Spark Scala S3 storage: permission denied

Question

I've read a lot of topic on Internet on how to get working Spark with S3 still there's nothing working properly. I've downloaded : Spark 2.3.2 with hadoop 2.7 and above.

I've copied only some libraries from Hadoop 2.7.7 (which matches Spark/Hadoop version) to Spark jars folder:

• hadoop-aws-2.7.7.jar
• hadoop-auth-2.7.7.jar
• aws-java-sdk-1.7.4.jar

Still I can't use nor S3N nor S3A to get my file read by spark:

For S3A I have this exception:

sc.hadoopConfiguration.set("fs.s3a.access.key","myaccesskey")
sc.hadoopConfiguration.set("fs.s3a.secret.key","mysecretkey")
val file = sc.textFile("s3a://my.domain:8080/test_bucket/test_file.txt")
com.amazonaws.services.s3.model.AmazonS3Exception: Status Code: 403, AWS Service: Amazon S3, AWS Request ID: AE203E7293ZZA3ED, AWS Error Code: null, AWS Error Message: Forbidden

Using this piece of Python, and some more code, I can list my buckets, list my files, download files, read files from my computer and get file url. This code gives me the following file url:

https://my.domain:8080/test_bucket/test_file.txt?Signature=%2Fg3jv96Hdmq2450VTrl4M%2Be%2FI%3D&Expires=1539595614&AWSAccessKeyId=myaccesskey

How should I install / set up / download to get spark able to read and write from my S3 server ?

Edit 3:

Using debug tool in comment here's the result.
Seems like the issue is with a signature thing not sure what it means.

Answer 1

First you will need to download aws-hadoop.jar and aws-java-sdk.jar that matches the install of your spark-hadoop release and add them to the jars folder inside spark folder.
Then you will need to precise the server you will use and enable path style if your S3 server do not support dynamic DNS:

sc.hadoopConfiguration.set("fs.s3a.path.style.access","true")
sc.hadoopConfiguration.set("fs.s3a.endpoint","my.domain:8080")
#I had to change signature version because I have an old S3 api implementation:
sc.hadoopConfiguration.set("fs.s3a.signing-algorithm","S3SignerType")

Here's my final code:

sc.hadoopConfiguration.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
val tmp = sc.textFile("s3a://test_bucket/test_file.txt")
sc.hadoopConfiguration.set("fs.s3a.access.key","mykey")
sc.hadoopConfiguration.set("fs.s3a.secret.key","mysecret")
sc.hadoopConfiguration.set("fs.s3a.endpoint","my.domain:8080")
sc.hadoopConfiguration.set("fs.s3a.connection.ssl.enabled","true")
sc.hadoopConfiguration.set("fs.s3a.path.style.access","true")
sc.hadoopConfiguration.set("fs.s3a.signing-algorithm","S3SignerType")
tmp.count()

I would recommand to put most of the settings inside spark-defaults.conf:

spark.hadoop.fs.s3a.impl                   org.apache.hadoop.fs.s3a.S3AFileSystem
spark.hadoop.fs.s3a.path.style.access      true
spark.hadoop.fs.s3a.endpoint               mydomain:8080
spark.hadoop.fs.s3a.connection.ssl.enabled true
spark.hadoop.fs.s3a.signing-algorithm      S3SignerType

One of the issue I had has been to set spark.hadoop.fs.s3a.connection.timeout to 10 but this value is set in millisecond prior to Hadoop 3 and it gives you a very long timeout; error message would appear 1.5 minute after the attempt to read a file.

PS:
Special thanks to Steve Loughran.
Thank you a lot for the precious help.