DBMS > Altibase vs. Datomic vs. RDF4J

System Properties Comparison Altibase vs. Datomic vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

RDF store

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

Score	0.94
Rank	#181	Overall
	#88	Relational DBMS

Score	1.68
Rank	#135	Overall
	#67	Relational DBMS

Score	0.75
Rank	#205	Overall
	#9	RDF stores

Website

altibase.com

www.datomic.com

rdf4j.org

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

docs.datomic.com

rdf4j.org/documentation

Developer

Altibase

Cognitect

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

Initial release

1999

2012

2004

Current release

v7.1.0.5.3, March 2021

1.0.6362, January 2022

License

Commercial or Open Source

Open Source

since February 2018

commercial

limited edition free

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java, Clojure

Java

Server operating systems

AIX
HP-UX
Linux

All OS with a Java VM

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

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

ANSI SQL-92

APIs and other access methods

JDBC
ODBC
Proprietary protocol

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C++
Java

Clojure
Java

Java
PHP
Python

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

Transaction Functions

yes

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

none

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

fine grained access rights according to SQL-standard

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More resources

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