DBMS > IBM Db2 vs. RDF4J vs. Spark SQL vs. STSdb

System Properties Comparison IBM Db2 vs. RDF4J vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

Common in IBM host environments, 2 different versions for host and Windows/Linux

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

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

RDF store

Relational DBMS

Key-value store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	127.49
Rank	#8	Overall
	#5	Relational DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.06
Rank	#365	Overall
	#55	Key-value stores

Website

www.ibm.com/products/db2

rdf4j.org

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

www.ibm.com/docs/en/db2

rdf4j.org/documentation

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

Developer

IBM

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

Apache Software Foundation

STS Soft SC

Initial release

1983

host version

2004

2014

2011

Current release

12.1, October 2016

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java

Scala

Server operating systems

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

.NET Client API

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Java
PHP
Python

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

with separate tools (MQ, InfoSphere)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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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