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https://issues.apache.org/jira/browse/BEAM-10917?focusedWorklogId=642514&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-642514
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ASF GitHub Bot logged work on BEAM-10917:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 26/Aug/21 18:30
Start Date: 26/Aug/21 18:30
Worklog Time Spent: 10m
Work Description: kmjung commented on pull request #15185:
URL: https://github.com/apache/beam/pull/15185#issuecomment-906643295
@satybald the downside of maintaining 100% compatibility directly in the
Beam source is that it reduces the efficiency of the pipeline -- you would be
paying for the VM cycles to convert every BigQuery `DATETIME` record from
string to native Python when using the storage API, for example. An extreme
case is on the Java side, where a non-zero number of pipelines read BigQuery
data using the read API in Avro format and then [convert it to
JSON](https://github.com/apache/beam/blob/3a5b52cd112ab7aad894808cf08775fe93adf7f4/sdks/java/io/google-cloud-platform/src/main/java/org/apache/beam/sdk/io/gcp/bigquery/BigQueryIO.java#L599)
for consumption by the pipeline.
With that said, if your proposal is that we should provide a single,
centrally-maintained compat layer that customers can make use of, that makes
sense to me. The `DATETIME` issue is particularly galling as an end user --
let's start there.
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Issue Time Tracking
-------------------
Worklog Id: (was: 642514)
Time Spent: 24h 10m (was: 24h)
> Implement a BigQuery bounded source using the BigQuery storage API
> ------------------------------------------------------------------
>
> Key: BEAM-10917
> URL: https://issues.apache.org/jira/browse/BEAM-10917
> Project: Beam
> Issue Type: New Feature
> Components: io-py-gcp
> Reporter: Kenneth Jung
> Assignee: Kanthi Subramanian
> Priority: P3
> Time Spent: 24h 10m
> Remaining Estimate: 0h
>
> The Java SDK contains a bounded source implementation which uses the BigQuery
> storage API to read from BigQuery. We should implement the same for Python.
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