I will not go into details of how to install all the technologies and services from the list. This provision is a “long-live” provision since VPC has practically no cost. This is something I have automized using Terraform and Consul and described here. In order to use a service like EC2 in AWS, the Virtual Private Cloud must be established. Visual Studio Code for software development, running Powershell.Powershell for running Docker and provision.Docker as test and development environment.Ansible for software installation on the cluster.Consul for cluster’s configuration settings.Terraform for provisioning the infrastructure in AWS.Data Scientist is responsible for data input and data storage in the code ( example). Spark cluster is provisioned for a specific job which is executed and then the cluster is destroyed. Spark cluster serves as a solution for running various jobs. There are two ways of using this solution: In this case, the data scientist hast to configure the cluster using the YAML file and prepare a GitHub repository. One of the points with automation is to make data scientists more independant of data engineers: data engineer builds the solution and data scientist uses it without having the need for engineering experience. The cluster, once provisioning is completed, is therefor ready to use immediately. The configuration of the cluster is done prior to the provisioning using the Jinja2 file templates. In this post, I explain how I provision Apache Spark cluster on Amazon. Pay-as-you-go is the philosophy behind it. 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.Īutomation is the key word when it comes to Last two tasks in this main.yml do the job for the Spark cluster. The above combination has proven to work on Spark installation packages that support Hadoop 2.7. usr/spark-s3-jars/aws-java-sdk-1.7.4.jar:/usr/spark-s3-jars/hadoop-aws-2.7.3.jarīe careful with the versions because they must match the Spark version. The above mentioned Jinja2 file also holds two configuration tuples relevant for these JAR files: /usr/spark-s3-jars/aws-java-sdk-1.7.4.jar:/usr/spark-s3-jars/hadoop-aws-2.7.3.jar The JAR files are the library sources for this This tells Spark what kind of file system The JAR filesįirst, the following tuple is mandatory for the Spark configuration: .s3a.impl .s3a.S3AFileSystem This now gives you the access to the S3 buckets, never mind if they are public or private. In the Ansible code, they can both be looked-up Variables in the file get exported to the Docker container. The file structure is like this: AWS_ACCESS_KEY_ID= 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 folder where DockerFile resides also has a file called aws_cred.env. An enterprise solution should use service like Hashicorp Vault, Ansible Vault, AWS IAM or similar. The following approach is suitable for a proof of concept or a testing. Getting environmental variables into Docker There are five configuration tuples needed to manipulate S3 data with Apache Spark. Is the default configuration file and proper configuration in the file tunes The fourth option is the one that will receive This means that if you run a long-live Spark cluster, the variables will not be available once you start using the cluster. If using Ansible, this can be done but only on a level of a task or role. The second option requires to set environment variables on all servers of the Spark cluster. It might have something to do with running Databricks on AWS. Honestly, I wouldn’t know much about the first option. Programmatically set in the SparkConf instance used to configure the Manually added to the Spark configuration in nf. Sets the associated authentication options for the s3n and s3a connectors to There are different ways of achieving that:Ĭloud infrastructure, the credentials are usually automatically set up.ĪWS_ACCESS_KEY, AWS_SECRET_KEY and AWS_SESSION_TOKEN environment variables and WARN ServletHandler: /api/v1/applications (1)Īccording to the Apache Spark documentation, Spark jobs must authenticate with S3 to be able to read or write data in the object storage.Provision Apache Spark in AWS with Hashistack and Ansible.Streaming messages from Kafka to EventHub with MirrorMaker.Capturing messages in Event Hubs to Blob Storage.Zealpath and Trivago: case for AWS Cloud Engineer position.Automating access from Apache Spark to S3 with Ansible.Using Python 3 with Apache Spark on CentOS 7 with help of virtualenv.
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