DBMS > Oracle Berkeley DB vs. RDF4J vs. RethinkDB vs. SiteWhere

System Properties Comparison Oracle Berkeley DB vs. RDF4J vs. RethinkDB vs. SiteWhere

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Editorial information provided by DB-Engines

Name

Description

Widely used in-process key-value store

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

M2M integration platform for persisting/querying time series data

Primary database model

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

RDF store

Document store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	2.58
Rank	#105	Overall
	#20	Document stores

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Website

www.oracle.com/database/technologies/related/berkeleydb.html

rdf4j.org

rethinkdb.com

github.com/sitewhere/sitewhere

Technical documentation

docs.oracle.com/cd/E17076_05/html/index.html

rdf4j.org/documentation

rethinkdb.com/docs

sitewhere1.sitewhere.io/index.html

Developer

Oracle

originally developed by Sleepycat, which was acquired by Oracle

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

The Linux Foundation

since July 2017

SiteWhere

Initial release

1994

2004

2009

2010

Current release

18.1.40, May 2020

2.4.1, August 2020

License

Commercial or Open Source

Open Source

commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2

Open Source

Common Public Attribution License Version 1.0

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, C++ (depending on the Berkeley DB edition)

Java

C++

Java

Server operating systems

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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.

yes

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP REST

Supported programming languages

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Java
PHP
Python

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

yes

Triggers

yes

only for the SQL API

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

range based

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

yes

users and table-level permissions

Users with fine-grained authorization concept

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