DBMS > Faircom DB vs. RDF4J vs. Solr vs. Spark SQL vs. VoltDB

System Properties Comparison Faircom DB vs. RDF4J vs. Solr vs. Spark SQL vs. VoltDB

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.

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Key-value store
Relational DBMS

RDF store

Search engine

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.faircom.com/products/faircom-db

Technical documentation

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

rdf4j.org/documentation

solr.apache.org/resources.html

spark.apache.org/docs/latest/sql-programming-guide.html

docs.voltdb.com

Developer

FairCom Corporation

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

Apache Software Foundation

VoltDB Inc.

Initial release

1979

2004

2006

2014

2010

Current release

V12, November 2020

9.6.0, April 2024

3.5.0 ( 2.13), September 2023

11.3, April 2022

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 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Scala

Java, C++

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

All OS with a Java VM

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

Linux
OS X
Windows

Linux
OS X

for development

Data scheme

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

yes

RDF Schemas

yes

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

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

Solr Parallel SQL Interface

SQL-like DML and DDL statements

yes

only a subset of SQL 99

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

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

Java
PHP
Python

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

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java plugins

Java

Triggers

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

yes, utilizing Spark Core

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

yes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Eventual Consistency

Foreign keys

Referential integrity

yes

FOREIGN KEY constraints are not supported

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

optimistic locking

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

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

Snapshots and command logging

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 roles with access to stored procedures

More information provided by the system vendor

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