DBMS > GridDB vs. Kinetica vs. MonetDB vs. OpenQM vs. Teradata Aster

System Properties Comparison GridDB vs. Kinetica vs. MonetDB vs. OpenQM vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Fully vectorized database across both GPUs and CPUs

A relational database management system that stores data in columns

QpenQM is a high-performance, self-tuning, multi-value DBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Secondary database models

Key-value store
Relational DBMS

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	0.28
Rank	#291	Overall
	#10	Multivalue DBMS

Website

griddb.net

www.kinetica.com

www.monetdb.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

Technical documentation

docs.griddb.net

docs.kinetica.com

www.monetdb.org/Documentation

Developer

Toshiba Corporation

Kinetica

MonetDB BV

Rocket Software, originally Martin Phillips

Teradata

Initial release

2013

2012

2004

1993

2005

Current release

5.1, August 2022

7.1, August 2021

Dec2023 (11.49), December 2023

3.4-12

License

Commercial or Open Source

Open Source

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

commercial

Open Source

Mozilla Public License 2.0

Open Source

GPLv2, extended commercial license available

commercial

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, C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

yes

with some exceptions

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

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

.Net
Basic
C
Java
Objective C
PHP
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

yes

R packages

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

Sharding

Sharding via remote tables

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

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

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users can be defined per database

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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