Survey: Graph Databases

TL;DR

This survey reviews the foundational models, query languages, storage architectures, and recent technological advancements in graph databases, highlighting their applications, challenges, and future directions in managing complex interconnected data.

Contribution

It provides a comprehensive overview of graph database models, architectures, and recent innovations, serving as a valuable resource for understanding current trends and challenges.

Findings

Analyzes property models, query languages, and storage architectures.

Evaluates recent advancements in graph database technologies.

Identifies key challenges like data sparsity and scalability issues.

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.