DSE Graph vs RedisGraph

Overview

DSE Graph

Stacks4

Followers8

Votes0

GitHub Stars0

Forks0

RedisGraph

Stacks31

Followers107

Votes7

DSE Graph vs RedisGraph: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between DSE Graph and RedisGraph, two popular graph databases.

Data Model: DSE Graph uses Property Graph data model, which consists of vertices and edges, where vertices represent entities and edges represent relationships between entities. On the other hand, RedisGraph uses Labelled Property Graph data model, which extends Property Graph with labeled properties for vertices and edges.
Scalability and Performance: DSE Graph offers high scalability and performance with its distributed architecture and the ability to store and process large volumes of graph data across multiple machines. RedisGraph also offers good performance but is more suitable for smaller datasets compared to DSE Graph.
Query Language: DSE Graph uses Gremlin, a powerful and expressive graph traversal language, for querying and traversing the graph data. Gremlin provides a wide range of graph traversal operators and functions. In contrast, RedisGraph uses its own query language called RedisGraph Query Language (RGQL), which is similar to SQL and provides a subset of graph-specific operators.
Integration with Ecosystem: DSE Graph is part of the DataStax Enterprise (DSE) ecosystem and integrates seamlessly with other DSE components like Apache Cassandra and DSE Analytics. This allows users to leverage the full power of DSE for data management, analytics, and deployment flexibility. Conversely, RedisGraph is a standalone database and does not have the same level of integration with other data management or analytical tools.
Data Consistency: DSE Graph ensures strong data consistency by supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions, which guarantee that the graph data is always in a valid state. Conversely, RedisGraph guarantees eventual consistency, which means that the data may be temporarily inconsistent but will eventually converge to a consistent state.
Community and Support: DSE Graph is backed by DataStax, a well-established company in the database industry, with a strong community and professional support options available. RedisGraph, while gaining popularity, has a relatively smaller community and less extensive support options compared to DSE Graph.

In summary, DSE Graph and RedisGraph differ in their data models, scalability, query languages, integration with ecosystems, data consistency guarantees, and community and support offerings.

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CLI (Node.js)

Manual

Detailed Comparison

DSE Graph	RedisGraph
It is a distributed graph database that is optimized for enterprise applications–Zero downtime, fast traversals at scale, and analysis of complex, related datasets in real time.	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
Graph Powered Insights; Graph Your Way; Graph Available Always	-
Statistics
GitHub Stars 0	GitHub Stars -
GitHub Forks 0	GitHub Forks -
Stacks 4	Stacks 31
Followers 8	Followers 107
Votes 0	Votes 7
Pros & Cons
No community feedback yet	Pros 3 10x – 600x faster than any other graph database 2 Cypher – graph query language 1 Open source 1 Great graphdb
Integrations
Python .NET JavaScript Java Groovy	Redis

What are some alternatives to DSE Graph, 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.

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.

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DSE Graph vs RedisGraph: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between DSE Graph and RedisGraph, two popular graph databases.

Data Model: DSE Graph uses Property Graph data model, which consists of vertices and edges, where vertices represent entities and edges represent relationships between entities. On the other hand, RedisGraph uses Labelled Property Graph data model, which extends Property Graph with labeled properties for vertices and edges.
Scalability and Performance: DSE Graph offers high scalability and performance with its distributed architecture and the ability to store and process large volumes of graph data across multiple machines. RedisGraph also offers good performance but is more suitable for smaller datasets compared to DSE Graph.
Query Language: DSE Graph uses Gremlin, a powerful and expressive graph traversal language, for querying and traversing the graph data. Gremlin provides a wide range of graph traversal operators and functions. In contrast, RedisGraph uses its own query language called RedisGraph Query Language (RGQL), which is similar to SQL and provides a subset of graph-specific operators.
Integration with Ecosystem: DSE Graph is part of the DataStax Enterprise (DSE) ecosystem and integrates seamlessly with other DSE components like Apache Cassandra and DSE Analytics. This allows users to leverage the full power of DSE for data management, analytics, and deployment flexibility. Conversely, RedisGraph is a standalone database and does not have the same level of integration with other data management or analytical tools.
Data Consistency: DSE Graph ensures strong data consistency by supporting ACID (Atomicity, Consistency, Isolation, Durability) transactions, which guarantee that the graph data is always in a valid state. Conversely, RedisGraph guarantees eventual consistency, which means that the data may be temporarily inconsistent but will eventually converge to a consistent state.
Community and Support: DSE Graph is backed by DataStax, a well-established company in the database industry, with a strong community and professional support options available. RedisGraph, while gaining popularity, has a relatively smaller community and less extensive support options compared to DSE Graph.

In summary, DSE Graph and RedisGraph differ in their data models, scalability, query languages, integration with ecosystems, data consistency guarantees, and community and support offerings.