Graph Databases vs. SQL with Graph Queries: Friends or Foes?

SQL (Structured Query Language) has been the standard language for managing and querying relational databases for decades. Relational databases store data in tables and use structured schemas to define relationships between these tables. However, with the increasing complexity of data relationships in modern applications, there has been a growing need to model and query data in ways that go beyond traditional tabular structures.

Graph databases, such as Neo4j, Amazon Neptune, and NebulaGraph, have emerged to address these needs by storing data as nodes (entities) and edges (relationships). They are optimized for traversing and querying complex relationships, making them ideal for applications like social networks, recommendation engines, fraud detection, and network analysis.

In response to the rising popularity of graph databases and the demand for more flexible data modeling, several major SQL database systems have introduced support for graph queries. This includes the ability to create, manage, and query graph structures directly within a SQL database, leveraging the familiar syntax and capabilities of SQL.

This introduction does bring benefits to organizations looking for unified data management as both relational and graph data can be managed within the same database system, simplifying their data architecture and reducing the need for multiple specialized databases. However, it does not necessarily mean that graph databases will no longer be needed or that they will be replaced. Here are several key points to consider regarding the impact of this development on graph databases:

Data Model Complexity and Flexibility

The graph database is a natural fit for graph data. If the data inherently represents complex relationships and networks (e.g., social networks, knowledge graphs), a graph database provides a more natural and intuitive way to model and query the data. What’s more, graph databases offer more flexibility in evolving the data model. In relational databases, schema changes can be complex and disruptive, whereas graph databases allow for more dynamic and flexible schema evolution.

Query Language and Usability

Graph databases often provide specialized query languages (e.g., Cypher for Neo4j, Gremlin for TinkerPop) that are designed specifically for graph traversal and pattern matching, making it easier to express complex queries. Since the ISO-GQL was officially released in April this year, the graph database industry is expecting this standard graph query language to provide a unified and standard experience for developers familiar with graph concepts, as using a standard graph-specific query language, can be more intuitive and efficient compared to using SQL extensions for graph queries.

Real-Time Analytics and Insights

For applications requiring real-time insights and analytics, such as fraud detection or recommendation engines, graph databases are optimized for low-latency query execution and can handle real-time data updates and queries more effectively. Some graph databases are designed to integrate seamlessly with streaming data sources, enabling real-time graph updates and analytics. Apparently, under situations requiring real-time analytics and insights, even though relational databases can perform graph queries, they may not be ideal options.

Scalability and Distributed Processing

In terms of scalability and distributed processing, relational databases with graph query support might not be competitive to dedicated graph databases. Many graph databases are designed to scale horizontally, distributing data and queries across multiple nodes in a cluster, for instance NebulaGraph. This can be particularly important for large-scale graph applications where the data volume and query complexity require distributed processing. Besides, dedicated graph databases often come with built-in, optimized graph algorithms (e.g., shortest path, centrality, community detection) that can be executed efficiently at scale.

Complementary Use Cases

Graph databases are optimized specifically for graph data and queries, offering advanced features such as efficient graph traversals, pattern matching, and handling of complex relationships. While SQL databases with graph query support can handle basic graph operations, dedicated graph databases are likely to remain superior for use cases that require high performance and scalability in graph processing. Therefore, SQL databases with graph capabilities and dedicated graph databases can coexist, each serving different needs and use cases.

Performance Considerations

While SQL databases with graph query support can handle graph data, they might not match the performance and efficiency of dedicated graph databases, especially for large-scale graph processing and real-time analytics. Graph databases are designed with specific optimizations for graph workloads, such as index-free adjacency, which can significantly improve query performance for certain types of graph operations.

Conclusion

In summary, while the support for graph queries in SQL databases enhances their capabilities and provides more flexibility, it does not render dedicated graph databases obsolete. Each type of database has its strengths and is suited for different use cases. Organizations will continue to choose the best tool for their specific requirements, and dedicated graph databases will remain crucial for applications that demand high-performance graph processing and advanced graph analytics. The coexistence of these technologies provides organizations with a broader range of tools to meet their diverse data management needs.