DBMS > InfinityDB vs. RDF4J vs. Spark SQL

System Properties Comparison InfinityDB vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A Java embedded Key-Value Store which extends the Java Map interface

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

Key-value store

RDF store

Relational DBMS

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

Score	0.12
Rank	#372	Overall
	#60	Key-value stores

Score	0.97
Rank	#220	Overall
	#9	RDF stores

Score	19.23
Rank	#36	Overall
	#22	Relational DBMS

Website

boilerbay.com

rdf4j.org

spark.apache.org/sql

Technical documentation

boilerbay.com/infinitydb/manual

rdf4j.org/documentation

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

Developer

Boiler Bay Inc.

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

Apache Software Foundation

Initial release

2002

2004

2014

Current release

4.0

3.5.0 ( 2.13), September 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

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Implementation language

Java

Scala

Server operating systems

All OS with a Java VM

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

Java

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

Immediate Consistency

READ-COMMITTED or SERIALIZED

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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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