DBMS > Cubrid vs. mSQL vs. Quasardb vs. SQream DB

System Properties Comparison Cubrid vs. mSQL vs. Quasardb vs. SQream DB

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Editorial information provided by DB-Engines

Name

Description

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Distributed, high-performance timeseries database

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

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

hughestech.com.au/products/msql

quasar.ai

sqream.com

Technical documentation

cubrid.org/manuals

doc.quasar.ai/master

docs.sqream.com

Developer

CUBRID Corporation, CUBRID Foundation

Hughes Technologies

quasardb

SQream Technologies

Initial release

2008

1994

2009

2017

Current release

11.0, January 2021

4.4, October 2021

3.14.1, January 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++, Java

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux
Windows

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

BSD
Linux
OS X
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

yes, ANSI Standard SQL Types

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

with tags

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

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

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC

HTTP API

.Net
JDBC
ODBC

Supported programming languages

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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