DBMS > Kinetica vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. NuoDB vs. RDF4J

System Properties Comparison Kinetica vs. MarkLogic vs. Microsoft Azure Cosmos DB vs. NuoDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

RDF store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.kinetica.com

www.marklogic.com

azure.microsoft.com/services/cosmos-db

www.3ds.com/nuodb-distributed-sql-database

rdf4j.org

Technical documentation

docs.kinetica.com

docs.marklogic.com

learn.microsoft.com/azure/cosmos-db

doc.nuodb.com

rdf4j.org/documentation

Developer

Kinetica

MarkLogic Corp.

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

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

Initial release

2012

2001

2014

2013

2004

Current release

7.1, August 2021

11.0, December 2022

License

Commercial or Open Source

commercial

restricted free version is available

commercial

limited edition free

Open Source

Eclipse Distribution License (EDL), v1.0.

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, C++

C++

Java

Server operating systems

Linux

Linux
OS X
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

Schema can be enforced

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

via XQuery or JavaScript

JavaScript

Java, SQL

yes

Triggers

yes

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

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

data is dynamically stored/cached on the nodes where it is read/written

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

yes

Managed transparently by NuoDB

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

ACID

tunable commit protocol

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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, with Range Indexes

yes

Temporary table

User concepts

Access control

Access rights for users and roles on table level

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

Standard SQL roles/ privileges, Administrative Users

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