GraphAr: An Efficient Storage Scheme for Graph Data in Data Lakes

TL;DR

GraphAr is a novel storage scheme that significantly enhances data lake capabilities for graph data by enabling efficient graph-specific operations, outperforming traditional methods with substantial speedups.

Contribution

This paper introduces GraphAr, a specialized storage scheme that captures LPG semantics and optimizes graph data operations within data lakes, a novel approach in this context.

Findings

4452x faster neighbor retrieval

14.8x faster label filtering

29.5x improvement in end-to-end workloads

Abstract

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 4452x for neighbor retrieval, 14.8x for label filtering, and 29.5x for end-to-end workloads. These findings highlight GraphAr's potential to extend the utility of data lakes by enabling efficient graph data management.