Cross region S3 access from AWS EMR Spark - apache-spark

Question

My EMR is at us-west-1 but my S3 bucket is at us-east-1 and i'm getting below error.
I have tried s3://{bucketname}.s3.amazon.com but that would create a new bucket with s3.amazon.com.
How can access s3 bucket cross region?
com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.model.AmazonS3Exception: Moved Permanently (Service: Amazon S3; Status Code: 301; Error Code: 301 Moved Permanently; Request ID: FB1139D9BD8F409B), S3 Extended Request ID: pWK3X9BBRp8BLlXEHOx008RCdlZC64YFTounDYGtnwsAneR0IDP1Z/gmDudRoqWhDArfYLNRxk4=
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.handleErrorResponse(AmazonHttpClient.java:1389)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.executeOneRequest(AmazonHttpClient.java:902)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.executeHelper(AmazonHttpClient.java:607)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.doExecute(AmazonHttpClient.java:376)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.executeWithTimer(AmazonHttpClient.java:338)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.http.AmazonHttpClient.execute(AmazonHttpClient.java:287)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3826)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:1015)
at com.amazon.ws.emr.hadoop.fs.shaded.com.amazonaws.services.s3.AmazonS3Client.getObjectMetadata(AmazonS3Client.java:991)
at com.amazon.ws.emr.hadoop.fs.s3n.Jets3tNativeFileSystemStore.retrieveMetadata(Jets3tNativeFileSystemStore.java:212)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:191)
at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
at com.sun.proxy.$Proxy38.retrieveMetadata(Unknown Source)
at com.amazon.ws.emr.hadoop.fs.s3n.S3NativeFileSystem.getFileStatus(S3NativeFileSystem.java:780)
at org.apache.hadoop.fs.FileSystem.exists(FileSystem.java:1428)
at com.amazon.ws.emr.hadoop.fs.EmrFileSystem.exists(EmrFileSystem.java:313)
at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:85)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:60)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:58)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:86)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:86)
at org.apache.spark.sql.execution.datasources.DataSource.write(DataSource.scala:487)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:211)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:194)
at org.apache.spark.sql.DataFrameWriter.text(DataFrameWriter.scala:520)

Answer 1

This solution is working for me on emr-5.0.0/emr-5.0.3:
Add the following property to the core-site configuration
"fs.s3n.endpoint":"s3.amazonaws.com"

Answer 2

Did reached out to AWS support team and TLDR is that they are aware of this problem and they are currently working on it and hope to fix this for next EMR release but I have no eta.
For "s3a", you can use custom s3 end points within spark at runtime but this does not work for "s3" or "s3n".
Also, you can configure EMR to point to another s3 region on creation time but once you configure this way you are stuck with that region.
This EMRFS's regional bind is applied from EMR 4.7.2 and onward according to the support team.

Answer 3

As suggested by #codingtwinky in the comments, EMR 4.6.0 does not have this problem in the emr.hadoop.fs layer. My hadoop jobs now work in EMR 4.6.0, but do not work in 5.0.0 or 4.7.0.