DBMS > Microsoft SQL Server vs. RDF4J

System Properties Comparison Microsoft SQL Server vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Microsofts relational DBMS

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

Primary database model

Relational DBMS

RDF store

Secondary database models

Document store
Graph DBMS

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Trend Chart

Score	1059.72
Rank	#3	Overall
	#3	Relational DBMS

Score	0.48
Rank	#220	Overall
	#9	RDF stores

Website

www.microsoft.com/en-us/sql-server

rdf4j.org

Technical documentation

docs.microsoft.com/en-ie/sql/sql-server/sql-server-technical-documentation

rdf4j.org/documentation

Developer

Microsoft

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

Initial release

1989

2004

Current release

SQL Server 2019, November 2019

License

Commercial or Open Source

commercial info

restricted free version is available

Open Source info

Eclipse Distribution License (EDL), v1.0.

Cloud-based only info

Only available as a cloud service

DBaaS offerings (sponsored links) info

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

XML support info

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

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RIO info

RDF Input/Output
Sail API
SeRQL info

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
PHP
Python

Server-side scripts info

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods info

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods info

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts info

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys info

Referential integrity

yes

Transaction concepts info

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

ACID

Isolation support depends on the API used

Concurrency info

Support for concurrent manipulation of data

yes

Durability info

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities info

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts info

Access control

fine grained access rights according to SQL-standard

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