Handling Highly Contended OLTP Workloads Using Fast Dynamic Partitioning

Research on transaction processing has made significant progress towards improving performance of main memory multicore OLTP systems under low contention. However, these systems struggle on workloads with lots of conflicts. Partitioned databases (and variants) perform well on high contention workloads that are statically partitionable, but time-varying workloads often make them impractical. Towards addressing this, we propose Strife---a novel transaction processing scheme that clusters transactions together dynamically and executes most of them without any concurrency control. Strife executes transactions in batches, where each batch is partitioned into disjoint clusters without any cross-cluster conflicts and a small set of residuals. The clusters are then executed in parallel with no concurrency control, followed by residuals separately executed with concurrency control. Strife uses a fast dynamic clustering algorithm that exploits a combination of random sampling and concurrent union-find data structure to partition the batch online, before executing it. Strife outperforms lock-based and optimistic protocols by up to 2x on high contention workloads. While Strife incurs about 50% overhead relative to partitioned systems in the statically partitionable case, it performs 2x better when such static partitioning is not possible and adapts to dynamically varying workloads.