![]() First, distributed transactions suffer from high latency because each of their accesses to remote data incurs a long network delay. In-depth experiments on a research prototype, an opensource 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 start-of-the-art systems on workloads with high data contention.ĭistributed transactions suffer from poor performance due to two major limiting factors. We also show how to reorder transactions with a thread-aware policy in multi-threaded OLTP architecture without a centralized validator. 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. Validator batching enables reordering of transactions before validation, reducing conflicts between transactions. Storage batching enables reordering of transaction reads and writes at the storage layer, reducing conflicts on the same object. In this paper, we present a framework to incorporate batching at multiple stages of transaction execution for OLTP systems based on optimistic concurrency control. 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. OLTP systems can often improve throughput by batching transactions and processing them as a group. Our results show that TicToc achieves up to 92% better throughput while reducing the abort rate by 3.3x over these previous algorithms. We implemented TicToc along with four other concurrency control algorithms in an in-memory, shared-everything OLTP DBMS and compared their performance on different workloads. TicToc removes the need for centralized timestamp allocation, and commits transactions that would be aborted by conventional T/O schemes. Instead of assigning timestamps to transactions, this protocol assigns read and write timestamps to data items and uses them to lazily compute a valid commit timestamp for each transaction. TicToc relies on a novel and provably correct data-driven timestamp management protocol. ![]() In this paper we present TicToc, a new optimistic concurrency control algorithm that avoids the scalability and concurrency bottlenecks of prior T/O schemes. This prevents the system from scaling to large numbers of cores. Previous research has shown that timestamp management is the key scalability bottleneck in concurrency control algorithms. Achieving higher performance on emerging many-core systems is difficult. Concurrency control for on-line transaction processing (OLTP) database management systems (DBMSs) is a nasty game.
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