DBMS > Grakn vs. IRONdb vs. RDF4J vs. TerminusDB

System Properties Comparison Grakn vs. IRONdb vs. RDF4J vs. TerminusDB

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Editorial information provided by DB-Engines

Name

Description

Grakn is a distributed, hyper-relational database for managing complex data that serves as a knowledge base for cognitive/AI systems.

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Time Series DBMS

RDF store

Graph DBMS

Secondary database models

Document store
RDF store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.67
Rank	#206	Overall
	#18	Graph DBMS
	#103	Relational DBMS

Score	0.00
Rank	#334	Overall
	#31	Time Series DBMS

Score	0.51
Rank	#230	Overall
	#9	RDF stores

Score	0.13
Rank	#295	Overall
	#27	Graph DBMS

Website

grakn.ai

www.circonus.com/solutions/time-series-database

rdf4j.org

terminusdb.com

Technical documentation

dev.grakn.ai/docs

docs.circonus.com/irondb

rdf4j.org/documentation

terminusdb.com/docs

Developer

Grakn Labs

Circonus LLC.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

DataChemist Ltd.

Initial release

2016

2017

2004

2018

Current release

1.8.4, November 2020

V0.10.20, January 2018

4.0, December 2020

License

Commercial or Open Source

Open Source

GPL Version 3, commercial licenses available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Prolog, Rust

Server operating systems

Linux
OS X
Windows

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like query language (WOQL)

APIs and other access methods

console (shell)
gRPC protocol
Workbase (visualisation software)

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
PHP
Python

JavaScript
Python

Server-side scripts

Stored procedures

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Cassandra

Automatic, metric affinity per node

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

by using Cassandra

configurable replication factor, datacenter aware

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

substituted by the relationship feature

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

in-memory journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

yes

at REST API level; other APIs in progress

Role-based access control

More information provided by the system vendor

Specific characteristics

Grakn is the knowledge graph to organise complex networks of data and making it queryable,...
» more

Competitive advantages

Expressive and flexible data model: The meta-schema of Grakn allows for modelling...
» more

Typical application scenarios

Life sciences : Grakn makes working with biological data much easier and accelerates...
» more

Licensing and pricing models

AGPL and Commercial . The commercial license is priced based on cluster resources...
» more

News

A Clinical Decision Support System Built With a Knowledge Graph
16 February 2021

You’re Invited to Grakn Orbit 2021
12 February 2021

How to Succeed with Your First Grakn Project
12 February 2021

Grakn 2.0 Alpha: best practices of Distributed Systems and Computer Science
13 January 2021

Discover, Build, Inspire with Grakn - We’ll take care of the swag!
18 August 2020

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

Conferences and events

more DBMS events

Grakn Orbit 2021
Online Event, 21-22 April 2021

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15 March 2017, DZone News

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27 November 2018, Forbes

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21 April 2020, PRNewswire

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