DBMS > Cubrid vs. Geode vs. Kinetica vs. LevelDB vs. MySQL

System Properties Comparison Cubrid vs. Geode vs. Kinetica vs. LevelDB vs. MySQL

Editorial information provided by DB-Engines

Name

Description

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Fully vectorized database across both GPUs and CPUs

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.92
Rank	#131	Overall
	#23	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

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

geode.apache.org

www.kinetica.com

github.com/google/leveldb

www.mysql.com

Technical documentation

cubrid.org/manuals

geode.apache.org/docs

docs.kinetica.com

github.com/google/leveldb/blob/main/doc/index.md

dev.mysql.com/doc

Developer

CUBRID Corporation, CUBRID Foundation

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Kinetica

Google

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2008

2002

2012

2011

1995

Current release

11.0, January 2021

1.1, February 2017

7.1, August 2021

1.23, February 2021

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available as Gemfire

commercial

Open Source

BSD

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

C, C++, Java

Java

C, C++

C++

C and C++

Server operating systems

Linux
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

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 query language (OQL)

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

Java Client API
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

.Net
All JVM based languages
C++
Groovy
Java
Scala

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

yes

proprietary syntax

Triggers

yes

Cache Event Listeners

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes, on a single node

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights per client and object definable

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

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