Dgraph vs Grakn

Overview

Dgraph

Stacks124

Followers221

Votes9

GitHub Stars21.3K

Forks1.6K

TypeDB

Stacks11

Followers34

Votes0

Dgraph vs Grakn: What are the differences?

Introduction

Dgraph and Grakn are both powerful graph databases that excel in different areas. While Dgraph is designed for high-performance distributed graph computing, Grakn focuses on providing a knowledge graph system to reason over complex data. Here are the key differences between Dgraph and Grakn:

Query Language: Dgraph uses GraphQL as its query language, which allows developers to retrieve data in a flexible and efficient manner. On the other hand, Grakn uses Graql, a purpose-built knowledge graph query language that supports complex reasoning capabilities.
Graph Model: Dgraph follows a labeled property graph model, where data is represented as nodes and edges with labels and properties. In contrast, Grakn uses a hypergraph model, which allows for richer relationships between entities and attributes.
Scalability: Dgraph is designed for horizontal scalability, using a sharded architecture and distributed processing to handle large datasets and high workloads. Grakn, on the other hand, focuses on providing ACID transactions and reasoning capabilities for smaller datasets.
Reasoning Capabilities: Grakn is specifically designed to support complex reasoning over data, allowing users to define rules and infer new knowledge from existing data. Dgraph, while not explicitly built for reasoning, can still perform basic graph traversal and filtering operations efficiently.
Tooling and Ecosystem: Dgraph provides a comprehensive set of tools and libraries, including a GraphQL API, a web-based interface, and integrations with popular programming languages. Grakn also offers its own set of tools, including a visual query builder and a reasoning engine, but with a narrower focus on knowledge graph use cases.
Community and Adoption: Dgraph has gained significant popularity within the developer community, thanks to its performance, ease of use, and extensive documentation. Grakn, being a relatively newer player, has been adopted by organizations looking for advanced reasoning capabilities and domain-specific knowledge graph solutions.

In summary, Dgraph and Grakn differ in their query languages, graph models, scalability approaches, reasoning capabilities, tooling ecosystems, and community adoption.

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CLI (Node.js)

Manual

Detailed Comparison

Dgraph	TypeDB
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.	TypeDB is a database with a rich and logical type system. TypeDB empowers you to solve complex problems, using TypeQL as its query language.
-	Distributed Analytics; Automated Reasoning; Higher-Level Language
Statistics
GitHub Stars 21.3K	GitHub Stars -
GitHub Forks 1.6K	GitHub Forks -
Stacks 124	Stacks 11
Followers 221	Followers 34
Votes 9	Votes 0
Pros & Cons
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	No community feedback yet

What are some alternatives to Dgraph, TypeDB?

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

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.

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

Dgraph vs Grakn: What are the differences?

Introduction

Query Language: Dgraph uses GraphQL as its query language, which allows developers to retrieve data in a flexible and efficient manner. On the other hand, Grakn uses Graql, a purpose-built knowledge graph query language that supports complex reasoning capabilities.
Graph Model: Dgraph follows a labeled property graph model, where data is represented as nodes and edges with labels and properties. In contrast, Grakn uses a hypergraph model, which allows for richer relationships between entities and attributes.
Scalability: Dgraph is designed for horizontal scalability, using a sharded architecture and distributed processing to handle large datasets and high workloads. Grakn, on the other hand, focuses on providing ACID transactions and reasoning capabilities for smaller datasets.
Reasoning Capabilities: Grakn is specifically designed to support complex reasoning over data, allowing users to define rules and infer new knowledge from existing data. Dgraph, while not explicitly built for reasoning, can still perform basic graph traversal and filtering operations efficiently.
Tooling and Ecosystem: Dgraph provides a comprehensive set of tools and libraries, including a GraphQL API, a web-based interface, and integrations with popular programming languages. Grakn also offers its own set of tools, including a visual query builder and a reasoning engine, but with a narrower focus on knowledge graph use cases.
Community and Adoption: Dgraph has gained significant popularity within the developer community, thanks to its performance, ease of use, and extensive documentation. Grakn, being a relatively newer player, has been adopted by organizations looking for advanced reasoning capabilities and domain-specific knowledge graph solutions.

In summary, Dgraph and Grakn differ in their query languages, graph models, scalability approaches, reasoning capabilities, tooling ecosystems, and community adoption.

Dgraph vs Grakn

Overview

Dgraph vs Grakn: What are the differences?