Elastic data partitioning for cloud-based SQL processing systems

One of the key advantages of cloud computing is the elasticity in which computing resources such as virtual machines can be increased or decreased. Current state-of-the-art shared-nothing parallel SQL processing systems, on the other hand, are often designed and optimized for a fixed number of database nodes. To take advantage of the elasticity afforded by cloud computing, cloud-based SQL processing systems need the ability to repartition the data easily when the number of database nodes is scaled up or down. In this paper, we investigate the problem of supporting elastic partitioning of data in cloud-based parallel SQL processing systems. We propose several algorithms and associated data organization techniques that minimizes the re-partitioning of tuples and the movement of data between nodes. Our experimental evaluation demonstrates the effectiveness of the proposed methods.