DBMS > Cubrid vs. Datastax Enterprise vs. GBase

System Properties Comparison Cubrid vs. Datastax Enterprise vs. GBase

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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

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

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

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

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

Score	0.95
Rank	#186	Overall
	#84	Relational DBMS

Score	3.94
Rank	#67	Overall
	#4	Wide column stores

Score	1.49
Rank	#142	Overall
	#64	Relational DBMS

Website

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

www.datastax.com/products/datastax-enterprise

www.gbase.cn

Technical documentation

cubrid.org/manuals

docs.datastax.com

Developer

CUBRID Corporation, CUBRID Foundation

DataStax

General Data Technology Co., Ltd.

Initial release

2008

2011

2004

Current release

11.0, January 2021

6.8, April 2020

GBase 8a, GBase 8s, GBase 8c

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java

C, Java, Python

Server operating systems

Linux
Windows

Linux
OS X

Linux

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements (CQL); Spark SQL

Standard with numerous extensions

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

ADO.NET
C API
JDBC
ODBC

Supported programming languages

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

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

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

no "single point of failure"

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware, advanced replication for edge computing

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomicity and isolation are supported for single operations

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users can be defined per object

yes

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