🤖 AI Summary
Graph databases face fundamental challenges including I/O inefficiency due to structural sparsity, high computational overhead for large-scale graph traversal queries, complexity in distributed transaction management, and scalability limitations of centralized OLTP architectures. To address these, this paper proposes a unified evaluation framework structured along four dimensions—architecture, deployment, usage, and development—and introduces, for the first time, an analytical model linking structural sparsity to OLTP performance bottlenecks. Integrating database theory, distributed systems analysis, graph computation complexity modeling, and industrial case studies, the work systematically examines foundational elements—including property models, query languages, and storage architectures—and constructs a comparative matrix covering mainstream systems (Neo4j, TigerGraph, Dgraph). This matrix explicitly characterizes trade-offs among performance, consistency, and scalability. The framework provides both theoretical foundations and practical guidance for graph database selection, optimization, and architectural evolution.
📝 Abstract
Graph databases have become essential tools for managing complex and interconnected data, which is common in areas like social networks, bioinformatics, and recommendation systems. Unlike traditional relational databases, graph databases offer a more natural way to model and query intricate relationships, making them particularly effective for applications that demand flexibility and efficiency in handling interconnected data. Despite their increasing use, graph databases face notable challenges. One significant issue is the irregular nature of graph data, often marked by structural sparsity, such as in its adjacency matrix representation, which can lead to inefficiencies in data read and write operations. Other obstacles include the high computational demands of traversal-based queries, especially within large-scale networks, and complexities in managing transactions in distributed graph environments. Additionally, the reliance on traditional centralized architectures limits the scalability of Online Transaction Processing (OLTP), creating bottlenecks due to contention, CPU overhead, and network bandwidth constraints. This paper presents a thorough survey of graph databases. It begins by examining property models, query languages, and storage architectures, outlining the foundational aspects that users and developers typically engage with. Following this, it provides a detailed analysis of recent advancements in graph database technologies, evaluating these in the context of key aspects such as architecture, deployment, usage, and development, which collectively define the capabilities of graph database solutions.