DBMS > Cubrid vs. DataFS vs. openGauss vs. Spark SQL

System Properties Comparison Cubrid vs. DataFS vs. openGauss vs. Spark SQL

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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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Object oriented DBMS

Relational DBMS

Secondary database models

Graph DBMS

Document store
Spatial DBMS

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

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	1.06
Rank	#184	Overall
	#84	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

newdatabase.com

gitee.com/opengauss
opengauss.org

spark.apache.org/sql

Technical documentation

cubrid.org/manuals

dev.mobiland.com/Overview.xsp

docs.opengauss.org/en
gitee.com/opengauss/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

CUBRID Corporation, CUBRID Foundation

Mobiland AG

Huawei and openGauss community

Apache Software Foundation

Initial release

2008

2018

2019

2014

Current release

11.0, January 2021

1.1.263, October 2022

3.0, March 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 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

Scala

Server operating systems

Linux
Windows

Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL 2011

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC

Supported programming languages

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

.Net
C
C#
C++
VB.Net

C
C++
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

horizontal partitioning (by range, list and hash)

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Windows-Profile

Access rights for users, groups and roles according to SQL-standard

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