Fetch a batch of rows from bigquery #5632
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superset/db_engine_specs.py
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| If this value is not set, the default value is set to 1, as described here, | ||
| https://googlecloudplatform.github.io/google-cloud-python/latest/_modules/google/cloud/bigquery/dbapi/cursor.html#Cursor | ||
| """ | ||
| arraysize = 5000 |
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I like the idea of setting the arraysize as a class attr, that would be the expected way to set it in derived classes. Maybe add those two lines in BaseEngineSpec.fetch_data:
if cls.arraysize:
cursor.arraysize = cls.arraysizeand set arraysize = None in the base class
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also let' s add a short comment on the arraysize = 5000 line that says where you got that number from (the default value in pybigquery)
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## master #5632 +/- ##
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+ Coverage 63.48% 63.49% +<.01%
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- Misses 8343 8344 +1
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* Fetch a batch of rows from bigquery * unused const * review comments (cherry picked from commit c9bd5a6)
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* Fetch a batch of rows from bigquery * unused const * review comments (cherry picked from commit c9bd5a6)
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* Fetch a batch of rows from bigquery * unused const * review comments (cherry picked from commit c9bd5a6)
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* Fetch a batch of rows from bigquery * unused const * review comments
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While running superset with Google BigQuery as the database I found that the queries are very slow. To fetch 1000 rows, it was taking approx. ~2 minutes. On further investigation I found that, they way our cursor is configured, it makes an REST API call for every row fetched, instead of one API call to fetch a batch of rows. pybigquery handles this batch fetch configuration, however, it does not work with cursor from the raw_connection used in superset.
After my change, the query to fetch 1000 rows from BigQuery takes ~1.5 seconds in Superset, down from ~120seconds. Which is on par with the query runtime, when run from BigQuery query editor.