Intelligent Transaction Scheduling via Conflict Prediction in OLTP DBMS

TL;DR

This paper proposes a conflict prediction-based transaction scheduling method for OLTP DBMS that significantly improves throughput by intelligently avoiding conflicts, using historical data for better performance.

Contribution

It introduces a novel approach to estimate transaction conflicts and schedule transactions accordingly, enhancing OLTP system efficiency over traditional random scheduling.

Findings

Achieves approximately 40% throughput increase on 20-core systems.

Demonstrates effectiveness of conflict prediction in OLTP benchmarks.

Explores different transaction representations for fast conflict detection.

Abstract

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.