SunStorm: Geographically distributed transactions over Aurora-style systems

There are two main approaches to scaling transactional database workloads: (1) a shared-nothing architecture with distributed transaction processing, or (2) an Aurora-style shared-storage architecture with separate compute and storage layers that scale independently. In option (2), the compute layer typically contains a single writer node and all other compute nodes are read-only. This may lead to scalability limits for write-intensive workloads, and introduces communication latency for write transactions that initiate far from the writer node. However, shared-nothing systems must pay the overhead of distributed coordination and commit protocols. In this paper, we discuss the design of a more scalable version of Aurora-style systems which supports multiple writer nodes managing geographically partitioned data. It yields many of the efficiency benefits of Aurora-style systems while removing the scalability bottleneck. Furthermore, geographic partitioning improves latency by over an order of magnitude for global applications in which clients from across the world can experience local write performance.