# Polyjuice: High-Performance Transactions via Learned Concurrency Control

arXiv: https://arxiv.org/abs/2105.10329 · HTML: https://mlsanity.com/paper/2105.10329/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2021.

## Built on by (most cited first, 4 of 4)

- [A Survey on Deep Reinforcement Learning for Data Processing and Analytics](https://mlsanity.com/paper/2108.04526/) (2021)
- [NeurDB: An AI-powered Autonomous Data System](https://mlsanity.com/paper/2405.03924/) (2024)
- [NeurDB: On the Design and Implementation of an AI-powered Autonomous Database](https://mlsanity.com/paper/2408.03013/) (2024)
- [AI-Driven Research for Databases](https://mlsanity.com/paper/2604.06566/) (2026)

## Abstract (arXiv)

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than two-phase-locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%.