Fabian, Turns out I was wrong. My flow was in fact running in two separate jobs due to me trying to use a local variable calculated by ...distinct().count() in a downstream flow. The second flow indeed set parallelism correctly! Thank you for the help. :)
On Wed, Oct 4, 2017 at 8:01 AM, Fabian Hueske <fhue...@gmail.com> wrote: > Hi Garrett, > > that's strange. DataSet.reduceGroup() will create a non-parallel > GroupReduce operator. > So even without setting the parallelism manually to 1, the operator should > not run in parallel. > What might happen though is that a combiner is applied to locally reduce > the data before it is shipped to the single instance. > Does your GroupReduceFunction implement a Combiner interface? > > I'm not aware of visualization problems of the web UI. > Can you maybe share a screenshot of the UI showing the issue? > > Thanks, Fabian > > 2017-10-03 21:57 GMT+02:00 Garrett Barton <garrett.bar...@gmail.com>: > >> Gábor >> , >> Thank you for the reply, I gave that a go and the flow still showed >> parallel 90 for each step. Is the ui not 100% accurate perhaps? >> >> To get around it for now I implemented a partitioner that threw all the >> data to the same partition, hack but works! >> >> On Tue, Oct 3, 2017 at 4:12 AM, Gábor Gévay <gga...@gmail.com> wrote: >> >>> Hi Garrett, >>> >>> You can call .setParallelism(1) on just this operator: >>> >>> ds.reduceGroup(new GroupReduceFunction...).setParallelism(1) >>> >>> Best, >>> Gabor >>> >>> >>> >>> On Mon, Oct 2, 2017 at 3:46 PM, Garrett Barton <garrett.bar...@gmail.com> >>> wrote: >>> > I have a complex alg implemented using the DataSet api and by default >>> it >>> > runs with parallel 90 for good performance. At the end I want to >>> perform a >>> > clustering of the resulting data and to do that correctly I need to >>> pass all >>> > the data through a single thread/process. >>> > >>> > I read in the docs that as long as I did a global reduce using >>> > DataSet.reduceGroup(new GroupReduceFunction....) that it would force >>> it to a >>> > single thread. Yet when I run the flow and bring it up in the ui, I >>> see >>> > parallel 90 all the way through the dag including this one. >>> > >>> > Is there a config or feature to force the flow back to a single >>> thread? Or >>> > should I just split this into two completely separate jobs? I'd >>> rather not >>> > split as I would like to use flinks ability to iterate on this alg and >>> > cluster combo. >>> > >>> > Thank you >>> >> >> >
