OLTP systems can often improve throughput by batching transactions and processing them as a group. Batching has been used for optimizations such as message packing and group commits; however, there is little research on the benefits of a holistic approach to batching across a transaction’s entire life cycle.
In this paper, we present a framework to incorporate batching at multiple stages of transaction execution for OLTP systems based on optimistic concurrency control. Storage batching enables reordering of transaction reads and writes at the storage layer, reducing conflicts on the same object. Validator batching enables reordering of transactions before validation, reducing conflicts between transactions. Dependencies between transactions make transaction reordering a non-trivial problem, and we propose several efficient and practical algorithms that can be customized to various transaction precedence policies such as reducing tail latency. We also show how to reorder transactions with a thread-aware policy in multi-threaded OLTP architecture without a centralized validator.
In-depth experiments on a research prototype, an open source OLTP system, and a production OLTP system show that our techniques increase transaction throughput by up to 2.2x and reduce their tail latency by up to 71% compared with the state-of-the-art systems on workloads with high data contention.