DBMS > Kinetica vs. Memcached vs. mSQL vs. MySQL vs. OrientDB

System Properties Comparison Kinetica vs. Memcached vs. mSQL vs. MySQL vs. OrientDB

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

In-memory key-value store, originally intended for caching

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used open source RDBMS

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Document store
Graph DBMS
Key-value store

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	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	3.19
Rank	#93	Overall
	#16	Document stores
	#7	Graph DBMS
	#14	Key-value stores

Website

www.kinetica.com

www.memcached.org

hughestech.com.au/products/msql

www.mysql.com

orientdb.org

Technical documentation

docs.kinetica.com

github.com/memcached/memcached/wiki

dev.mysql.com/doc

www.orientdb.com/docs/last/index.html

Developer

Kinetica

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Hughes Technologies

Oracle

since 2010, originally MySQL AB, then Sun

OrientDB LTD; CallidusCloud; SAP

Initial release

2012

2003

1994

1995

2010

Current release

7.1, August 2021

1.6.25, March 2024

4.4, October 2021

8.4.0, April 2024

3.2.29, March 2024

License

Commercial or Open Source

commercial

Open Source

BSD license

commercial

free licenses can be provided

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C, C++

C and C++

Java

Server operating systems

Linux

FreeBSD
Linux
OS X
Unix
Windows

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

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

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

yes

with proprietary extensions

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

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

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

Java, Javascript

Triggers

yes

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

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Repcached, a Memcached patch, provides this functionallity

none

Multi-source replication
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

relationship in graphs

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

table locks or row locks depending on storage engine

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.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

yes

using SASL (Simple Authentication and Security Layer) protocol

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles; record level security configurable

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