Is it because of my output I'm participating writing to 180 partitions? Or because of more pipeline operations & transforms
On Thu, Jan 11, 2018 at 10:48 AM, Chamikara Jayalath <[email protected]> wrote: > Dataflow service has a 10MB request size limit. Seems like you are hitting > this. See following for more information regarding this. > https://cloud.google.com/dataflow/pipelines/troubleshooting-your-pipeline > > Looks like your are hitting this due to number of partitions. I don't > think currently there's a good solution other than to execute multiple > jobs. We hope to introduce dynamic destinations feature to Python BQ sink > in the near future which will allow you to write this using a more compact > pipeline. > > Thanks, > Cham > > > On Wed, Jan 10, 2018 at 10:22 PM Unais Thachuparambil < > [email protected]> wrote: > >> I wrote a python dataflow job to read data from biqquery and do some >> transform and save the result as bq table.. >> >> I tested with 8 days data it works fine - when I scaled to 180 days I’m >> getting the below error >> >> ```"message": "Request payload size exceeds the limit: 10485760 >> bytes.",``` >> >> >> ```pitools.base.py.exceptions.HttpError: HttpError accessing < >> https://dataflow.googleapis.com/v1b3/projects/careem-mktg- >> dwh/locations/us-central1/jobs?alt=json>: response: <{'status': '400', >> 'content-length': '145', 'x-xss-protection': '1; mode=block', >> 'x-content-type-options': 'nosniff', 'transfer-encoding': 'chunked', >> 'vary': 'Origin, X-Origin, Referer', 'server': 'ESF', '-content-encoding': >> 'gzip', 'cache-control': 'private', 'date': 'Wed, 10 Jan 2018 22:49:32 >> GMT', 'x-frame-options': 'SAMEORIGIN', 'alt-svc': 'hq=":443"; ma=2592000; >> quic=51303431; quic=51303339; quic=51303338; quic=51303337; >> quic=51303335,quic=":443"; ma=2592000; v="41,39,38,37,35"', 'content-type': >> 'application/json; charset=UTF-8'}>, content <{ >> "error": { >> "code": 400, >> "message": "Request payload size exceeds the limit: 10485760 bytes.", >> "status": "INVALID_ARGUMENT" >> } >> >> ``` >> >> >> In short, this is what I’m doing >> 1 - Reading data from bigquery table using >> ```beam.io.BigQuerySource ``` >> 2 - Partitioning each days using >> ``` beam.Partition ``` >> 3- Applying transforms each partition and combining some output >> P-Collections. >> 4- After the transforms, the results are saved to a biqquery date >> partitioned table. >> >
