DBMS > Faircom DB vs. RDF4J vs. RethinkDB vs. Solr

System Properties Comparison Faircom DB vs. RDF4J vs. RethinkDB vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

Primary database model

Key-value store
Relational DBMS

RDF store

Document store

Search engine

Secondary database models

Spatial DBMS

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

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	2.84
Rank	#106	Overall
	#19	Document stores

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

www.faircom.com/products/faircom-db

rdf4j.org

rethinkdb.com

solr.apache.org

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

rdf4j.org/documentation

rethinkdb.com/docs

solr.apache.org/resources.html

Developer

FairCom Corporation

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

The Linux Foundation

since July 2017

Apache Software Foundation

Initial release

1979

2004

2009

2006

Current release

V12, November 2020

2.4.1, August 2020

9.5.0, February 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

Open Source

Apache Version 2

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

ANSI C, C++

Java

C++

Java

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Unix
Windows

Linux
OS X
Windows

All OS with a Java VM

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

Data scheme

schema free, schema optional, schema required, partial schema,

yes

RDF Schemas

schema-free

yes

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

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

string, binary, float, bool, date, geometry

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Java
PHP
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java plugins

Triggers

yes

Client-side triggers through changefeeds

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

range based

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Isolation support depends on the API used

Atomic single-document operations

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

yes

users and table-level permissions

yes

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