DBMS > Cubrid vs. GridDB vs. JaguarDB vs. Machbase Neo vs. MonetDB

System Properties Comparison Cubrid vs. GridDB vs. JaguarDB vs. Machbase Neo vs. MonetDB

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Scalable in-memory time series database optimized for IoT and Big Data

Performant, highly scalable DBMS for AI and IoT applications

TimeSeries DBMS for AIoT and BigData

A relational database management system that stores data in columns

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Vector DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Key-value store
Relational DBMS

Document store
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

Technical documentation

cubrid.org/manuals

docs.griddb.net

www.jaguardb.com/support.html

machbase.com/dbms

www.monetdb.org/Documentation

Developer

CUBRID Corporation, CUBRID Foundation

Toshiba Corporation

DataJaguar, Inc.

Machbase

MonetDB BV

Initial release

2008

2013

2015

2013

2004

Current release

11.0, January 2021

5.1, August 2022

3.3 July 2023

V8.0, August 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

GPL V3.0

commercial

free test version available

Open Source

Mozilla Public License 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, C++, Java

C++

the server part. Clients available in other languages

Server operating systems

Linux
Windows

Linux

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

yes

SQL92, SQL-like TQL (Toshiba Query Language)

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

yes

SQL 2003 with some extensions

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

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

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

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

selectable replication factor

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

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

volatile and lookup table

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

rights management via user accounts

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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

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