What Are the Key Features of Dgraph?

Dgraph is an open-source, complete graph database built for terabyte-scale, real-time workloads. According to the Dgraph Documentation, it is distributed, horizontally scalable, highly available, and designed for data flexibility, processing queries in parallel to return fast results even on complex workloads. If you need a graph database for real-time use cases that must scale beyond a single machine, Dgraph is positioned for exactly that; if you only need a small embedded graph store, its distributed architecture may be more than you require.

Core characteristics

The documentation describes Dgraph along five main dimensions:

• Open and complete — it is open-source and presented as a complete graph database rather than a partial or add-on solution.
• Terabyte-scale — it is explicitly described as being used at terabyte scale to power real-time use cases.
• Real-time workloads — the system is designed for real-time performance rather than batch-oriented analytics.
• Distributed system — Dgraph is implemented as a distributed system, which is what enables the scalability and availability properties below.
• Data flexibility — the design accommodates flexible data, which fits graph modeling where relationships and shapes vary.

Scalability and availability

Because Dgraph is implemented as a distributed system, two properties follow directly from that architecture:

The parallel query processing is the mechanism behind the speed claim: rather than executing a query in a single thread or on a single node, Dgraph splits the work so that complex workloads still return results quickly.

Two ways to use Dgraph

The documentation frames the product around a choice of path, and this is the most practical feature distinction for anyone evaluating it:

Dgraph DB

Use Dgraph as a property graph database for direct database operations and administration. This path suits you if you want to work with the graph directly — running queries and mutations, and operating the database yourself.

GraphQL API

Use the GraphQL API for rapid application development with schema-driven APIs. The documentation describes this path as generating a GraphQL API and a graph backend from your GraphQL schema. This suits you if you would rather define a schema and let Dgraph produce the API and backing graph for you.

The choice between them is not about capability so much as workflow: direct database control versus schema-driven API generation for faster application development.

How to decide whether it fits

Use the documented features as a checklist against your own requirements:

• Choose Dgraph if your workload is real-time, your data volume reaches terabyte scale, and you need horizontal scalability and high availability from a distributed system.
• Consider the GraphQL API path if you want to move quickly from a GraphQL schema to a working API and backend.
• Consider the Dgraph DB path if you need direct database operations and administration.
• Look elsewhere if your data fits comfortably in a single node and you do not need distribution, since the distributed design is the source of both its strengths and its operational complexity.

For setup instructions, query syntax, and operational details, the documentation itself is the authoritative next step: start from the Dgraph DB or GraphQL API section depending on the path you chose above.