DBMS > Cubrid vs. Lovefield vs. MariaDB vs. Oracle Berkeley DB vs. Quasardb

System Properties Comparison Cubrid vs. Lovefield vs. MariaDB vs. Oracle Berkeley DB vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

Widely used in-process key-value store

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

Secondary database models

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

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

google.github.io/lovefield

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

www.oracle.com/database/technologies/related/berkeleydb.html

quasar.ai

Technical documentation

cubrid.org/manuals

github.com/google/lovefield/blob/master/docs/spec_index.md

mariadb.com/kb/en/library

docs.oracle.com/cd/E17076_05/html/index.html

doc.quasar.ai/master

Developer

CUBRID Corporation, CUBRID Foundation

Google

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Oracle

originally developed by Sleepycat, which was acquired by Oracle

quasardb

Initial release

2008

2014

2009

Fork of MySQL, which was first released in 1995

1994

2009

Current release

11.0, January 2021

2.1.12, February 2017

11.3.2, February 2024

18.1.40, May 2020

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

GPL version 2, commercial enterprise subscription available

Open Source

commercial license available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

JavaScript

C and C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

Dynamic columns are supported

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

only with the Berkeley DB XML edition

Secondary indexes

yes

with tags

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

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

JavaScript

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

PL/SQL compatibility added with version 10.3

Triggers

yes

Using read-only observers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

several options for horizontal partitioning and Sharding

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

not for in-memory storage engine

yes

by using LevelDB

In-memory capabilities

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

yes

using MemoryDB

yes

with MEMORY storage engine

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

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Market metrics

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Licensing and pricing models

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