CLIC: An Extensible and Efficient Cross-Platform Data Analytics System

With the ever-increasing data volume and application diversity, a modern data analytics job is generally built as a workflow consisting of multiple tasks. For either specific functionalities or higher performance, tasks in a workflow may need to be deployed on different data processing platforms. This article proposes CLIC, a highly extensible system for efficient cross-platform data analytics. To leverage the advantage of diverse platforms while alleviating development efforts, we propose an embedding-based operator encoding scheme and a Graph Convolutional Network model for efficient platform selection. Aiming at flexibly integrating new operators and platforms, CLIC is designed with a highly extensible system architecture that decouples the core functionalities from backend platforms. Experiments show that CLIC can significantly improve the performance of modern data analysis workflows with fast platform selection.