Dgraph: Exploring a JSON Graph Database

This is the first analysis for The Cagle Report, a look at the Dgraph JSON Graph database with GraphQL support. Listen to this on Spotify.

Disclaimer: This is a paid analysis of a software product, aiming to remain unbiased and objective. It discusses Dgraph's strengths and areas where other platforms may outperform it.

The tl;dr

• Seamless GraphQL Integration: Dgraph's ease of use and support for GraphQL enable efficient web-centric applications and semantic publishing systems.
• High Performance: Built with Go and modern indexing, Dgraph competes effectively in the broader database space, ensuring fast query responses.
• Powerful Knowledge Graphs: Dgraph's hybrid nature combines RDF and labeled property graphs, making it ideal for AI-aware large language model editors and data analytics.
• Extensibility and Lambdas: With DQL and Lambdas, Dagraph allows custom extensions and overrides for data analytics, comparisons, and more.
• Cloud Native and Multitenancy: Dgraph's cloud-friendly design enables easy deployment across multiple graphs with secure multitenancy features.

Understanding Knowledge Graphs

Knowledge graphs, a type of highly connected network graph, store semantic information and are distinct from other data structures due to their unique features:

• Addressable: Concepts are marked with globally unique identifiers, embracing an open-world approach.
• Schematically Accessible: Schema and structure are easily accessible from instance data.
• Hypergraph: Predicate objects can have multiple values.
• Reifiable: Supports annotations about assertions.

Dgraph is a commercial native graph database designed for scalability, distributed computing, and low-latency ACID transactions. It combines the strengths of Labeled Property Graphs and RDF-based Knowledge Graphs, using GraphQL+- as its query language.

The Power of GraphQL in Dgraph

GraphQL, introduced by Facebook in 2015, enables easy manipulation of JSON data stores like graphs. Dgraph's version of GraphQL ensures transactional support for both queries and updates. It also allows developers to shape query responses, reducing client-side processing.

Dgraph Strengths:

• Ease of Use: Designed to make GraphQL usage easy and seamless.
• Cloud Native: Easily deployable in the cloud, allowing shared content across multiple graphs on multiple data systems.
• Performant: Powered by Go and modern internal indexing, offering competitive performance in the broader database space.
• RDF Oriented: While using JSON as the native language, Dgraph can read and create RDF nt data.
• Facets: Supports facets corresponding to rdf-star expressions for annotations and version management.
• DQL: Provides a declarative query language for graph traversal and generative output, complementing GraphQL.
• LLM/AI Aware: Developing an API for integrating with AI-based systems like ChatGPT.
• Extensions: Allows writing extensions (in JavaScript) for data analytics, reporting, comparisons, transitive closures, and document searches.
• Lambdas: Utilizes specialized functions to override GraphQL queries or mutations, enhancing flexibility.
• Multitenancy: Supports multiple graphs accessible only through namespaces, enabling multitenancy.
• Security: Allows defining access control levels based on permissions, ensuring proper data visibility and updates.
• Visualization: Employs Ratel as a GUI for record management and graph visualization to show entity relationships.
• Data Analytics: Utilizes the hybrid nature to leverage RDF capabilities and labeled property graphs for data science analytics.
• Innovative Graph Database: Redefines graph database norms and expectations.

Dgraph Weaknesses:

• W3C RDF Stack: Some limitations with RDF-related features like Sparql, OWL, and SHACL, but can be mitigated in JavaScript.
• No JSON-LD: Lacks direct support for JSON-LD, but schema can be adapted to use GraphQL templates.
• Marginal Inferencing: Inferencing is possible but more complex than RDF systems; basic inferencing can be done via lambdas.

Additional Information:

• Dgraph is under an open-source (Apache 2.0) license, with enterprise features available in a separate commercial offering.
• It runs on Linux Ubuntu, deployable under Kubernetes, Docker, or Amazon virtual machines, providing flexible configurations.
• Dgraph offers an extensive cloud offering, enabling quick deployment with minimal configuration.

Dgraph: Empowering Companies for Diverse Use Cases

Dgraph is a versatile knowledge graph database that excels in web-centric applications due to its efficient handling of JSON data. Here are some key use cases where Dgraph stands out:

• Semantic Publishing Systems. Dgraph simplifies creating publishing systems that manage taxonomies, user roles, workflows, and semantic interconnectivity. It offers functionality comparable to WordPress, Drupal, and others with reduced complexity.
• Product Documentation Systems. Dgraph is ideal for tying together various content types like text, images, videos, and PDFs in documentation systems and help interfaces.
• Large Language Model Editors. For systems like ChatGPT, Dgraph complements the AI model by curating content, ensuring governance, and performing master data management.
• Scene Graphs. In gaming, simulations, and digital twins, Dgraph's JSON encapsulation with RDF structures is superior for maintaining metadata about resources in a 3D space.
• 360° Systems. Dgraph powers applications like Customer 360°, Product 360°, Patient 360°, Supply Chain 360°, Equipment 360°, and more, offering diverse views of complex domains without specialized development.
• Graph Analytics / Route Analysis. Dgraph's unique ability to hold facets for edges enables efficient graph analytics and route optimization, surpassing many RDF graph systems.
• Data Catalogs. The hybrid nature of Dgraph, along with its support for running CLIs or Lambdas, makes it well-suited for data catalogs and API management.
• Taxonomy Management. Dgraph's GraphQL-based system excels in handling, querying, and retrieving taxonomies, especially for recursive structures, outperforming traditional RDF-based systems.
• AI Integration. With the rise of AI systems, Dgraph's JSON querying and transformation capabilities make it a natural choice for integrating various AI models.

Dgraph's power extends beyond these use cases, allowing data meshes of interconnected specialized knowledge graphs for extensive enterprise applications. As businesses embrace AI and deal with large-scale data, Dgraph emerges as a robust solution for seamless integration and advanced data management.

Dgraph Is A Powerful JSON Graph Engine

In summary, Dgraph emerges as a formidable player in knowledge graphs. Offering a marriage of GraphQL’s adaptability to JSON data stores and RDF’s semantic integrity, it promises a knowledge graph system that is user-friendly, cloud-native, performant, and RDF-oriented.

While it does have a few limitations, such as its compatibility with the W3C RDF Stack and lack of out-of-the-box network visualization, these can be mitigated with the right tools and knowledge. Furthermore, Dgraph’s open-source nature and enterprise offering allow for a broad range of applications, from semantic publishing systems to large language model editors and data catalogs.

The potential of Dgraph lies in its novel integration of different technologies, offering a flexible, efficient, and versatile tool for handling complex data structures. As more developers, businesses, and data scientists look for ways to manage and use knowledge graphs effectively, Dgraph is a promising solution. Its ability to accommodate the varied demands of contemporary web-centric applications and AI models gives it a unique edge in the knowledge graph market.

Overall, Dgraph has proven to be an exceptional product that redefines the norms and expectations of what a graph database can achieve. Dgraph sets a new standard for the future of knowledge graph technologies by addressing the shortcomings of traditional JSON and RDF-based systems.

Kurt Cagle is the editor of The Cagle Report. He lives in Bellevue, Washington, with his wife, kids, and weird furry wingless sociopathic dragons (meow).