Cayley vs Dgraph

Overview

Cayley

Stacks25

Followers73

Votes7

Dgraph

Stacks124

Followers221

Votes9

GitHub Stars21.3K

Forks1.6K

Cayley vs Dgraph: What are the differences?

Developers describe Cayley as "An open-source graph database". 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. On the other hand, Dgraph is detailed as "Fast, Distributed Graph DB". 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 and Dgraph can be primarily classified as "Graph Databases" tools.

Cayley and Dgraph are both open source tools. It seems that Cayley with 12.6K GitHub stars and 1.13K forks on GitHub has more adoption than Dgraph with 9.95K GitHub stars and 695 GitHub forks.

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Advice on Cayley, 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

Comments

Detailed Comparison

Cayley	Dgraph
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.	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.
Written in Go;Easy to get running (3 or 4 commands, below);RESTful API;or a REPL if you prefer;Built-in query editor and visualizer;Multiple query languages:;JavaScript, with a Gremlin-inspired* graph object.;(simplified) MQL, for Freebase fans;Plays well with multiple backend stores:;LevelDB;Bolt;MongoDB for distributed stores;In-memory, ephemeral;Modular design;easy to extend with new languages and backends;Good test coverage;Speed, where possible.	-
Statistics
GitHub Stars -	GitHub Stars 21.3K
GitHub Forks -	GitHub Forks 1.6K
Stacks 25	Stacks 124
Followers 73	Followers 221
Votes 7	Votes 9
Pros & Cons
Pros 7 Full open source	Pros 3 Graphql as a query language is nice if you like apollo 2 Easy set up 2 Low learning curve 1 High Performance 1 Open Source

What are some alternatives to Cayley, Dgraph?

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.

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

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.