[
https://issues.apache.org/jira/browse/BEAM-11705?focusedWorklogId=543220&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-543220
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ASF GitHub Bot logged work on BEAM-11705:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 28/Jan/21 00:16
Start Date: 28/Jan/21 00:16
Worklog Time Spent: 10m
Work Description: pabloem opened a new pull request #13827:
URL: https://github.com/apache/beam/pull/13827
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Issue Time Tracking
-------------------
Worklog Id: (was: 543220)
Remaining Estimate: 0h
Time Spent: 10m
> Write to bigquery always assigns unique insert id per row causing performance
> issue
> -----------------------------------------------------------------------------------
>
> Key: BEAM-11705
> URL: https://issues.apache.org/jira/browse/BEAM-11705
> Project: Beam
> Issue Type: Improvement
> Components: io-py-gcp
> Reporter: Ning Kang
> Assignee: Pablo Estrada
> Priority: P2
> Time Spent: 10m
> Remaining Estimate: 0h
>
> The `ignore_insert_id` argument in BigQuery IO Connector
> https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery.py#L1471
> does not take effect.
> Because the implementation of sending insert rows request always uses an auto
> generated uuid even when the insert_ids is set to None when
> `ignore_insert_id` is True:
> https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery_tools.py#L1062
> The implementation should explicitly set insert_id as None instead of using a
> generated uuid, an example:
> https://github.com/googleapis/python-bigquery/blob/master/samples/table_insert_rows_explicit_none_insert_ids.py#L33
> An unique insert id per row would make the streaming inserts very slow.
> Additionally, the `DEFAULT_SHARDS_PER_DESTINATION` doesn't seem to take any
> effect when `ignore_insert_id` is True in the implementation because it
> skipped the `ReshufflePerKey`
> (https://github.com/apache/beam/blob/master/sdks/python/apache_beam/io/gcp/bigquery.py#L1422).
> When `ignore_insert_id` is True, we seem to lost the batch size control?
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