DBMS > GBase vs. HugeGraph vs. IRONdb vs. Kinetica vs. RDF4J

System Properties Comparison GBase vs. HugeGraph vs. IRONdb vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A fast-speed and highly-scalable Graph DBMS

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

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.gbase.cn

github.com/hugegraph
hugegraph.apache.org

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

www.kinetica.com

rdf4j.org

Technical documentation

hugegraph.apache.org/docs

docs.circonus.com/irondb/category/getting-started

docs.kinetica.com

rdf4j.org/documentation

Developer

General Data Technology Co., Ltd.

Baidu

Circonus LLC.

Kinetica

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

Initial release

2004

2018

2017

2012

2004

Current release

GBase 8a, GBase 8s, GBase 8c

0.9

V0.10.20, January 2018

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C, Java, Python

Java

C and C++

C, C++

Java

Server operating systems

Linux

Linux
macOS
Unix

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Standard with numerous extensions

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

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

HTTP API

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Groovy
Java
Python

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

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

asynchronous Gremlin script jobs

yes, in Lua

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

configurable replication factor, datacenter aware

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

yes

Users, roles and permissions

Access rights for users and roles on table level

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