Dgraph vs RedisGraph

Overview

Dgraph

Stacks124

Followers221

Votes9

GitHub Stars21.3K

Forks1.6K

RedisGraph

Stacks31

Followers107

Votes7

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.

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.
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.
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.
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.
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.
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.

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Advice on Dgraph, 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

Dgraph	RedisGraph
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.	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
Statistics
GitHub Stars 21.3K	GitHub Stars -
GitHub Forks 1.6K	GitHub Forks -
Stacks 124	Stacks 31
Followers 221	Followers 107
Votes 9	Votes 7
Pros & Cons
Pros 3 Graphql as a query language is nice if you like apollo 2 Low learning curve 2 Easy set up 1 High Performance 1 Open Source	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 Dgraph, RedisGraph?

Neo4j

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.

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.

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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.

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.
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.
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.
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.
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.
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.