Description on how to use a custom S3 endpoint (like Rados Gateway for Ceph)

custom_s3_endpoint_in_spark.md

Custom S3 endpoints with Spark

To be able to use custom endpoints with the latest Spark distribution, one needs to add an external package (hadoop-aws). Then, custum endpoints can be configured according to docs.

Use the hadoop-aws package

bin/spark-shell --packages org.apache.hadoop:hadoop-aws:2.7.2

SparkContext configuration

Add this to your application, or in the spark-shell:

sc.hadoopConfiguration.set("fs.s3a.endpoint", "<<ENDPOINT>>");
sc.hadoopConfiguration.set("fs.s3a.access.key","<<ACCESS_KEY>>");
sc.hadoopConfiguration.set("fs.s3a.secret.key","<<SECRET_KEY>>");

If your endpoint doesn't support HTTPS, then you'll need the following:

sc.hadoopConfiguration.set("fs.s3a.connection.ssl.enabled", "false");

S3 url usage

You can use s3a urls like this:

s3a://<<BUCKET>>/<<FOLDER>>/<<FILE>>

Also, it is possible to use the credentials in the path:

s3a://<<ACCESS_KEY>>:<<SECRET_KEY>>@<<BUCKET>>/<<FOLDER>>/<<FILE>>

@rockb1017 try this configuration(https://github.com/apache/hadoop/blob/branch-2.9.2/hadoop-tools/hadoop-aws/src/main/java/org/apache/hadoop/fs/s3a/Constants.java#L75):

<property>
  <name>fs.s3a.path.style.access</name>
  <value>true</value>
</property>

I am using spark structured streaming. So when should I set the values using sc.hadoopConfiguration()? Although I am new to spark, I am not sure if we can set these values after the spark session is created. Can someone help me our here?

Idk how common this use case is, but I have a pipeline that would be greatly simplified if I was able to use multiple S3 endpoints in a single spark submit. That is, I have a minio endpoint along with a standard aws s3 endpoint. Is there a way to configure the session on the fly such that I can use both?

@Jamesthegiantpeach were you able to resolve your issue?

Hello, fantastic work !
It works for me when I use a custom s3 (minio on Kubernetes) without TLS. But when I use my S3 with self-signed certificate TLS, event if I set the following value:

spark.hadoop.fs.s3a.endpoint=https://<my-endpoint-name>:9000

I get this exception:

Exception in thread "main" org.apache.hadoop.fs.s3a.AWSClientIOException: doesBucketExist on bucket:
com.amazonaws.SdkClientException: Unable to execute HTTP request: sun.security.validator.ValidatorException:
PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException:
unable to find valid certification path to requested target: 
Unable to execute HTTP request: sun.security.validator.ValidatorException: PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: 
unable to find valid certification path to requested target at org.apache.hadoop.fs.s3a.S3AUtils.translateException

I really don't find in Internet how to set the path to the self-signed certificate. Do you ?

@ysennoun IMO, the JVM(which runs spark) needs a custom ca cert. Googling like this: "custom certs into jvm"

Idk how common this use case is, but I have a pipeline that would be greatly simplified if I was able to use multiple S3 endpoints in a single spark submit. That is, I have a minio endpoint along with a standard aws s3 endpoint. Is there a way to configure the session on the fly such that I can use both?

@Jamesthegiantpeach: I have a similar use case. How did you solve this problem in your case?

I am getting this error when I am reading S3 local instance.

The confs I have set are -

val sparkConf = new SparkConf().setAppName("testing") .set("fs.s3a.endpoint", "http://127.0.0.1:9090") .set("fs.s3a.multipart.size", "104857600) .set("fs.s3a.connection.ssl.enabled", "false") .set("fs.s3a.access.key", "adfadf") .set("fs.s3a.secret.key", "qerqer")

java.lang.IllegalArgumentException
at java.base/java.util.concurrent.ThreadPoolExecutor.(ThreadPoolExecutor.java:1293)
at java.base/java.util.concurrent.ThreadPoolExecutor.(ThreadPoolExecutor.java:1215)
at org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:280)

You have to set the style because it is a different from retrieving from AWS directly
.set("fs.s3a.path.style.access", "true")

spark._jsc.hadoopConfiguration().set("fs.s3a.endpoint","https://.us-east-1.vpce.amazonaws.com")
spark._jsc.hadoopConfiguration().set("fs.s3a.connection.ssl.enabled", "false")
spark._jsc.hadoopConfiguration().set("fs.s3a.path.style.access", "true")
spark._jsc.hadoopConfiguration().set("fs.s3a.endpoint.region","us-east-1")
spark._jsc.hadoopConfiguration().set("fs.s3a.access.key", "")
spark._jsc.hadoopConfiguration().set("fs.s3a.secret.key", "*****")
spark._jsc.hadoopConfiguration().set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")

df = spark.read.csv("s3a://*****/dt=2022-05-27/*",header=True,inferSchema=True)
error :Caused by: com.amazonaws.services.s3.model.AmazonS3Exception: Bad Request (Service: Amazon S3; Status Code: 400; Error Code: 400 Bad Request;
Private bucket