Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Dgraph

Stacks124

Followers221

Votes9

GitHub Stars21.3K

Forks1.6K

Dgraph vs Neo4j: What are the differences?

Key Differences between Dgraph and Neo4j

Dgraph and Neo4j are two popular graph databases with some key differences.

1. Data Sharding: Dgraph automatically shards data across multiple servers, allowing for efficient horizontal scaling. On the other hand, Neo4j does not have built-in support for data sharding, requiring manual distribution of data across different instances.

2. Query Language: Dgraph uses GraphQL+- as its query language, which enables developers to write expressive and efficient queries with complex filtering and aggregations. Neo4j, on the other hand, uses the Cypher query language, which is specifically designed for querying graph databases.

3. Architecture: Dgraph follows a distributed architecture, where data is distributed across multiple servers, providing high availability and fault tolerance. In contrast, Neo4j follows a single-server architecture, where all data is stored on a single instance.

4. ACID Compliance: Dgraph is designed to be eventually consistent, focusing more on horizontal scalability and performance, while sacrificing full ACID (Atomicity, Consistency, Isolation, Durability) compliance. Neo4j, on the other hand, offers strong consistency and full ACID compliance out of the box.

5. JSON-based Data Model: Dgraph natively supports a JSON-based data model, allowing for flexible and dynamic schema-less data storage. Neo4j uses a property graph model, where data is represented as nodes connected by relationships, providing more rigid schema enforcement.

6. Community and Ecosystem: While both Dgraph and Neo4j have active communities, Neo4j has a larger and more established ecosystem. Neo4j has been around for a longer time and has a wider range of community-contributed extensions and integrations.

In summary, Dgraph and Neo4j differ in terms of data sharding, query language, architecture, ACID compliance, data model, and ecosystem. Dgraph focuses on scalability and performance with automatic data sharding and a flexible JSON-based data model, while Neo4j offers strong consistency, ACID compliance, and a more established ecosystem.

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Advice on Neo4j, Dgraph

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

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Detailed Comparison

Neo4j	Dgraph
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.	Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.
intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API	-
Statistics
GitHub Stars 15.3K	GitHub Stars 21.3K
GitHub Forks 2.5K	GitHub Forks 1.6K
Stacks 1.2K	Stacks 124
Followers 1.4K	Followers 221
Votes 351	Votes 9
Pros & Cons
Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost	Pros 3 Graphql as a query language is nice if you like apollo 2 Easy set up 2 Low learning curve 1 Open Source 1 High Performance

What are some alternatives to Neo4j, Dgraph?

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

Cayley

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned.

Blazegraph

It is a fully open-source high-performance graph database supporting the RDF data model and RDR. It operates as an embedded database or over a client/server REST API.

Graph Engine

The distributed RAM store provides a globally addressable high-performance key-value store over a cluster of machines. Through the RAM store, GE enables the fast random data access power over a large distributed data set.

JanusGraph

It is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. It is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

Titan

Titan is a scalable graph database optimized for storing and querying graphs containing hundreds of billions of vertices and edges distributed across a multi-machine cluster. Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.

TypeDB

TypeDB is a database with a rich and logical type system. TypeDB empowers you to solve complex problems, using TypeQL as its query language.

Memgraph

Memgraph is a streaming graph application platform that helps you wrangle your streaming data, build sophisticated models that you can query in real-time, and develop applications you never thought possible in days, not months.

Nebula Graph

It is an open source distributed graph database. It has a shared-nothing architecture and scales quite well due to the separation of storage and computation. It can handle hundreds of billions of vertices and trillions of edges while still maintaining milliseconds of latency. It is openCypher compatible.

Akutan

A distributed knowledge graph store. Knowledge graphs are suitable for modeling data that is highly interconnected by many types of relationships, like encyclopedic information about the world.