Comparing Dgraph and Neo4j Graph Databases: Key Differences and Use Cases

In modern data engineering, graph databases have gained prominence for their ability to efficiently store, query, and traverse interconnected data. When selecting the ideal graph database for your project, two names often come up: Dgraph and Neo4j. Each has unique strengths and weaknesses, and in this post, we’ll dive into the features, pros, and cons of both to help you make an informed decision.

Dgraph is a native graph database, making its debut in 2017 with version 1.0. It uses the Dgraph Query Language (DQL), a modification of the GraphQL spec by Facebook, tailored specifically for graph databases. DQL combines GraphQL’s flexibility with advanced graph traversal capabilities, enabling complex filtering, sorting, and pagination.

While Dgraph is a commercial product, it also offers an open-source license without additional restrictions, providing flexibility to organizations considering its adoption.

Dgraph, like other graph databases, excels in managing multi-dimensional data relationships, making it popular in industries like finance, healthcare, social networking, and e-commerce — where complex relationships are essential.

Dgraph’s distributed design includes clusters of server nodes: Dgraph Zero for metadata management and Dgraph Alpha for storing data and indexes. This setup ensures scalability and data security, with built-in distributed backup and recovery to safeguard data availability.

Source

Dgraph’s standout performance is enabled by its custom-built storage engine optimized for graph data. It uses a unique approach known as predicate sharding, allowing parallel execution of complex queries.

Dgraph provides a schema-less design, allowing engineers to make rapid changes to the data model. It supports typed predicates, enabling data type specification for fields, which ensures strong data typing and validation during data ingestion.

Dgraph provides essential tools for schema definition, data loading, and querying:

• Ratel: A user-friendly GUI for executing DQL queries and mutations, viewing and editing schemas, and managing clusters.
• Dgraph Lambdas: JavaScript-based data functions that enhance query results. They serve as database triggers and custom GraphQL resolvers, running within an optional Node.js server included in cloud deployments.

Neo4j is a well-established player in the graph database world. It is an open-source, native graph database providing high-performance graph processing capabilities.

Neo4j supports the Cypher query language, an expressive, SQL-like language optimized for working with graph data. Cypher simplifies the expression of graph patterns and relationships in queries, making it easy for engineers, data analysts, and data scientists to analyze and visualize data without extensive database management knowledge.

Neo4j's architecture is purpose-built for efficient graph database management. It operates within a clustered environment comprising Primary and Secondary server nodes. Primary nodes handle transaction processing and data integrity using the Raft protocol for replication, enhancing fault tolerance. Secondary nodes scale read queries and serve as caches, enabling efficient handling of large-scale graph queries.

This architecture prioritizes high availability and operational simplicity, streamlining tasks like scaling and resource allocation while ensuring robust performance and seamless data views through Neo4j's causal consistency model.

Neo4j uses a property graph model with an index-based storage engine designed to handle high-performance graph traversals.

Neo4j has a rich ecosystem of plugins and extensions that allow you to extend its functionality for specific use cases. These include integrations with popular programming languages and frameworks, as well as third-party tools for data visualization and analysis.

Neo4j equips engineers with a range of tools for defining schemas, loading data, and querying the database:

• Neo4j Desktop: A user-friendly interface for managing Neo4j databases, installing plugins, and developing applications with Neo4j.
• Cypher Query Language: A SQL-like declarative language tailored for querying and modifying graph data within Neo4j.
• Plugins and Libraries: Neo4j offers various plugins and libraries, with one standout example being APOC (Awesome Procedures on Cypher). APOC is a library of user-defined procedures and functions, facilitating tasks like data import, export, and transformation within Neo4j.

To highlight the differences between Dgraph’s DQL and Neo4j’s Cypher, here’s a quick comparison. Imagine you’re working with a social network and want to find a specific user’s friends.

In Dgraph, the schema might look like this:

type User {
    name: string
    age: int
    friends: [User]
}

In Neo4j, it would follow a property graph model:

CREATE (u:User {name: "Alice", age: 30})

Query Example

DQL (Dgraph):

{
  user(func: eq(name, "Alice")) {
    name
    age
    friends {
      name
    }
  }
}

Cypher (Neo4j):

MATCH (u:User {name: "Alice"})-[:FRIENDS_WITH]->(friend:User)
RETURN u.name, u.age, friend.name

Both queries retrieve a user’s details and their friends. DQL’s GraphQL-like syntax emphasizes flexibility, while Cypher’s SQL-like syntax is designed for readability in complex relationship querying. This comparison highlights the learning curve and syntax differences that may influence your decision.

Dgraph and Neo4j both support horizontal scaling, but they approach it differently:

• Dgraph uses predicate sharding to distribute data properties across nodes, enabling parallel processing of large queries. This approach is efficient for applications with high read/write demands.
• Neo4j employs a causal consistency model with the Raft protocol for replication, ensuring that data reads are consistent across nodes. This setup is ideal for environments prioritizing read stability, though it requires additional configuration for distributed deployment.

Dgraph’s predicate sharding is optimal for applications with high data throughput, while Neo4j’s causal consistency is better for applications that need stable, reliable reads.

Both databases offer ACID compliance but differ in transaction management:

• Dgraph handles distributed transactions across nodes automatically, ideal for distributed systems where data consistency is required at scale.
• Neo4j provides explicit control over transactions, with a focus on commit/rollback operations within a single instance. For distributed use, it replicates transactions across nodes for consistency.

While Dgraph’s model offers flexible scaling, network latency may affect performance in large, global deployments. Neo4j’s explicit transaction control is ideal for cases where data integrity and control are essential.

Neo4j offers a comprehensive library of graph algorithms like PageRank, shortest path, and community detection, making it suitable for applications such as recommendation engines and fraud detection. These algorithms enable complex data insights directly from the database without extensive external processing.

Dgraph lacks native graph algorithms but can integrate with third-party libraries like Apache Spark and GraphQL for analytics. This setup is ideal for users focused on high-performance write operations and comfortable relying on external analytics tools.

• Ease of Use: Dgraph isn't the easiest system to use. It takes some effort to learn its query language and data model. Neo4j, on the other hand, simplifies this with its intuitive Cypher language and user-friendly tools.

• Performance: Dgraph excels in handling massive graph datasets with high read/write throughput. Neo4j, with its focus on expressive querying, may shine in smaller-scale use cases where complex graph traversals are more important.

• Scalability: Dgraph offers horizontal scalability out-of-the-box through data distribution across nodes. Neo4j also supports horizontal scalability but requires additional configuration for distributed deployments.

• Algorithms: Neo4j has a vast library of built-in algorithms for graph analysis, making it ideal for analytics-heavy applications. Dgraph may require you to build custom algorithms or rely on third-party libraries.

• ACID Compliance and Transaction Support: Both Dgraph and Neo4j are ACID compliant. Dgraph’s distributed transactions support consistency across multiple nodes, while Neo4j offers fine-grained transaction control with commit/rollback.

• Schema Design: Dgraph’s schema-less design supports quick iterations, while Neo4j’s property graph model supports explicit schema definition with constraints and indexes for data validation.

• Third-Party Integrations and Tooling: Both databases offer extensive client libraries for popular programming languages. Neo4j’s integrations, particularly with APOC, enhance its data transformation capabilities.

• Support: Both Dgraph and Neo4j have active communities. Dgraph’s community has grown recently, while Neo4j’s extensive community resources and support offer a robust foundation for developers.

Here’s a high-level comparison table to quickly summarize key strengths of Dgraph and Neo4j:

Dgraph is ideal for teams seeking a scalable, efficient solution for complex data relationships, especially in resource-conscious environments. Its distributed architecture and scalability make it a strong choice for large-scale applications.

Neo4j, however, offers a mature ecosystem suited to enterprises or projects that benefit from built-in algorithms, analytics, and extensive community support. Neo4j shines for use cases like recommendation engines and analytics-heavy applications.

In conclusion, both Dgraph and Neo4j bring powerful solutions to handling complex data. Choosing between them depends on your project’s unique requirements, as the right choice can greatly impact performance, scalability, and ease of development.

Additional materials

• Neo4j docs
• Dgraph docs
• Graph Databases by Ian Robinson, Jim Webber, Emil Eifrem
• Graph Databases in Action by Dave Bechberger, Josh Perryman
• The Practitioner's Guide to Graph Data by Denise Gosnell, Matthias Broecheler