Amazon SageMaker provides multiple options for running distributed data processing jobs with Apache Spark. These options include running Spark applications interactively from Amazon SageMaker Studio, using a pre-built SageMaker Spark container to run Spark applications as batch jobs on a fully managed distributed cluster, connecting Studio notebooks with Amazon EMR clusters, or running Spark clusters on Amazon EC2.
By using Amazon SageMaker Studio and AWS Glue Interactive Sessions, you can run Spark jobs with a serverless cluster, choosing between Apache Spark or Ray for processing large datasets. This eliminates the need for cluster management and provides flexibility for data processing and model training.
Additionally, you can install and run Spark History Server on SageMaker Studio and access the Spark UI directly from the SageMaker Studio IDE. This allows you to analyze Spark logs produced by various AWS services, such as AWS Glue Interactive Sessions, SageMaker Processing jobs, and Amazon EMR, stored in an Amazon S3 bucket.
The solution integrates Spark History Server into the Jupyter Server app in SageMaker Studio, enabling direct access to Spark logs. It also provides a command-line interface (CLI) called sm-spark-cli for managing Spark History Server from the SageMaker Studio system terminal.
To host Spark UI on SageMaker Studio, you can follow the provided steps to install the necessary components and start the Spark UI using the sm-spark-cli. You can configure the S3 location where the event logs are stored and set up the Spark event log location directly from notebooks or the SageMaker Python SDK.
For IT admins, the installation of the Spark UI can be automated for SageMaker Studio users using a lifecycle configuration. This can be done for all user profiles or specific ones under a SageMaker Studio domain.
To clean up the Spark UI, you can manually uninstall it or automatically uninstall it for all SageMaker Studio user profiles using the domain settings.
In conclusion, the Spark UI on SageMaker Studio enables ML and data engineering teams to access and analyze Spark logs using scalable cloud compute. It provides a standardized and expedited solution for provisioning in the cloud, avoiding the need for custom development environments for ML projects.