通过 OLTP DBMS 中的冲突预测实现智能事务调度

Tieying Zhang, Anthony Tomasic, Andrew Pavlo
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引用次数: 0

摘要

当前主内存联机事务处理(OLTP)数据库管理系统(DBMS)的架构通常使用随机调度将事务分配给线程。这种方法实现了线程间负载的一致性,但却忽略了事务间冲突的可能性。如果 DBMS 能够估计事务冲突的可能性,然后智能地调度事务以避免冲突,那么系统就能提高性能。首先,冲突发生的条件很复杂,与调度决策的时间相距甚远。其次,事务必须以紧凑高效的方式表示,以便快速检测冲突。第三,考虑到潜在冲突的某些证据,数据库管理系统必须以最小化冲突的方式调度事务。在本文中,我们系统地探讨了解决这些问题的设计决策。然后,我们在标准 OLTP 基准上实证测量了不同表示法对性能的影响。我们的结果表明,使用历史记录的智能调度在 20 核机器上将吞吐量提高了 $\sim$40\% 。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Transaction Scheduling via Conflict Prediction in OLTP DBMS
Current architectures for main-memory online transaction processing (OLTP) database management systems (DBMS) typically use random scheduling to assign transactions to threads. This approach achieves uniform load across threads but it ignores the likelihood of conflicts between transactions. If the DBMS could estimate the potential for transaction conflict and then intelligently schedule transactions to avoid conflicts, then the system could improve its performance. Such estimation of transaction conflict, however, is non-trivial for several reasons. First, conflicts occur under complex conditions that are far removed in time from the scheduling decision. Second, transactions must be represented in a compact and efficient manner to allow for fast conflict detection. Third, given some evidence of potential conflict, the DBMS must schedule transactions in such a way that minimizes this conflict. In this paper, we systematically explore the design decisions for solving these problems. We then empirically measure the performance impact of different representations on standard OLTP benchmarks. Our results show that intelligent scheduling using a history increases throughput by $\sim$40\% on 20-core machine.
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