DBMS > DataFS vs. RDF4J vs. TimescaleDB

System Properties Comparison DataFS vs. RDF4J vs. TimescaleDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Object oriented DBMS

RDF store

Time Series DBMS

Secondary database models

Graph DBMS

Relational DBMS

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

Score	0.06
Rank	#390	Overall
	#21	Object oriented DBMS

Score	1.02
Rank	#226	Overall
	#9	RDF stores

Score	5.39
Rank	#79	Overall
	#5	Time Series DBMS

Website

newdatabase.com

rdf4j.org

www.timescale.com

Technical documentation

dev.mobiland.com/Overview.xsp

rdf4j.org/documentation

docs.timescale.com

Developer

Mobiland AG

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

Timescale

Initial release

2018

2004

2017

Current release

1.1.263, October 2022

2.11.0, May 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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

Java

Server operating systems

Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
C
C#
C++
VB.Net

Java
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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.

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

More information provided by the system vendor

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Exploring Intelligent Graph and PathQL - DataScienceCentral.com
21 February 2022, Data Science Central

Ontotext's GraphDB 10 Brings Modern Data Architectures to the ...
5 July 2022, PR Newswire

Intara FAQs
16 November 2022, Jane's

GraphDB 9.8 Brings Text Mining and Kafka Connectivity
2 June 2021, PR Newswire

SPARQL, le SQL du Web, et Linked Data Fragment : le point sur le ...
1 January 2017, LinuxFr.org

provided by Google News

TimescaleDB Is a Vector Database Now, Too
25 September 2023, Datanami

Introduction to Databases in Data Science
8 September 2023, KDnuggets

Visualizing IoT Data at Scale With Hopara and TimescaleDB
16 May 2023, Embedded Computing Design

InfluxDB Clustered targets on-premises time-series database deployments
6 September 2023, InfoWorld

New TDengine Benchmark Results Show Up to 37.0x Higher Query ...
28 February 2023, GlobeNewswire

provided by Google News

Job opportunities

Ontologist
Johns Hopkins Applied Physics Laboratory (APL), Laurel, MD

Rust Engineers (Multiple Openings) - Remote
Keyrock, Remote

Full Stack Developer
Sheesha Finance, Remote

Mid or Senior Rust Backend Developer (Multiple openings - remote possible)
Keyrock, Remote

Senior Full Stack Developer
Sheesha Finance, Remote

Financial Markets Data Specialist
Federal Reserve Bank of New York, New York, NY

jobs by

Share this page