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https://issues.apache.org/jira/browse/SPARK-33605?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17240863#comment-17240863
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Rafal Wojdyla commented on SPARK-33605:
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Actually, the pyspark package includes the config for S3 via
{{core-default.xml}} that comes from {{hadoop-common}}, but not the AWS jars.
Further {{core-default.xml}} doesn't include defaults for GCS, which is a
Hadoop issue. But I still wonder if pyspark could make things easier for users
to package extra shaded FS jars, I understand though that would be an extra
complexity and increase the size of the package.
> Add GCS FS/connector to the dependencies akin to S3
> ---------------------------------------------------
>
> Key: SPARK-33605
> URL: https://issues.apache.org/jira/browse/SPARK-33605
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, Spark Core
> Affects Versions: 3.0.1
> Reporter: Rafal Wojdyla
> Priority: Major
>
> Spark comes with
> [hadoop-aws|https://github.com/apache/spark/blob/cb3fa6c9368e64184a5f7b19688181d11de9511c/hadoop-cloud/pom.xml#L74-L77]
> batteries included, which makes it easy to use with S3, for GCS to work
> users are required to manually configure the jars. This is especially
> problematic for python users who may not be accustomed to java dependencies
> etc. This is an example of workaround for pyspark:
> [pyspark_gcs|https://github.com/ravwojdyla/pyspark_gcs]. If we include the
> [GCS
> connector|https://cloud.google.com/dataproc/docs/concepts/connectors/cloud-storage],
> it would make things easier for GCS users.
> The fix could be to:
> * add the [gcs-connector
> dependency|https://mvnrepository.com/artifact/com.google.cloud.bigdataoss/gcs-connector]
> to the {{hadoop-cloud}}
> * test that there are not problematic classpath conflicts
> * test that pyspark package includes gcs connector in the jars
> Please let me know what you think.
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