Neo4j vs RedisGraph

Overview

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

RedisGraph

Stacks31

Followers107

Votes7

Neo4j vs RedisGraph: What are the differences?

Neo4j and RedisGraph are both graph databases that are used for storing and querying connected data. Let's explore the key differences between them.

Data Model: Neo4j is a property graph database, which means it uses nodes, relationships, and properties to represent data. RedisGraph, on the other hand, is a graph database implemented as a module for Redis, a key-value store. RedisGraph's data model is based on the Resource Description Framework (RDF), which uses subject-predicate-object triples to represent relationships between entities.
Scalability: Neo4j is designed to scale horizontally, meaning it can distribute data across multiple machines to handle large-scale datasets and high query loads. RedisGraph, being a Redis module, inherits Redis' scalability and can also be scaled horizontally by adding more Redis instances to the cluster.
Indexing: Neo4j provides extensive indexing capabilities, allowing users to create different types of indexes on nodes and relationships to optimize query performance. RedisGraph, on the other hand, currently only supports indexing on node labels, limiting its indexing capabilities compared to Neo4j.
Built-in Features: Neo4j comes with a wide range of built-in features such as a query language (Cypher), full-text search, and advanced security and access control mechanisms. RedisGraph, being a module for Redis, relies on Redis' built-in features and does not provide the same level of functionality out-of-the-box as Neo4j.
Language Support: Neo4j provides official drivers and libraries for various programming languages, including Java, JavaScript, Python, and .NET, making it easy to integrate with different application stacks. RedisGraph, being a module for Redis, can be accessed using Redis' own client libraries and drivers, which are available for numerous programming languages.
Data Persistence: Neo4j stores data persistently on disk, ensuring durability and fault-tolerance. RedisGraph, being a module for Redis, also supports data persistence by leveraging Redis' capabilities to persist data to disk. However, RedisGraph does not provide the same level of durability and fault-tolerance as Neo4j.

In summary, Neo4j is a mature graph database that offers a rich set of features and is well-suited for complex graph queries and analytics. On the other hand, RedisGraph leverages the speed and scalability of Redis to provide a high-performance graph database solution, making it ideal for real-time graph processing and caching scenarios.

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Advice on Neo4j, RedisGraph

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

Comments

Detailed Comparison

Neo4j	RedisGraph
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.	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
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 -
GitHub Forks 2.5K	GitHub Forks -
Stacks 1.2K	Stacks 31
Followers 1.4K	Followers 107
Votes 351	Votes 7
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 10x – 600x faster than any other graph database 2 Cypher – graph query language 1 Open source 1 Great graphdb
Integrations
No integrations available	Redis

What are some alternatives to Neo4j, RedisGraph?

Dgraph

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.

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.

FalkorDB

FalkorDB is developing a novel graph database that revolutionizes the graph databases and AI industries. Our graph database is based on novel but proven linear algebra algorithms on sparse matrices that deliver unprecedented performance up to two orders of magnitude greater than the leading graph databases. Our goal is to provide the missing piece in AI in general and LLM in particular, reducing hallucinations and enhancing accuracy and reliability. We accomplish this by providing a fast and interactive knowledge graph, which provides a superior solution to the common solutions today.

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.

Related Comparisons

Neo4j vs RedisGraph: What are the differences?

Neo4j and RedisGraph are both graph databases that are used for storing and querying connected data. Let's explore the key differences between them.

Data Model: Neo4j is a property graph database, which means it uses nodes, relationships, and properties to represent data. RedisGraph, on the other hand, is a graph database implemented as a module for Redis, a key-value store. RedisGraph's data model is based on the Resource Description Framework (RDF), which uses subject-predicate-object triples to represent relationships between entities.
Scalability: Neo4j is designed to scale horizontally, meaning it can distribute data across multiple machines to handle large-scale datasets and high query loads. RedisGraph, being a Redis module, inherits Redis' scalability and can also be scaled horizontally by adding more Redis instances to the cluster.
Indexing: Neo4j provides extensive indexing capabilities, allowing users to create different types of indexes on nodes and relationships to optimize query performance. RedisGraph, on the other hand, currently only supports indexing on node labels, limiting its indexing capabilities compared to Neo4j.
Built-in Features: Neo4j comes with a wide range of built-in features such as a query language (Cypher), full-text search, and advanced security and access control mechanisms. RedisGraph, being a module for Redis, relies on Redis' built-in features and does not provide the same level of functionality out-of-the-box as Neo4j.
Language Support: Neo4j provides official drivers and libraries for various programming languages, including Java, JavaScript, Python, and .NET, making it easy to integrate with different application stacks. RedisGraph, being a module for Redis, can be accessed using Redis' own client libraries and drivers, which are available for numerous programming languages.
Data Persistence: Neo4j stores data persistently on disk, ensuring durability and fault-tolerance. RedisGraph, being a module for Redis, also supports data persistence by leveraging Redis' capabilities to persist data to disk. However, RedisGraph does not provide the same level of durability and fault-tolerance as Neo4j.

Neo4j vs RedisGraph

Overview