Hi again, So it scales. Now my pipeline is running in two parts:
1. Reading files content (~400) and then GroupByKey 2. From GroupByKey transform and write in bigquery (~50M) 2 is scaling as expected. 1 takes about 25 minutes on my files and 2 about 35 minutes scaling. But what if I want to Window so that the second part starts sooner and the process is more parallel? I tried to add a 60 seconds FixedWindow time but it's not working (Job ID : 2017-06-06_04_36_01-9894155361321571250) Regards, *Sébastien MORAND* Team Lead Solution Architect Technology & Operations / Digital Factory Veolia - Group Information Systems & Technology (IS&T) Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 Bureau 0144C (Ouest) 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France *www.veolia.com <http://www.veolia.com>* <http://www.veolia.com> <https://www.facebook.com/veoliaenvironment/> <https://www.youtube.com/user/veoliaenvironnement> <https://www.linkedin.com/company/veolia-environnement> <https://twitter.com/veolia> On 6 June 2017 at 01:24, Morand, Sebastien <[email protected]> wrote: > Fine, it scales ... Thank you very much. > > *Sébastien MORAND* > Team Lead Solution Architect > Technology & Operations / Digital Factory > Veolia - Group Information Systems & Technology (IS&T) > Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 > <+33%201%2085%2057%2071%2008> > Bureau 0144C (Ouest) > 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France > *www.veolia.com <http://www.veolia.com>* > <http://www.veolia.com> > <https://www.facebook.com/veoliaenvironment/> > <https://www.youtube.com/user/veoliaenvironnement> > <https://www.linkedin.com/company/veolia-environnement> > <https://twitter.com/veolia> > > On 6 June 2017 at 00:31, Morand, Sebastien <[email protected]> > wrote: > >> Thank you Eugene, >> >> I'm trying the Sourabh way (and yours since it looks like it's the same >> idea) and let you know if it's better. >> >> Regards, >> >> *Sébastien MORAND* >> Team Lead Solution Architect >> Technology & Operations / Digital Factory >> Veolia - Group Information Systems & Technology (IS&T) >> Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 >> <+33%201%2085%2057%2071%2008> >> Bureau 0144C (Ouest) >> 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France >> *www.veolia.com <http://www.veolia.com>* >> <http://www.veolia.com> >> <https://www.facebook.com/veoliaenvironment/> >> <https://www.youtube.com/user/veoliaenvironnement> >> <https://www.linkedin.com/company/veolia-environnement> >> <https://twitter.com/veolia> >> >> On 5 June 2017 at 23:31, Eugene Kirpichov <[email protected]> wrote: >> >>> I looked at the job ID you quoted, and yes, it suffers from excessive >>> fusion. I wish we had tooling to automatically detect that and emit a >>> warning, but we don't have that yet. >>> >>> Here's an example of how you can break fusion: https://github.com/apa >>> che/beam/blob/master/sdks/java/io/jdbc/src/main/java/org/apa >>> che/beam/sdk/io/jdbc/JdbcIO.java#L326-L339 >>> >>> On Mon, Jun 5, 2017 at 1:27 PM Sourabh Bajaj <[email protected]> >>> wrote: >>> >>>> Yes you're correct. >>>> >>>> On Mon, Jun 5, 2017 at 1:23 PM Morand, Sebastien < >>>> [email protected]> wrote: >>>> >>>>> Between parenthesis of each step I meant the number of records in >>>>> output >>>>> >>>>> When I ungroup I send again the 200 data? not the 20M? >>>>> >>>>> Shouldn't I do instead: >>>>> Read (200) -> GroupByKey (200) -> UnGroup(20M) -> combine (20M) -> >>>>> clean (20M) -> filter (20M) -> insert >>>>> >>>>> >>>>> *Sébastien MORAND* >>>>> Team Lead Solution Architect >>>>> Technology & Operations / Digital Factory >>>>> Veolia - Group Information Systems & Technology (IS&T) >>>>> Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 >>>>> <+33%201%2085%2057%2071%2008> >>>>> Bureau 0144C (Ouest) >>>>> 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France >>>>> *www.veolia.com <http://www.veolia.com>* >>>>> <http://www.veolia.com> >>>>> <https://www.facebook.com/veoliaenvironment/> >>>>> <https://www.youtube.com/user/veoliaenvironnement> >>>>> <https://www.linkedin.com/company/veolia-environnement> >>>>> <https://twitter.com/veolia> >>>>> >>>>> On 5 June 2017 at 22:17, Sourabh Bajaj <[email protected]> >>>>> wrote: >>>>> >>>>>> I think you need to do something like: >>>>>> >>>>>> Read (200) -> GroupByKey (200) -> UnGroup(200) [Not this can be on >>>>>> 200 different workers] -> combine (20M) -> clean (20M) -> filter >>>>>> (20M) -> insert >>>>>> >>>>>> On Mon, Jun 5, 2017 at 1:14 PM Morand, Sebastien < >>>>>> [email protected]> wrote: >>>>>> >>>>>>> Yes fusion looks like my problem. A job ID to look at: >>>>>>> 2017-06-05_10_14_25-5856213199384263626. >>>>>>> >>>>>>> The point is in your link: >>>>>>> << >>>>>>> For example, one case in which fusion can limit Dataflow's ability >>>>>>> to optimize worker usage is a "high fan-out" ParDo. In such an >>>>>>> operation, >>>>>>> you might have an input collection with relatively few elements, but the >>>>>>> ParDo produces an output with hundreds or thousands of times as many >>>>>>> elements, followed by another ParDo >>>>>>> >> >>>>>>> >>>>>>> This is exactly what I'm doing in the step >>>>>>> transform-combine-7d5ad942 in the above job id. >>>>>>> >>>>>>> As fas as I understand, I should create a GroupByKey after the >>>>>>> transform-combine-7d5ad942 on a unique field and then ungroup the data? >>>>>>> (meaning I add two operations in the pipeline to help the worker? >>>>>>> >>>>>>> Right now: >>>>>>> Read (200) -> combine (20M) -> clean (20M) -> filter (20M) -> insert >>>>>>> >>>>>>> Will become: >>>>>>> Read (200) -> combine (20M) -> GroupByKey (20M) -> ungroup (20M) -> >>>>>>> clean (20M) -> filter (20M) -> insert >>>>>>> >>>>>>> It this the right way? >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> *Sébastien MORAND* >>>>>>> Team Lead Solution Architect >>>>>>> Technology & Operations / Digital Factory >>>>>>> Veolia - Group Information Systems & Technology (IS&T) >>>>>>> Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 >>>>>>> <+33%201%2085%2057%2071%2008> >>>>>>> Bureau 0144C (Ouest) >>>>>>> 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France >>>>>>> *www.veolia.com <http://www.veolia.com>* >>>>>>> <http://www.veolia.com> >>>>>>> <https://www.facebook.com/veoliaenvironment/> >>>>>>> <https://www.youtube.com/user/veoliaenvironnement> >>>>>>> <https://www.linkedin.com/company/veolia-environnement> >>>>>>> <https://twitter.com/veolia> >>>>>>> >>>>>>> On 5 June 2017 at 21:42, Eugene Kirpichov <[email protected]> >>>>>>> wrote: >>>>>>> >>>>>>>> Do you have a Dataflow job ID to look at? >>>>>>>> It might be due to fusion https://cloud.google.co >>>>>>>> m/dataflow/service/dataflow-service-desc#preventing-fusion >>>>>>>> >>>>>>>> On Mon, Jun 5, 2017 at 12:13 PM Prabeesh K. <[email protected]> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Please try using *--worker_machine_type* n1-standard-4 or >>>>>>>>> n1-standard-8 >>>>>>>>> >>>>>>>>> On 5 June 2017 at 23:08, Morand, Sebastien < >>>>>>>>> [email protected]> wrote: >>>>>>>>> >>>>>>>>>> I do have a problem with my tries to test scaling on dataflow. >>>>>>>>>> >>>>>>>>>> My dataflow is pretty simple: I get a list of files from pubsub, >>>>>>>>>> so the number of files I'm going to use to feed the flow is well >>>>>>>>>> known at >>>>>>>>>> the begining. Here are my steps: >>>>>>>>>> Let's say I have 200 files containing about 20,000,000 of records >>>>>>>>>> >>>>>>>>>> - *First Step:* Read file contents from storage: files are >>>>>>>>>> .tar.gz containing each 4 files (CSV). I return the file content >>>>>>>>>> as the >>>>>>>>>> whole in a record >>>>>>>>>> *OUT:* 200 records (one for each file containing the data of >>>>>>>>>> all 4 files). Bascillacy it's a dict : {file1: content_of_file1, >>>>>>>>>> file2: >>>>>>>>>> content_of_file2, etc...} >>>>>>>>>> >>>>>>>>>> - *Second step:* Joining the data of the 4 files in one >>>>>>>>>> record (the main file contains foreign key to get information >>>>>>>>>> from the >>>>>>>>>> other files) >>>>>>>>>> *OUT:* 20,000,000 records each for every line in the files. >>>>>>>>>> Each record is a list of string >>>>>>>>>> >>>>>>>>>> - *Third step:* cleaning data (convert to prepare integration >>>>>>>>>> in bigquery) and set them as a dict where keys are bigquery >>>>>>>>>> column name. >>>>>>>>>> *OUT:* 20,000,000 records as dict for each record >>>>>>>>>> >>>>>>>>>> - *Fourth step:* insert into bigquery >>>>>>>>>> >>>>>>>>>> So the first step return 200 records, but I have 20,000,000 >>>>>>>>>> records to insert. >>>>>>>>>> This takes about 1 hour and half and always use 1 worker ... >>>>>>>>>> >>>>>>>>>> If I manually set the number of workers, it's not really faster. >>>>>>>>>> So for an unknow reason, it doesn't scale, any ideas how to do it? >>>>>>>>>> >>>>>>>>>> Thanks for any help. >>>>>>>>>> >>>>>>>>>> *Sébastien MORAND* >>>>>>>>>> Team Lead Solution Architect >>>>>>>>>> Technology & Operations / Digital Factory >>>>>>>>>> Veolia - Group Information Systems & Technology (IS&T) >>>>>>>>>> Cell.: +33 7 52 66 20 81 / Direct: +33 1 85 57 71 08 >>>>>>>>>> <+33%201%2085%2057%2071%2008> >>>>>>>>>> Bureau 0144C (Ouest) >>>>>>>>>> 30, rue Madeleine-Vionnet - 93300 Aubervilliers, France >>>>>>>>>> *www.veolia.com <http://www.veolia.com>* >>>>>>>>>> <http://www.veolia.com> >>>>>>>>>> <https://www.facebook.com/veoliaenvironment/> >>>>>>>>>> <https://www.youtube.com/user/veoliaenvironnement> >>>>>>>>>> <https://www.linkedin.com/company/veolia-environnement> >>>>>>>>>> <https://twitter.com/veolia> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> ------------------------------------------------------------ >>>>>>>>>> -------------------------------- >>>>>>>>>> This e-mail transmission (message and any attached files) may >>>>>>>>>> contain information that is proprietary, privileged and/or >>>>>>>>>> confidential to >>>>>>>>>> Veolia Environnement and/or its affiliates and is intended >>>>>>>>>> exclusively for >>>>>>>>>> the person(s) to whom it is addressed. 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