Towards Optimal Transaction Scheduling

Maximizing transaction throughput is key to high-performance database systems, which focus on minimizing data access conflicts to improve performance. However, finding efficient schedules that reduce conflicts remains an open problem. For efficiency, previous scheduling techniques consider only a small subset of possible schedules. In this work, we propose systematically exploring the entire schedule space, proactively identifying efficient schedules, and executing them precisely during execution to improve throughput. We introduce a greedy scheduling policy, SMF, that efficiently finds fast schedules and outperforms state-of-the-art search techniques. To realize the benefits of these schedules in practice, we develop a schedule-first concurrency control protocol, MVSchedO, that enforces fine-grained operation orders. We implement both in our system R-SMF, a modified version of RocksDB, to achieve up to a 3.9× increase in throughput and 3.2× reduction in tail latency on a range of benchmarks and real-world workloads.