ForeSight: A Predictive-Scheduling Deterministic Database

TL;DR

ForeSight introduces a predictive scheduling approach for deterministic databases that uses conflict prediction and dependency analysis to significantly improve throughput and scalability under contention.

Contribution

It presents a novel conflict prediction model, an optimized storage engine, and a dependency analysis algorithm to enhance deterministic database performance.

Findings

Up to 2x higher throughput on skewed workloads

Maintains strong performance under contention

Reduces scheduling overhead significantly

Abstract

Deterministic databases enable scalable replicated systems by executing transactions in a predetermined order. However, existing designs fail to capture transaction dependencies, leading to insufficient scheduling, high abort rates, and poor resource utilization. By addressing these challenges with lightweight conflict prediction and informed scheduling, we present ForeSight, a high-performance deterministic database system. Our system has three core improvements: (1) We design an Association Sum-Product Network to predict potential transaction conflicts, providing the input for dependency analysis without pre-obtained read/write sets. (2) We enhance the storage engine to integrate multi-version-based optimization, improving the execution process and fallback strategy to boost commit rates and concurrency. (3) We propose a matrix two-pass forward scan algorithm that performs dependency analysis to generate conflict-aware schedules, significantly reducing scheduling overhead. Experimental results on multiple benchmarks show that ForeSight achieves up to 2 times higher throughput on skewed workloads and maintains strong performance under contention, demonstrating that predictive scheduling substantially improves deterministic database scalability.