Dgraph vs MongoDB: What are the differences?
- Data Model: Dgraph uses a graph-based data model, allowing for complex relationships between data entities, while MongoDB uses a document-based data model, which is more suited for hierarchical data structures.
- Query Language: Dgraph supports GraphQL as its query language, providing a more intuitive and powerful way to retrieve and manipulate data, whereas MongoDB uses its query language based on JSON-like syntax.
- Indexing: Dgraph provides automatic indexing on all predicates (properties) which results in faster queries, while MongoDB requires explicit declaration and management of indexes which can impact query performance if not done efficiently.
- Scalability: Dgraph is designed from the ground up to be distributed and horizontally scalable, offering better support for handling large volumes of data and high traffic loads compared to MongoDB which may require more manual effort to scale effectively.
- ACID Transactions: MongoDB supports multi-document ACID transactions, ensuring data consistency across multiple operations, while Dgraph lacks full ACID transaction support, though it does offer some level of data consistency through its commitment to eventual consistency.
- Community Support: MongoDB has a larger and more established community, providing extensive resources, documentation, and support for users, whereas Dgraph, being a newer technology, may have a smaller community and fewer resources available.
In Summary, Dgraph and MongoDB differ in their data models, query languages, indexing mechanisms, scalability, transaction support, and community resources.
