DBMS > BigObject vs. IRONdb vs. Oracle Berkeley DB vs. RDF4J

System Properties Comparison BigObject vs. IRONdb vs. Oracle Berkeley DB vs. RDF4J

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Analytic DBMS for real-time computations and queries

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

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.

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

RDF store

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

bigobject.io

www.circonus.com/solutions/time-series-database/

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

rdf4j.org

Technical documentation

docs.bigobject.io

docs.circonus.com/irondb/category/getting-started

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

rdf4j.org/documentation

Developer

BigObject, Inc.

Circonus LLC.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

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

Initial release

2015

2017

1994

2004

Current release

V0.10.20, January 2018

18.1.40, May 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

commercial license available

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 and C++

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

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

SQL-like DML and DDL statements

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.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

Server-side scripts

Stored procedures

Lua

yes, in Lua

yes

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

configurable replication factor, datacenter aware

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

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

User concepts

Access control

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