Graph databases have become increasingly popular in recent years, especially for companies dealing with large amounts of connected data. But is a graph database the right choice for your application? In this comprehensive article, we’ll examine the pros and cons of graph databases to help you determine if investing in this technology is worth it for your needs.
What is a Graph Database?
A graph database is a type of NoSQL database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data. Instead of tables of rows and columns like a relational database, a graph database uses nodes to store data entities and edges to store relationships between entities. Each node contains properties in the form of key-value pairs. Edges have a start node, end node, type, and properties.
This graph structure provides a more natural way to model connected data than a relational database. Queries can traverse relationships quickly for fast reads, and adding new relationships does not affect existing queries. Graph databases also allow you to analyze interconnections and relationships in the data more efficiently.
Some popular graph database solutions include Neo4j, Amazon Neptune, Microsoft Azure Cosmos DB, TigerGraph, ArangoDB, and OrientDB. Graph databases are especially useful for connected data use cases like social networking, recommendations, fraud detection, master data management, network and IT operations, real-time analytics, and more.
Benefits of Graph Databases
Here are some of the main benefits that graph databases offer over relational databases:
Powerful Data Modeling
Graph databases allow you to logically model relationships between data entities. Instead of needing to infer relationships across multiple tables like in a relational database, a graph structures relationships explicitly through nodes connected with edges. This reduces join complexity for connected queries. Graphs make it easier to see connections in the data visually for fast insights.
Fast Queries for Connected Data
By storing relationships directly with the data, graph databases can traverse relationships very efficiently. Instead of increasing query complexity through multiple joins, graph queries follow relationships directly no matter how deep or interconnected the traversals. This provides order-of-magnitude faster queries for connected data compared to relational databases.
High Scalability
Graph databases are designed to scale out easily across distributed systems rather than scale up on a single server. Their node-centric nature lets you add more nodes without affecting existing queries. This makes graph databases highly performant and scalable for large datasets while maintaining speed. Relational databases often struggle to scale relationships efficiently.
Flexibility for Evolving Data
Graph structures are flexible to changes in your data relationships over time. You can add new nodes, edges, and properties to capture new data interconnections without migrations or affecting existing queries. Compare this to relational tables, where even small schema changes can require changes to queries, indexes, and ETL processes. Graphs naturally accommodate evolving domains with shifting relationships.
Powerful Algorithms for Connected Data
Graph databases include many built-in algorithms optimized for analyzing connected datasets. You can find patterns, communities, and key nodes; recommend friends in a social network; calculate similarity between nodes; identify fraud rings; and more. These algorithms enable you to derive insights from graph data that would be very difficult in a relational database.
Native Graph Storage
Instead of needing to model graphs on top of generic storage, graph databases natively store entities and relationships optimized for graph operations. This gives better performance for graph queries than trying to force a graph structure onto a relational database or other systems not optimized for dense, interrelated data.
Limitations of Graph Databases
While graph databases excel for connected data, they may not be the optimal choice in all cases. Here are some potential limitations to consider:
Steeper Learning Curve
Developers need to learn a new data model, query language, and design principles to leverage graph databases. There is often less documentation and community expertise on graph databases versus relational databases. The skills gap makes it harder to onboard new developers and administer these systems.
Query Complexity
While index lookups are fast, complex graph queries with multiple traversal steps or filters can get expensive. It takes skill to optimize graph patterns into efficient queries. You also lose some of the strict schema checks from SQL for ensuring predictable performance.
Database Administration
Graph databases often provide weaker DBA tools like logging, access control, and management versus mature relational databases. This makes security, access policies, backup/restore, infrastructure automation, and monitoring more challenging.
Analytics Limitations
While good for interconnected queries, graph databases lack the depth of traditional analytics capabilities like Online Analytical Processing (OLAP), BI integration, and aggregates for reporting. You may still need a data warehouse or OLAP database for heavy analysis.
No Standard Query Language
Graph databases don’t use a standard query language like SQL. Vendors often create their own syntax like Cypher. This means less portability when having to switch graph platforms. However, efforts are underway to create standards like GQL from ISO.
Immaturity of Technology
Graph databases are still an emerging technology compared to decades of development on relational databases. As capabilities mature, early adopters deal with gaps in features, best practices, tooling, and stability. The technology may not be ready for mission-critical workloads yet.
When to Use a Graph Database
The main driver for a graph database is when you need to efficiently query and analyze relationships between connected data entities. Consider a graph database when you have:
- A domain with complex, interconnected relationships
- A need to analyze relationship patterns, clustering, or connections frequently
- Low latency requirements for traversing relationships at query time
- A flexible schema with evolving relationships between objects
- Trouble scaling joins or normalization in a relational database
Some example use cases well-suited for graph databases:
- Social networking (find friends of friends)
- Recommendations (people who buy X also buy Y)
- Master data management (connecting users to accounts and policies)
- Network and IT operations (model device connectivity and topology)
- Fraud detection (find rings of related fraudulent transactions)
Graph databases can also supplement existing relational databases by offloading complex relationship queries. This reduces load on the main transactional database. The graph handles relationship traversals, while relational databases handle transactions.
When Not to Use a Graph Database
On the other hand, graph databases may not be the best choice when:
- Your domain has little connectivity between entities
- Your workload consists mostly of simple lookups or writes
- You need strong transactional integrity and database procedures
- You require complex analytics like multi-dimensional aggregations
- Your team lacks graph database development skills
Relational databases work better for transactions, consistency, and analytics on discreet entities. Other specialized databases like time series or search may be superior for non-graph workloads.
Key Graph Database Architectural Considerations
If a graph database looks like a potential fit, assess how it aligns with your architecture:
Data Modeling and Loading
Model your domain as nodes, edges, and properties that capture relationships. Translate data into this graph representation through ETL processes. Avoid overloading graphs with too many node types and relationships. Denormalize data for fast traversals.
Query Performance and Caching
Optimize graph patterns for efficient traversals. Denormalize for speed rather than space. Add indexes to quickly find starting nodes. Cache common query results. Set appropriate database timeouts. Test complex queries at scale.
Graph Algorithms
Determine if built-in algorithms like shortest path, clustering, recommendation engines, etc. meet requirements, or if custom algorithms are needed. Evaluate algorithm performance for large graphs.
Scalability and Availability
Evaluate if the graph database scales on commodity hardware through database sharding, replication, and partitioning. Assess availability configurations like high-availability, disasters recovery, and backups.
Security
Review native access control, encryption, and permission mechanisms. Ensure sensitive data is protected. Determine how to secure graph database connectivity.
Management and Monitoring
Assess management tools for administering and monitoring graph infrastructure. Evaluate integrations with existing logging, metrics, and alerting systems. Plan for graph database backup/restore.
Tooling and Visualization
Verify developers can efficiently build and iterate with integrations to existing toolchains. Check if visualization tools connect to graphs to see data relationships. Ensure browser-based graph visualization for business users.
Additional Technology Stacks
Determine what other databases or technologies are needed besides the graph database, like relational databases, data warehouses, search indexes, machine learning, etc. Integrate the graph into the existing architecture.
Should You Use a Graph Database?
Here is a quick checklist of questions to assess if your use case warrants a graph database investment:
– Do you need to efficiently analyze relationships between highly interconnected data?
– Will your queries traverse many junction table joins in a relational model?
– Do relationships frequently change between entities in your domain?
– Are fast responses for relationship queries a key requirement?
– Is connectivity a core aspect of your data, not just a secondary concern?
– Do you anticipate scaling relationship complexity as the business grows?
– Is graph analysis like clustering, linkage, or recommendation important?
– Are developers open to learning graph data modeling?
If you answered yes to most of these questions, your use case likely warrants exploring a graph database. Be sure to evaluate the specific graph platform against your requirements like any technology. But the graph data model offers fundamental advantages over relational databases for network-style data domains.
Conclusion
Graph databases provide excellent benefits like flexible modeling, fast relationship queries, scalability, and built-in algorithms for connected data. However, they are not a magic bullet, and come with trade-offs like lower transaction support and a steeper learning curve. For workloads centered around relationships between entities that evolve over time, graphs offer a superior paradigm than relational models. But other architectures may be better suited for transactional or analytical needs. Weigh your specific requirements, use case, and development skills to determine if investing in a graph database is worth it.
