Dgraph and SQL are compared as database management systems. Dgraph stands out for its graph-like structure, allowing for efficient data retrieval, while SQL is traditionally used but can struggle with complex and interlinked data. Dgraph’s data model, based on RDF, simplifies data management by treating everything as a node or an edge. It boasts of horizontal scalability, enabling it to handle vast amounts of data with ease.
Dgraph’s query language, GraphQL+-, is inspired by Facebook’s GraphQL and offers powerful features like cascade directive, upsert block, and lambda resolvers. It’s flexible, allowing for complex queries without multiple round trips to the server. On the other hand, SQL requires JOIN operations for related data, which can be cumbersome and inefficient.
Dgraph also offers ACID transactions, ensuring data integrity even in the event of a system failure. It supports distributed transactions, which SQL does not. Moreover, Dgraph allows for easy data distribution and replication across multiple servers, ensuring high availability and fault tolerance.
In terms of performance, Dgraph outperforms SQL in handling complex queries and large datasets. It uses Badger, a high-performance key-value store, which is faster than RocksDB used by many SQL databases. Dgraph’s design makes it a more efficient, versatile, and robust choice for modern applications dealing with complex, interlinked data.
Go to source article: https://open.dgraph.io/post/sql-vs-dgraph/