Improving SQL Join Algorithms for Distributed Systems: A Case Study of Compute Express Link-Based Multihost Shared Memory

The advent of Compute Express Link (CXL) has introduced the possibility of multi-host shared memory architectures. Despite this advancement, there has been limited exploration of shared memory at the application layer. Traditional distributed systems typically partition data across multiple servers, enabling independent processing. However, cross-partition operations, such as joins, require data repartitioning, leading to significant communication overhead. To address this challenge, we propose Merge Hash Join (MHJ), a novel SQL join algorithm that leverages shared memory to eliminate the need for repartitioning. By storing the joining table in shared memory and making it directly accessible to all servers, MHJ significantly reduces communication overhead. To validate our approach, we implemented MHJ and the necessary shared memory functionalities on a CXL-based shared memory prototype. Extensive evaluations using the industry-standard TPC-DS benchmark demonstrate that MHJ achieves up to 1.5× performance improvement compared to conventional join algorithms.

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Publication Details

Journal
IEEE Micro
Published
2025-05-30
DOI
https://doi.org/10.1109/mm.2025.3574357
Primary Topic
Advanced Database Systems and Queries
Type
article
Field-Weighted Citation Impact
0.00
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article

Improving SQL Join Algorithms for Distributed Systems: A Case Study of Compute Express Link-Based Multihost Shared Memory

JaeYung Jun, Jungmin Choi, Byungil Koh, Donguk Moon et al.
IEEE Micro
Advanced Database Systems and Queries
article

Improving SQL Join Algorithms for Distributed Systems: A Case Study of Compute Express Link-Based Multihost Shared Memory

JaeYung Jun, Jungmin Choi, Byungil Koh, Donguk Moon, Joohee Lee, Hyun-Soo Ahn
article en

Abstract

The advent of Compute Express Link (CXL) has introduced the possibility of multi-host shared memory architectures. Despite this advancement, there has been limited exploration of shared memory at the application layer. Traditional distributed systems typically partition data across multiple servers, enabling independent processing. However, cross-partition operations, such as joins, require data repartitioning, leading to significant communication overhead. To address this challenge, we propose Merge Hash Join (MHJ), a novel SQL join algorithm that leverages shared memory to eliminate the need for repartitioning. By storing the joining table in shared memory and making it directly accessible to all servers, MHJ significantly reduces communication overhead. To validate our approach, we implemented MHJ and the necessary shared memory functionalities on a CXL-based shared memory prototype. Extensive evaluations using the industry-standard TPC-DS benchmark demonstrate that MHJ achieves up to 1.5× performance improvement compared to conventional join algorithms.

IEEE MicroVol. 45(6)
SK Group (South Korea) (KR)
Openalex Percentile: Top 88%
Advanced Database Systems and Queries
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