DBMS > Drizzle vs. EsgynDB vs. Kinetica vs. MySQL vs. Riak KV

System Properties Comparison Drizzle vs. EsgynDB vs. Kinetica vs. MySQL vs. Riak KV

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Key/Value like access via memcached API

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.esgyn.cn

www.kinetica.com

www.mysql.com

Technical documentation

docs.kinetica.com

dev.mysql.com/doc

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Drizzle project, originally started by Brian Aker

Esgyn

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

OpenSource, formerly Basho Technologies

Initial release

2008

2015

2012

1995

2009

Current release

7.2.4, September 2012

7.1, August 2021

8.4.0, April 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++, Java

C, C++

C and C++

Erlang

Server operating systems

FreeBSD
Linux
OS X

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

yes

schema-free

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

restricted

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API
Native Erlang Interface

Supported programming languages

C
C++
Java
PHP

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
JavaScript (Node.js)
Python

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

yes

proprietary syntax

Erlang

Triggers

hooks for callbacks inside the server can be used.

yes

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

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

Source-replica replication

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

links between data sets can be stored

Transaction concepts

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

ACID

not for MyISAM storage engine

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

yes, using Riak Security

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