Don't Look Back, Look into the Future: Prescient Data Partitioning and Migration for Deterministic Database Systems

Deterministic database systems have been shown to significantly improve the availability and scalability of a distributed database system deployed on a shared-nothing architecture across WAN while ensuring strong consistency. However, their scalability and performance advantages highly depend on the quality of data partitioning due to the reduced flexibility in transaction processing. Although a deterministic database system can employ workload driven data (re-)partitioning and live data migration algorithms to partition data, we found that the effectiveness of these algorithms is limited in complex real-world environments due to the unpredictability of machine workloads. In this paper, we present Hermes, a deterministic database system prototype that, for the first time, does not rely on sophisticated data partitioning to achieve high scalability and performance. Hermes employs a novel transaction routing mechanism that jointly optimizes the balance of machine workloads, data (re-)partitioning, and live data migration by looking into the queued transactions to be executed in the near future. We conducted extensive experiments which show that Hermes is able to yield 29% to 137% increase in transaction throughput as compared to the state-of-the-art systems under complex real-world workloads.