DBMS > RDF4J vs. SQL.JS vs. Transwarp StellarDB

System Properties Comparison RDF4J vs. SQL.JS vs. Transwarp StellarDB

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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.

Port of SQLite to JavaScript

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

RDF store

Relational DBMS

Graph DBMS

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

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.53
Rank	#246	Overall
	#115	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Graph DBMS

Website

rdf4j.org

sql.js.org

www.transwarp.cn/en/product/stellardb

Technical documentation

rdf4j.org/documentation

sql.js.org/documentation/index.html

Developer

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

Alon Zakai

enhancements implemented by others

Transwarp

Initial release

2004

2012

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

Server operating systems

Linux
OS X
Unix
Windows

server-less, requires a JavaScript environment (browser, Node.js)

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

yes

SQL-92 is not fully supported

SQL-like query language

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JavaScript API

OpenCypher

Supported programming languages

Java
PHP
Python

JavaScript

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

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

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

except by serializing a db to a file

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

yes

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More resources

DB-Engines blog posts

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