DBMS > Kyligence Enterprise vs. mSQL vs. OpenQM vs. OrigoDB vs. Spark SQL

System Properties Comparison Kyligence Enterprise vs. mSQL vs. OpenQM vs. OrigoDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, built on top of Apache Kylin

mSQL (Mini SQL) is a simple and lightweight RDBMS

QpenQM is a high-performance, self-tuning, multi-value DBMS

A fully ACID in-memory object graph database

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

Primary database model

Relational DBMS

Multivalue DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

kyligence.io/kyligence-enterprise

hughestech.com.au/products/msql

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

origodb.com

spark.apache.org/sql

Technical documentation

origodb.com/docs

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

Developer

Kyligence, Inc.

Hughes Technologies

Rocket Software, originally Martin Phillips

Robert Friberg et al

Apache Software Foundation

Initial release

2016

1994

1993

2009

under the name LiveDB

2014

Current release

4.4, October 2021

3.4-12

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

GPLv2, extended commercial license available

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

Java

Scala

Server operating systems

Linux

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

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

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

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

JDBC
ODBC

Supported programming languages

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
Basic
C
Java
Objective C
PHP
Python

.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

yes

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Write ahead log

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

Access rights can be defined down to the item level

Role based authorization

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