DBMS > Kinetica vs. RDF4J vs. Sequoiadb vs. Ultipa

System Properties Comparison Kinetica vs. RDF4J vs. Sequoiadb vs. Ultipa

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

NewSQL database with distributed OLTP and SQL

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

RDF store

Document store
Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.42
Rank	#263	Overall
	#41	Document stores
	#121	Relational DBMS

Score	0.11
Rank	#338	Overall
	#32	Graph DBMS

Website

Technical documentation

docs.kinetica.com

rdf4j.org/documentation

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

www.ultipa.com/document

Developer

Kinetica

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Sequoiadb Ltd.

Ultipa

Initial release

2012

2004

2013

2019

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Server: AGPL; Client: Apache V2

commercial

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

C++

Server operating systems

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

yes

RDF Schemas

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

proprietary protocol using JSON

RESTful HTTP API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

.Net
C++
Java
PHP
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

JavaScript

Triggers

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

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

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 or Eventual Consistency depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Access rights for users and roles on table level

simple password-based access control

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More resources

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