Dgraph vs RedisGraph: What are the differences?

Introduction

Dgraph and RedisGraph are both graph databases but they have key differences in terms of features and capabilities.

1. Data Modeling: Dgraph is a distributed graph database that uses a native graph model, allowing for complex relationships and nested data structures. It supports directed and undirected edges with properties, and can handle large-scale data and query workloads. On the other hand, RedisGraph is an in-memory graph database that uses the property graph model, which includes nodes, edges, and properties. It offers limited support for nested data structures and is optimized for real-time queries on small to medium-sized datasets.

2. Query Language: Dgraph provides a powerful query language called GraphQL+-, which extends the GraphQL standard with additional graph-specific features. It allows for complex filtering, aggregations, and traversals on distributed graphs. RedisGraph, on the other hand, uses the Cypher query language, which is a standard query language for property graphs. It provides a more declarative and expressive syntax for graph traversal and pattern matching.

3. Scalability: Dgraph is designed to be horizontally scalable and supports sharding and replication, allowing it to handle large amounts of data and high query loads. It can distribute data across multiple nodes and execute queries in a distributed manner. RedisGraph, on the other hand, is a single-server database and lacks built-in support for scaling horizontally. It is optimized for in-memory operations and real-time queries on a single machine.

4. Persistence: Dgraph supports multiple storage options, including disk-based storage for durability and performance. It uses a log-structured merge tree (LSM tree) for efficient write operations and supports persistent data snapshots. RedisGraph, on the other hand, is an in-memory database and does not provide built-in support for disk-based persistence. It relies on Redis for durability and persistence, which can be configured based on Redis settings.

5. Transactions: Dgraph provides support for distributed transactions across multiple nodes in a cluster. It ensures ACID (Atomicity, Consistency, Isolation, Durability) properties for transactions and allows for fine-grained control over data consistency. RedisGraph, on the other hand, does not natively support distributed transactions. It provides limited transactional support within a single server, with optional durability guarantees based on Redis settings.

6. Community and Ecosystem: Dgraph has an active and growing community, with a strong focus on GraphQL and open-source development. It offers various tools and libraries, such as Ratel (a web-based user interface) and GraphQL libraries for different programming languages. RedisGraph, on the other hand, is developed and maintained by Redis Labs, a well-established company in the database space. It has a dedicated user community and integrates well with other Redis modules and tools.

In Summary, Dgraph and RedisGraph differ in their data modeling capabilities, query languages, scalability, persistence options, transaction support, and community ecosystems.