TxnSails: Achieving Serializable Transaction Scheduling with Self-Adaptive Isolation Level Selection

TL;DR

TxnSails introduces a self-adaptive system that dynamically selects the optimal isolation level for transaction scheduling, balancing performance and serializability, and outperforms existing solutions significantly.

Contribution

It presents a novel self-adaptive approach combining a unified concurrency control algorithm and deep learning for optimal isolation level selection in transaction processing.

Findings

Outperforms state-of-the-art solutions by up to 26.7x

Outperforms PostgreSQL's serializable level by up to 4.8x

Achieves serializability with minimal overhead

Abstract

Achieving the serializable isolation level, regarded as the gold standard for transaction processing, is costly. Recent studies reveal that adjusting specific query patterns within a workload can still achieve serializability even at lower isolation levels. Nevertheless, these studies typically overlook the trade-off between the performance advantages of lower isolation levels and the overhead required to maintain serializability, potentially leading to suboptimal isolation level choices that fail to maximize performance. In this paper, we present TxnSails, a middle-tier solution designed to achieve serializable scheduling with self-adaptive isolation level selection. First, TxnSails incorporates a unified concurrency control algorithm that achieves serializability at lower isolation levels with minimal additional overhead. Second, TxnSails employs a deep learning method to characterize the trade-off between the performance benefits and overhead associated with lower isolation levels, thus predicting the optimal isolation level. Finally, TxnSails implements a cross-isolation validation mechanism to ensure serializability during real-time isolation level transitions. Extensive experiments demonstrate that TxnSails outperforms state-of-the-art solutions by up to 26.7x and PostgreSQL's serializable isolation level by up to 4.8x.