> *) Thereby exposing our bug, seemingly data duplication. But just to be >>> clear, this is actually the *correct* behavior. >>> >>> >>> >>> On Tue, Feb 5, 2013 at 3:18 PM, Tim van Heugten <[email protected]>wrote: >>> >>>> Hi, >>>> >>>> It turns out the data in the two branches that are unioned in union2 is >>>> not mutually exclusive (counter to what I was expecting). Probably we >>>> should expect data duplication. >>>> >>>> However, this does still not explain why sometimes we find data >>>> duplication and sometimes we don't. >>>> >>>> Will keep you posted, >>>> >>>> Tim >>>> >>>> >>>> On Tue, Feb 5, 2013 at 11:32 AM, Tim van Heugten <[email protected]>wrote: >>>> >>>>> Hi Gabriel, >>>>> >>>>> I've been unsuccessful so far to reproduce the issue in a controlled >>>>> environment. As said, its fragile, maybe the types involved play a role, >>>>> so >>>>> when I tried to simplify those I broke the failure condition. >>>>> I decide it's time to try providing more information without giving an >>>>> explicit example. >>>>> >>>>> The pipeline we build is illustrated here: http://yuml.me/8ef99512. >>>>> Depending on where we materialize the data occurs twice in UP. >>>>> The EITPI job filters the exact opposite of the filter branch. In PWR >>>>> only data from EITPI is passed through, while the PITP data is used to >>>>> modify it. >>>>> Below you find the job names as executed when dataduplication occurs, >>>>> materializations occur before BTO(*) and after UP. >>>>> >>>>> "Avro(target/stored/sIPhase)+EITPI+GBK+PITEI+Avro(/tmp/crunch655004156/p4)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+PITP]/[Avro(/tmp/crunch655004156/p4)]]+GBK+PWR+UnionCollectionWrapper+Avro(/tmp/crunch655004156/p2)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+PITP]/[Avro(/tmp/crunch655004156/p4)]]+GBK+PWR+BTO+Avro(/tmp/crunch655004156/p8)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+S0+BTO]/[Avro(/tmp/crunch655004156/p8)]]+GBK+UP+Avro(/tmp/crunch655004156/p6)" >>>>> "Avro(/tmp/crunch655004156/p6)+GetData+Avro(/tmp/crunch655004156/p10)" >>>>> >>>>> "Avro(/tmp/crunch655004156/p6)+GetTraces+Avro(target/trace-dump/traces)" >>>>> >>>>> Here are the jobs performed when materialization is added between BTO >>>>> and gbk: >>>>> >>>>> "Avro(target/stored/sIPhase)+EITPI+GBK+PITEI+Avro(/tmp/crunch-551174870/p4)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+PITP]/[Avro(/tmp/crunch-551174870/p4)]]+GBK+PWR+UnionCollectionWrapper+Avro(/tmp/crunch-551174870/p2)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+PITP]/[Avro(/tmp/crunch-551174870/p4)]]+GBK+PWR+BTO+Avro(/tmp/crunch-551174870/p6)" >>>>> "Avro(/tmp/crunch-551174870/p6)+GBK+UP+Avro(/tmp/crunch-551174870/p8)" >>>>> >>>>> "Avro(/tmp/crunch-551174870/p8)+GetData+Avro(/tmp/crunch-551174870/p10)" >>>>> >>>>> "Avro(/tmp/crunch-551174870/p8)+GetTraces+Avro(target/trace-dump/traces)" >>>>> >>>>> Without changing changing anything else, the added materialization >>>>> fixes the issue of data duplication. >>>>> >>>>> If you have any clues how I can extract a clean working example I'm >>>>> happy to hear. >>>>> >>>>> >>>>> *) This materialization probably explains the second job, however, >>>>> where the filtered data is joined is lost on me. This is not the cause >>>>> though, with just one materialize at the end, after UP, the data count >>>>> still doubled. The jobs then look like this: >>>>> >>>>> "Avro(target/stored/sIPhase)+EITPI+GBK+PITEI+Avro(/tmp/crunch369510677/p4)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+PITP]/[Avro(/tmp/crunch369510677/p4)]]+GBK+PWR+BTO+Avro(/tmp/crunch369510677/p6)" >>>>> >>>>> "[[Avro(target/stored/sIPhase)+S0+BTO]/[Avro(/tmp/crunch369510677/p6)]]+GBK+UP+Avro(/tmp/crunch369510677/p2)" >>>>> >>>>> "Avro(/tmp/crunch369510677/p2)+GetTraces+Avro(target/trace-dump/traces)" >>>>> "Avro(/tmp/crunch369510677/p2)+GetData+Avro(/tmp/crunch369510677/p8)" >>>>> >>>>> BR, >>>>> >>>>> Tim van Heugten >>>>> >>>>> >>>>> On Thu, Jan 31, 2013 at 9:27 PM, Gabriel Reid >>>>> <[email protected]>wrote: >>>>> >>>>>> Hi Tim, >>>>>> >>>>>> On 31 Jan 2013, at 10:45, Tim van Heugten <[email protected]> wrote: >>>>>> >>>>>> > Hi Gabriel, >>>>>> > >>>>>> > For the most part it is similar to what was send around recently on >>>>>> this mailinglist, see: >>>>>> > From Dave Beech <[email protected]> >>>>>> > Subject Question about mapreduce job planner >>>>>> > Date Tue, 15 Jan 2013 11:41:42 GMT >>>>>> > >>>>>> > So, the common path before multiple outputs branch is executed >>>>>> twice. Sometimes the issues seem related to unions though, i.e. multiple >>>>>> inputs. We seem to have been troubled by a grouped table parallelDo on a >>>>>> table-union-gbk that got its data twice (all grouped doubled in size). >>>>>> Inserting a materialize between the union and groupByKey solved the >>>>>> issue. >>>>>> > >>>>>> > These issues seem very fragile (so they're fixed easily by changing >>>>>> something that's irrelevant to the output), so usually we just add or >>>>>> remove a materialization to make it run again. >>>>>> > I'll see if I can cleanly reproduce the data duplication issue >>>>>> later this week. >>>>>> >>>>>> Ok, that would be great if you could replicate it in a small test, >>>>>> thanks! >>>>>> >>>>>> - Gabriel >>>>> >>>>> >>>>> >>>> >>> >> >
