DBMS > Cachelot.io vs. RDF4J vs. RocksDB vs. Transbase

System Properties Comparison Cachelot.io vs. RDF4J vs. RocksDB vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

In-memory caching system

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	0.04
Rank	#388	Overall
	#62	Key-value stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

cachelot.io

rdf4j.org

rocksdb.org

www.transaction.de/en/products/transbase.html

Technical documentation

rdf4j.org/documentation

github.com/facebook/rocksdb/wiki

www.transaction.de/en/products/transbase/features.html

Developer

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

Facebook, Inc.

Transaction Software GmbH

Initial release

2015

2004

2013

1987

Current release

9.2.1, May 2024

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Simplified BSD License

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

BSD

commercial

free development license

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

C++

C and C++

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Unix
Windows

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

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

APIs and other access methods

Memcached protocol

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

C++ API
Java API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

Java
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

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

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

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

User concepts

Access control

fine grained access rights according to SQL-standard

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