According to the Apache Spark documentation, Spark jobs must authenticate with S3 to be able to read or write data in the object storage. There are different ways of achieving that:

• When Spark is running in a cloud infrastructure, the credentials are usually automatically set up.
• spark-submit reads the AWS_ACCESS_KEY, AWS_SECRET_KEY and AWS_SESSION_TOKEN environment variables and sets the associated authentication options for the s3n and s3a connectors to Amazon S3.
• In a Hadoop cluster, settings may be set in the core-site.xml file.
• Authentication details may be manually added to the Spark configuration in spark-defaults.conf.
• Alternatively, they can be programmatically set in the SparkConf instance used to configure the application’s SparkContext.

Honestly, I wouldn’t know much about the first option. It might have something to do with running Databricks on AWS.

The second option requires to set environment variables on all servers of the Spark cluster. If using Ansible, this can be done but only on a level of a task or role. This means that if you run a long-live Spark cluster, the variables will not be available once you start using the cluster.

The fourth option is the one that will receive the attention in this post. The spark-defaults.conf is the default configuration file and proper configuration in the file tunes your Spark cluster.

There are five configuration tuples needed to manipulate S3 data with Apache Spark. They are explained below.

Getting environmental variables into Docker

The following approach is suitable for a proof of concept or a testing. An enterprise solution should use service like Hashicorp Vault, Ansible Vault, AWS IAM or similar.

I am using Docker on Windows 10. The folder where DockerFile resides also has a file called aws_cred.env. Make sure this file is added to the .gitignore file so that it is not checked into source code repository! The env file holds the AWS key and secret key needed to authenticate with S3. The file structure is like this:

AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=

When running the docker container with option –env-file the environmental variables in the file get exported to the Docker container.

In the Ansible code, they can both be looked-up in the following way:

{{ lookup('env', 'AWS_ACCESS_KEY_ID') }}
{{ lookup('env', 'AWS_SECRET_ACCESS_KEY') }}

These can be used in the Jinja2 template file spark-defaults.conf.j2 to generate a Spark configuration file. The configuration tuples relevant in this case are these two:

spark.hadoop.fs.s3a.access.key {{ lookup('env', 'AWS_ACCESS_KEY_ID') }}
spark.hadoop.fs.s3a.secret.key {{ lookup('env', 'AWS_SECRET_ACCESS_KEY') }}

This now gives you the access to the S3 buckets, never mind if they are public or private.

The JAR files

First, the following tuple is mandatory for the Spark configuration:

spark.hadoop.fs.s3a.impl      org.apache.hadoop.fs.s3a.S3AFileSystem

This tells Spark what kind of file system it is dealing with. The JAR files are the library sources for this configuration.

Two libraries must be added to the instances of the Spark cluster:

• aws-java-sdk-1.7.4
• hadoop-aws-2.7.3

The above mentioned Jinja2 file also holds two configuration tuples relevant for these JAR files:

spark.driver.extraClassPath   /usr/spark-s3-jars/aws-java-sdk-1.7.4.jar:/usr/spark-s3-jars/hadoop-aws-2.7.3.jar
spark.executor.extraClassPath /usr/spark-s3-jars/aws-java-sdk-1.7.4.jar:/usr/spark-s3-jars/hadoop-aws-2.7.3.jar

Be careful with the versions because they must match the Spark version. The above combination has proven to work on Spark installation packages that support Hadoop 2.7. Last two tasks in this main.yml do the job for the Spark cluster.

Once the files are downloaded (for example, I download them to /usr/spark-s3-jars) Apache Spark can start reading and writing to the S3 object storage.