Data lakes, increasingly adopted for their ability to store and analyze diverse types of data, commonly use columnar storage formats like Parquet and ORC for handling relational tables. However, these traditional setups fall short when it comes to efficiently managing graph data, particularly those conforming to the Labeled Property Graph (LPG) model. To address this gap, this paper introduces GraphAr, a specialized storage scheme designed to enhance existing data lakes for efficient graph data management. Leveraging the strengths of Parquet, GraphAr captures LPG semantics precisely and facilitates graph-specific operations such as neighbor retrieval and label filtering. Through innovative data organization, encoding, and decoding techniques, GraphAr dramatically improves performance. Our evaluations reveal that GraphAr outperforms conventional Parquet and Acero-based methods, achieving an average speedup of $3283\times$ for neighbor retrieval, $6.0\times$ for label filtering, and $29.5\times$ for end-to-end workloads. These findings highlight GraphAr's potential to extend the utility of data lakes by enabling efficient graph data management.

翻译：数据湖因其能够存储和分析多种类型数据而日益普及，通常采用Parquet和ORC等列式存储格式来处理关系表。然而，这些传统设置在处理图数据（尤其符合标签属性图（LPG）模型的图数据）时效率低下。为弥补这一不足，本文提出GraphAr，一种专门设计的存储方案，旨在增强现有数据湖以实现高效的图数据管理。GraphAr利用Parquet的优势，精确捕捉LPG语义，并支持邻居检索和标签过滤等图特有操作。通过创新的数据组织、编码和解码技术，GraphAr显著提升了性能。我们的评估显示，GraphAr优于传统的基于Parquet和Acero的方法，在邻居检索、标签过滤和端到端工作负载方面分别实现了平均$3283\times$、$6.0\times$和$29.5\times$的加速比。这些发现凸显了GraphAr通过实现高效图数据管理来扩展数据湖实用性的潜力。