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Rafal Wojdyla commented on SPARK-33605: --------------------------------------- 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. -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org