DBMS > Drizzle vs. JaguarDB vs. Kinetica vs. Microsoft Azure Table Storage vs. Sequoiadb

System Properties Comparison Drizzle vs. JaguarDB vs. Kinetica vs. Microsoft Azure Table Storage vs. Sequoiadb

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.

Performant, highly scalable DBMS for AI and IoT applications

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

Key-value store
Vector DBMS

Relational DBMS

Wide column store

Document store
Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Website

www.jaguardb.com

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

www.sequoiadb.com

Technical documentation

www.jaguardb.com/support.html

docs.kinetica.com

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Drizzle project, originally started by Brian Aker

DataJaguar, Inc.

Kinetica

Microsoft

Sequoiadb Ltd.

Initial release

2008

2015

2012

2013

Current release

7.2.4, September 2012

3.3 July 2023

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

GPL V3.0

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

the server part. Clients available in other languages

C, C++

C++

Server operating systems

FreeBSD
Linux
OS X

Linux

hosted

Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

yes

with proprietary extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

C
C++
Java
PHP

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

C++
Java
JavaScript (Node.js)
Python

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

JavaScript

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

rights management via user accounts

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

simple password-based access control

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

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1 July 2013, Matthias Gelbmann

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