Dgraph vs Hasura: What are the differences?

Introduction

In this markdown code, the key differences between Dgraph and Hasura will be discussed.

1. Scalability: Dgraph is developed with scalability in mind and offers a distributed architecture that allows for horizontal scaling across multiple nodes. It uses sharding to distribute data across clusters and utilizes a distributed consensus algorithm to ensure data consistency in a fault-tolerant manner. On the other hand, Hasura is a lightweight GraphQL engine that can be run as a single instance or deployed on Kubernetes. While Hasura provides vertical scaling options, it does not offer built-in horizontal scalability features like Dgraph.

2. Data Modeling: Dgraph is a native graph database and offers a schema-based approach for modeling data. It supports complex relationships and has built-in features for handling graph queries efficiently. On the contrary, Hasura is primarily focused on simplifying GraphQL API development and does not provide native support for complex relationships or graph-specific features. It relies on the underlying database's data modeling capabilities.

3. Real-time capabilities: Dgraph provides real-time capabilities through its subscription feature, allowing clients to subscribe to changes in the data and receive updates in real-time. It leverages the Pub/Sub mechanism to notify subscribed clients about changes. In contrast, Hasura also offers real-time capabilities through GraphQL subscriptions, allowing clients to subscribe to specific events and receive real-time updates. However, Hasura does not provide the same level of flexibility and scalability as Dgraph, especially when dealing with complex graph-based data.

4. Geospatial data handling: Dgraph has built-in support for geospatial data, allowing developers to perform spatial queries and operations efficiently. It provides spatial indexing and supports geo-point and geo-shape data types. On the other hand, Hasura does not have native support for handling geospatial data. Developers using Hasura would need to rely on the capabilities of the underlying database for geospatial operations.

5. Authorization and Access Control: Dgraph offers a flexible and fine-grained access control mechanism through its ACL (Access Control Lists) system. It allows developers to define custom access rules and roles for different users or user groups. Dgraph's authorization system is based on predicates and predicates groups, providing granular control over data access. In contrast, Hasura provides a simpler role-based access control (RBAC) mechanism, where access can be granted or denied based on user roles defined within Hasura. While Hasura's RBAC system is sufficient for most use cases, Dgraph's ACL system offers more flexibility and control over data access.

6. Community and Ecosystem: Dgraph has a growing community and a vibrant ecosystem of libraries, tools, and resources. It has gained popularity in the graph database space and has active community support. Dgraph also provides official client libraries for various programming languages, making it easier for developers to integrate with their applications. On the other hand, Hasura also has an active community and offers a range of integrations and extensions. However, compared to Dgraph, Hasura's community and ecosystem are relatively smaller.

In Summary, the key differences between Dgraph and Hasura lie in their scalability capabilities (horizontal scaling in Dgraph vs. limited vertical scaling in Hasura), data modeling approach (graph-based vs. traditional), real-time capabilities (built-in support in Dgraph vs. limited support in Hasura), geospatial data handling (native support in Dgraph vs. reliance on underlying database in Hasura), authorization and access control mechanisms (fine-grained ACL system in Dgraph vs. simpler role-based system in Hasura), and the size and vibrancy of their respective communities and ecosystems.