DBMS > InfinityDB vs. RDF4J vs. Tkrzw vs. Yaacomo

System Properties Comparison InfinityDB vs. RDF4J vs. Tkrzw vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

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.

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

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

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

boilerbay.com

rdf4j.org

dbmx.net/tkrzw

yaacomo.com

Technical documentation

boilerbay.com/infinitydb/manual

rdf4j.org/documentation

Developer

Boiler Bay Inc.

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

Mikio Hirabayashi

Q2WEB GmbH

Initial release

2002

2004

2020

2009

Current release

4.0

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2.0

commercial

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

Java

C++

Server operating systems

All OS with a Java VM

Linux
OS X
Unix
Windows

Linux
macOS

Android
Linux
Windows

Data scheme

yes

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

yes

RDF Schemas

schema-free

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

yes

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

C++
Java
Python
Ruby

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

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

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

yes

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

ACID

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.

yes

using specific database classes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

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