DBMS > Cubrid vs. EsgynDB vs. mSQL vs. Spark SQL vs. TerminusDB

System Properties Comparison Cubrid vs. EsgynDB vs. mSQL vs. Spark SQL vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Relational DBMS

Graph DBMS

Secondary database models

Document store
RDF store

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

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

www.esgyn.cn

hughestech.com.au/products/msql

spark.apache.org/sql

terminusdb.com

Technical documentation

cubrid.org/manuals

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

terminusdb.github.io/terminusdb/#

Developer

CUBRID Corporation, CUBRID Foundation

Esgyn

Hughes Technologies

Apache Software Foundation

DataChemist Ltd.

Initial release

2008

2015

1994

2014

2018

Current release

11.0, January 2021

4.4, October 2021

3.5.0 ( 2.13), September 2023

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

Apache 2.0

Open Source

GPL V3

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++, Java

Scala

Prolog, Rust

Server operating systems

Linux
Windows

Linux

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

Linux
OS X
Windows

Linux

Data scheme

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

A subset of ANSI SQL is implemented

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

SQL-like DML and DDL statements

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

JDBC
ODBC

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Delphi
Java
Perl
PHP
Tcl

Java
Python
R
Scala

JavaScript
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes, utilizing Spark Core

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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 journaling

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

Role-based access control

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