DBMS > RDF4J vs. Spark SQL vs. STSdb vs. TimescaleDB

System Properties Comparison RDF4J vs. Spark SQL vs. STSdb vs. TimescaleDB

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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.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Primary database model

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

rdf4j.org

spark.apache.org/sql

github.com/STSSoft/STSdb4

www.timescale.com

Technical documentation

rdf4j.org/documentation

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

docs.timescale.com

Developer

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

Apache Software Foundation

STS Soft SC

Timescale

Initial release

2004

2014

2011

2017

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

2.13.0, November 2023

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

Server operating systems

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

.NET Client API

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

Supported programming languages

Java
PHP
Python

Java
Python
R
Scala

C#
Java

.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

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

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

fine grained access rights according to SQL-standard

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