DBMS > Alibaba Cloud Table Store vs. Cubrid vs. EsgynDB vs. mSQL vs. Spark SQL

System Properties Comparison Alibaba Cloud Table Store vs. Cubrid vs. EsgynDB vs. mSQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

Primary database model

Wide column store

Relational DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

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

Website

www.alibabacloud.com/product/table-store

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

www.esgyn.cn

hughestech.com.au/products/msql

spark.apache.org/sql

Technical documentation

www.alibabacloud.com/help/en/tablestore

cubrid.org/manuals

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

Developer

Alibaba

CUBRID Corporation, CUBRID Foundation

Esgyn

Hughes Technologies

Apache Software Foundation

Initial release

2016

2008

2015

1994

2014

Current release

11.0, January 2021

4.4, October 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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

Server operating systems

hosted

Linux
Windows

Linux

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

Linux
OS X
Windows

Data scheme

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.

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

APIs and other access methods

HTTP API

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java
Python

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

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication

Multi-source replication between multi datacenters

none

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

Atomic single-row 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.

User concepts

Access control

Access rights based on subaccounts and tokens

fine grained access rights according to SQL-standard

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