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arXiv · 2503.10036

Modeling Concurrency Control as a Learnable Function

Hexiang Pan·Shaofeng Cai·Tien Tuan Anh Dinh·Yuncheng Wu·Yeow Meng Chee·Gang Chen·Beng Chin Ooi
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Abstract

Concurrency control (CC) algorithms are important in modern transactional databases, as they enable high performance by executing transactions concurrently while ensuring correctness. However, state-of-the-art CC algorithms struggle to perform well across diverse workloads, and most do not consider workload drifts. In this paper, we propose NeurCC, a novel learned concurrency control algorithm that achieves high performance across diverse workloads. The algorithm is quick to optimize, making it robust against dynamic workloads. It learns a function that captures a large number of design choices from existing CC algorithms. The function is implemented as an efficient in-database lookup table that maps database states to concurrency control actions. The learning process is based on a combination of Bayesian optimization and a novel graph reduction search algorithm, which converges quickly to a function that achieves high transaction throughput. We compare NeurCC against five state-of-the-art CC algorithms and show that it consistently outperforms the baselines both in transaction throughput and in optimization time.

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Hexiang Pan, Shaofeng Cai, Tien Tuan Anh Dinh, Yuncheng Wu, Yeow Meng Chee, Gang Chen, Beng Chin Ooi. 2025-03-13. Modeling Concurrency Control as a Learnable Function. https://arxiv.org/abs/2503.10036

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