On Thu, Oct 30, 2014 at 2:43 PM, Jonás Mora <[email protected]>
 wrote:

> The ETL task is done in a background job that connects to the ftp server,
> iterates through the files, and process each one of them. The process
> consists in downloading the file, parsing it and transforming it to a
> bigquery supported format. I'm using the streaming insert API to load the
> data into bigquery but I read that this is not the best alternative for
> batch processing. I don't understand how to do it otherwise. I would really
> appreciate some help here.
> Source:
> https://cloud.google.com/developers/articles/bigquery-in-practice/#h.757bq5t4k2m8
>


Assuming you've transformed the source data files into CSV, you can use the
demo code at this link to batch-load the file data into BigQuery:
https://cloud.google.com/bigquery/loading-data-into-bigquery#loaddatagcs .
Note that *[sourceCSV]* is a URI to the CSV file hosted on Cloud Storage.

But if you're not experiencing any problems with streaming insert, there's
nothing wrong with continuing with your existing design.


On Thu, Oct 30, 2014 at 2:43 PM, Jonás Mora <[email protected]>
 wrote:

> Another issue I ran into is that when running multiple background threads
> with ndb.multi_put I get "Process terminated because the backend was
> stopped".
>


Is that the exact/only text that was printed out into logging? It's more
than likely you're hitting some memory limits or throughput throttles. Try
lowering the number of threads/increasing the number of instances running.
Also try reducing the size and number of puts you're doing within each
multi_put call.

Can you give a general description of how much data is added during each
multi_put call, and the general design of the entity model?


-----------------
-Vinny P
Technology & Media Consultant
Chicago, IL

App Engine Code Samples: http://www.learntogoogleit.com

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