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https://issues.apache.org/jira/browse/SPARK-33605?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Rafal Wojdyla updated SPARK-33605:
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Description:
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.
was:
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.
> 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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