The ultimate guide to graph databases

As data becomes increasingly interconnected, graph databases offer a more intuitive and performant way to manage complex data relationships.

Graph databases, like Dgraph, provide significant advantages for modern application development, particularly when dealing with use cases such as long-term memory for agents, search and recommendation, and entity resolution.

Introduction to graph databases

Graph databases are designed to store and query data in a way that prioritizes relationships between entities. Unlike traditional relational databases that organize information in tables with rows and columns, graph databases use a structure of nodes and relationships. Nodes represent entities, while relationships represent how those entities are connected.

This approach mirrors how people naturally think about connections in the real world, making graph databases particularly well-suited for applications where relationships are as important as—or more important than—the data itself.

The evolution of database technology

Relational databases

In the 1970s, Edgar Codd introduced the relational model, describing a method for storing data in tables with fixed-length records. This concept was implemented by researchers at UC Berkeley who created INGRES, proving that relational databases could be both efficient and practical.

By the 1990s, SQL had become the standard language for data manipulation, and relational databases dominated the market.

The NoSQL revolution

As internet usage exploded in the early 2000s, traditional relational databases faced challenges with scaling to handle massive workloads. Google developed BigTable, a distributed storage system that could run across many servers. This innovation sparked the NoSQL movement, which includes several database types:

• Key/Value Stores: Simple, high-performance systems that store pairs of keys and values
• Document Stores: Handle semi-structured data like JSON or XML
• Wide Column Stores: Inspired by BigTable, optimized for horizontal scaling
• Graph Databases: Designed specifically for managing complex relationships

Return to PostgreSQL

As the NoSQL movement matured, organizations began to recognize that while NoSQL solutions excelled at specific use cases, they often lacked the mature features, transactional guarantees, and comprehensive tooling offered by traditional relational databases. This realization led to what many in the industry call "the return to PostgreSQL".

PostgreSQL, or Postgres, had continued to evolve during the NoSQL boom, adding features that addressed many of the limitations that had originally driven developers away. Several key developments contributed to this resurgence:

1. JSON support: PostgreSQL added native JSON and JSONB data types, allowing developers to store and query unstructured data within a relational framework. This brought document-store capabilities to the traditional database.
2. Performance improvements: Significant optimizations made PostgreSQL competitive with NoSQL solutions for many workloads, narrowing the performance gap that had initially driven the NoSQL revolution.
3. Horizontal scaling options: Extensions and third-party solutions like CitusDB and Postgres-XL provided ways to scale PostgreSQL horizontally, addressing one of the main advantages NoSQL had claimed.
4. Reliability and maturity: Many organizations found that PostgreSQL's decades of development resulted in fewer surprises in production environments compared to newer NoSQL technologies.

This "Postgres renaissance" did not eliminate the need for specialized database types but instead highlighted that the future of data storage would be heterogeneous. Organizations began adopting a "right tool for the right job" philosophy, using PostgreSQL as their primary database while employing specialized systems like graph databases for specific use cases where relationship-focused data models offered clear advantages.

Why graph databases?

Graph databases exist to address fundamental limitations in how databases handle relationships.

The relational model's relationship problem

In relational databases, relationships are created in two ways:

1. Direct reference: one table references another through a foreign key
2. Join tables: a separate table is created solely to maintain relationships between entities

These approaches work, but they come with significant drawbacks:

• JOIN operations are computationally expensive
• Complex relationship patterns require multiple JOINs, further reducing performance
• Many-to-many relationships require additional tables, making the schema more complex
• The data model doesn't intuitively represent how people think about connected information

Many developers have found clever workaround for these limitations, either extracting graph data into an in-memory representation that can be traversed or pre-computing those multi-hop relationships.

When the need for connected data is occasional or less important, these workarounds are a worthwhile trade-off to prevent needing to manage multiple data stores.

However, for use cases such as personalization, network analysis, search or agentic memory— the majority of the queries are “relationship oriented,” making the performance and modeling drawbacks more pronounced and the overhead of managing multiple databases worth it.

The graph advantage

Graph databases solve these problems by storing relationships directly with the data. This design offers several benefits:

1. Intuitive data modeling: Relationships are represented naturally, as they exist in the real world
2. Superior query performance: No need for expensive JOIN operations
3. Flexibility: Easy adaptation to changing requirements without major schema modifications
4. Scalability: Many modern graph databases are designed to scale horizontally

As data becomes increasingly interconnected across industries, graph databases provide a more natural and efficient way to manage these complex relationships.

Graph databases for AI and agentic applications

Artificial intelligence and autonomous agents rely heavily on understanding relationships between entities. Graph databases provide several critical advantages in these domains.

Knowledge representation

AI systems need to organize information in ways that mirror human understanding. Graph databases excel at creating knowledge graphs that capture the nuanced relationships between concepts, making it easier for AI to:

• Understand context
• Make connections between seemingly unrelated information
• Infer new knowledge based on existing relationships

Reasoning and inference

Graph databases enable sophisticated reasoning capabilities that are essential for AI systems:

• Path finding: discover connections between entities, especially important when trying to find what data connotes about the broader query
• Multi-modal matching: identify complex relationship patterns as a combination of geospatial, key-word, relationships, and similarity
• Pattern detection: find groups of nodes with similar connection patterns. At Hypermode we call these Graph Embeddings
• Online learning: as relationships evolve in the graph, AI systems can access the updated learning without retraining

Agentic systems

Autonomous agents need to understand their environment and the relationships between different entities to make informed decisions. Graph databases provide:

• A unified view of facts: all relationships are represented in a single, coherent structure
• Efficient traversal: agents can quickly explore possible paths and outcomes
• Dynamic updates: as the world changes, the graph can be updated in real-time

Key use cases for graph databases

Knowledge graphs

A knowledge graph is a system of facts that is used by agentic systems to reduce hallucinations and improve outputs by applying a rigorous world-view to its inferences.

More technically, it's a structured representation of information where:

• Entities (people, places, concepts, etc.) are represented as nodes
• Relationships between entities are represented as edges

Together, these form a network of interconnected data that captures how information relates.

Think of a knowledge graph like a subway map—you only need to know where you are in relation to other stations and the connections between them to navigate effectively. Similarly, AI systems can navigate complex information spaces by understanding the relationships between concepts rather than just the concepts themselves.

Social networks

Social platforms are inherently graph-based. Users (nodes) connect with other users, create content, express interests, and engage with various entities. Graph databases naturally represent these complex social structures.

Facebook recognized this early on and developed TAO, its own graph system, to manage the billions of connections between users and content.

Recommendation engines

Effective recommendation systems need to consider multiple factors: user preferences, product attributes, inventory status, and even the preferences of similar users. Graph databases excel at traversing these complex relationships to identify relevant recommendations in real-time.

Fraud detection

Modern fraud detection requires looking beyond individual transactions to identify suspicious patterns of behavior. Graph databases can reveal complex networks of relationships that may indicate fraudulent activity.

Companies like Feedzai, which processes trillions of dollars in transactions annually, use graph databases to enhance their fraud detection capabilities.

Machine learning

Machine learning models often depend on feature engineering to identify patterns in data. Graph databases can help extract relationship-based features that significantly improve model performance.

For example, in predicting whether someone will adopt a particular behavior, their social connections often provide more predictive power than individual attributes alone.

Dgraph: a modern graph database solution

Dgraph stands out in the graph database landscape with several key advantages:

Performance at scale

Dgraph was designed from the ground up for performance and scalability. It can handle billions of edges while maintaining fast query times.

For example, a large financial services firm uses Dgraph to execute 15,000 queries per second against a dataset of 48 billion relationships.

Distributed architecture

Dgraph was built as a distributed system from day one, allowing it to scale horizontally across multiple machines. This design enables Dgraph to handle massive datasets without compromising on performance.

Consistency and reliability

Dgraph is the first and only graph database to have been Jepsen tested, verifying its consistency guarantees in distributed environments. This rigorous testing ensures that Dgraph maintains data integrity even during network partitions or node failures.

When to consider a graph database

Organizations should consider transitioning to a graph database if:

1. Their data is highly connected: If an application deals with many-to-many relationships or complex networks of connections, a graph database will provide a more natural way to represent and query this data.
2. Query performance is critical: As datasets grow larger and more complex, traditional relational databases struggle with performance. Graph databases excel at traversing relationships quickly, making them ideal for applications that require fast query response times.
3. Flexibility is needed: Graph databases can adapt to changing requirements without major schema modifications, making them well-suited for evolving applications.
4. Agentic systems: graphs provide a unique compatibility for human-agent communication. Language, by itself, is not deterministic. Knowledge graphs provide a rigorous perspective that can be understood by both the agents, but also the humans who curate that system of facts.

Conclusion

As data becomes increasingly interconnected, graph databases offer a more natural and efficient way to manage complex relationships. For AI and agentic applications that depend on understanding these relationships, graph databases such as Dgraph provide significant advantages in terms of performance, scalability, and flexibility.

By adopting a graph-based approach, organizations can unlock new insights from their data, build more intelligent applications, and gain a competitive edge in today's data-driven landscape.