DBMS > Faircom DB vs. RDF4J vs. Splice Machine

System Properties Comparison Faircom DB vs. RDF4J vs. Splice Machine

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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.

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Key-value store
Relational DBMS

RDF store

Relational DBMS

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

Score	0.18
Rank	#351	Overall
	#53	Key-value stores
	#148	Relational DBMS

Score	1.15
Rank	#203	Overall
	#8	RDF stores

Score	0.88
Rank	#226	Overall
	#106	Relational DBMS

Website

www.faircom.com/products/faircom-db

rdf4j.org

splicemachine.com

Technical documentation

learn.faircomcorp.com/developers/documentation_directory

rdf4j.org/documentation

doc.splicemachine.com

Developer

FairCom Corporation

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

Splice Machine

Initial release

1979

2004

2014

Current release

V12, November 2020

3.1, March 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL 3.0, commercial license available

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

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
Solaris
Windows

Data scheme

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

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

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

JDBC
Native Spark Datasource
ODBC

Supported programming languages

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

Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

Achieve Hundreds of Thousands of Transactions Per Second on A Single Database Server...
» more

Competitive advantages

Top Features SQL Key-value store and navigational NoSQL API Indexed binary objects...
» more

Typical application scenarios

Real-time transaction processing solutions leverage FairCom DB because its NoSQL...
» more

Key customers

Microsoft, UPS, Verizon, Motorola, Commvault, Thomson Reuters, ACI, BNY Mellon, Rockwell...
» more

Market metrics

From banking and financial transactions, transportation, warehouse automation, package...
» more

Licensing and pricing models

Just as the technical aspects of FairCom DB are flexible to meet operation requirements,...
» more

News

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11 June 2021

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1 June 2021

The Compliance Nightmare Lurking in Your Data Science Team
6 May 2021

Whose Job Is It Anyway?
23 April 2021

How Optimizing MLOps Can Revolutionize Enterprise AI
15 April 2021

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

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