Automatic Workload Pinning and Regression Detection for Apache Spark

This technical report discusses the implementation of Versionless Spark in Databricks, which aims to simplify the process of upgrading Apache Spark versions for users. Traditionally, upgrading Spark versions has been challenging due to the tight coupling between application code and engine code, leading to dependency issues and reluctance to adopt new features. The report outlines how Databricks leverages Spark Connect to decouple client applications from the Spark engine, enabling seamless upgrades and minimizing disruptions. It details the architecture of Versionless Spark, which allows users to run workloads indefinitely on the latest version without requiring code changes. The report also describes the multi-user capabilities of Databricks' standard clusters, which ensure secure and isolated environments for users. Additionally, it explains how the serverless architecture further enhances resource management and user experience by dynamically provisioning resources based on workload requirements, ultimately streamlining the upgrade process and improving operational efficiency.