Pangea: Monolithic distributed storage for data analytics

Abstract

Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and nonshared execution data in separate systems such as a distributed file system like HDFS, an in-memory file system like Alluxio, and a computation framework like Spark. Such layering introduces significant performance and management costs. In this paper we propose a single system called Pangea that can manage all data-both intermediate and long-lived data, and their buffer/caching, data placement optimization, and failure recovery-all in one monolithic distributed storage system, without any layering. We present a detailed performance evaluation of Pangea and show that its performance compares favorably with several widely used layered systems such as Spark.