DBMS > RDF4J vs. Solr vs. Virtuoso vs. WakandaDB

System Properties Comparison RDF4J vs. Solr vs. Virtuoso vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

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

A widely used distributed, scalable search engine based on Apache Lucene

Virtuoso is a multi-model hybrid-RDBMS that supports management of data represented as relational tables and/or property graphs

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

RDF store

Search engine

Document store
Graph DBMS
Native XML DBMS
Relational DBMS
RDF store
Search engine

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	4.26
Rank	#78	Overall
	#13	Document stores
	#4	Graph DBMS
	#2	Native XML DBMS
	#42	Relational DBMS
	#2	RDF stores
	#9	Search engines

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

rdf4j.org

solr.apache.org

virtuoso.openlinksw.com

wakanda.github.io

Technical documentation

rdf4j.org/documentation

solr.apache.org/resources.html

docs.openlinksw.com/virtuoso

wakanda.github.io/doc

Developer

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

Apache Software Foundation

OpenLink Software

Wakanda SAS

Initial release

2004

2006

1998

2012

Current release

9.5.0, February 2024

7.2.11, September 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

Open Source

GPLv2, extended commercial license available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, JavaScript

Server operating systems

Linux
OS X
Unix
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

AIX
FreeBSD
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

SQL - Standard relational schema
RDF - Quad (S, P, O, G) or Triple (S, P, O)
XML - DTD, XML Schema
DAV - freeform filesystem objects, plus User Defined Types a/k/a Dynamic Extension Type

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

yes

SQL-92, SQL-200x, SQL-3, SQLX

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Java API
RESTful HTTP/JSON API

ADO.NET
GeoSPARQL
HTTP API
JDBC
Jena RDF API
ODBC
OLE DB
RDF4J API
RESTful HTTP API
Sesame REST HTTP Protocol
SOAP webservices
SPARQL 1.1
WebDAV
XPath
XQuery
XSLT

RESTful HTTP API

Supported programming languages

Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

.Net
C
C#
C++
Java
JavaScript
Perl
PHP
Python
Ruby
Visual Basic

JavaScript

Server-side scripts

Stored procedures

yes

Java plugins

yes

Virtuoso PL

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Chain, star, and bi-directional replication
Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate 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

optimistic locking

ACID

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

User concepts

Access control

yes

Fine-grained Attribute-Based Access Control (ABAC) in addition to typical coarse-grained Role-Based Access Control (RBAC) according to SQL-standard. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

yes

More information provided by the system vendor

Specific characteristics

Virtuoso is a modern multi-model RDBMS for managing data represented as tabular relations...
» more

Competitive advantages

Performance & Scale — as exemplified by DBpedia and the LOD Cloud it spawned, i.e.,...
» more

Typical application scenarios

Used for — Analytics/BI Conceptual Data Virtualization Enterprise Knowledge Graphs...
» more

Key customers

Broad use across enterprises and governments including — European Union (EU) US Government...
» more

Market metrics

Largest installed-base of Multi-Model RDBMS for AI-friendly Knowledge Graphs Platform...
» more

Licensing and pricing models

Available in both Commercial Enterprise and Open Source (GPL v2) Editions Feature...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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