DBMS > mSQL vs. RDF4J vs. Spark SQL

System Properties Comparison mSQL vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

Primary database model

Relational DBMS

RDF store

Relational DBMS

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

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

hughestech.com.au/products/msql

rdf4j.org

spark.apache.org/sql

Technical documentation

rdf4j.org/documentation

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

Developer

Hughes Technologies

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

Apache Software Foundation

Initial release

1994

2004

2014

Current release

4.4, October 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free licenses can be provided

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

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Implementation language

Java

Scala

Server operating systems

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

C
C++
Delphi
Java
Perl
PHP
Tcl

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

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

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