DBMS > GBase vs. MonetDB vs. mSQL vs. Stardog vs. Tkrzw

System Properties Comparison GBase vs. MonetDB vs. mSQL vs. Stardog vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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

A relational database management system that stores data in columns

mSQL (Mini SQL) is a simple and lightweight RDBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Relational DBMS

Graph DBMS
RDF store

Key-value store

Secondary database models

Document store
Spatial DBMS

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

Score	1.05
Rank	#186	Overall
	#86	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

www.gbase.cn

www.monetdb.org

hughestech.com.au/products/msql

www.stardog.com

dbmx.net/tkrzw

Technical documentation

www.monetdb.org/Documentation

docs.stardog.com

Developer

General Data Technology Co., Ltd.

MonetDB BV

Hughes Technologies

Stardog-Union

Mikio Hirabayashi

Initial release

2004

1994

2010

2020

Current release

GBase 8a, GBase 8s, GBase 8c

Dec2023 (11.49), December 2023

4.4, October 2021

7.3.0, May 2020

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

commercial

free licenses can be provided

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache Version 2.0

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++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS
Windows

Linux
macOS

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Standard with numerous extensions

yes

SQL 2003 with some extensions

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

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

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

none

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Access rights for users and roles

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