DBMS > IRONdb vs. RDF4J vs. searchxml vs. Solr vs. WakandaDB

System Properties Comparison IRONdb vs. RDF4J vs. searchxml vs. Solr vs. WakandaDB

Editorial information provided by DB-Engines

Name

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

Description

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

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

DBMS for structured and unstructured content wrapped with an application server

A widely used distributed, scalable search engine based on Apache Lucene

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Time Series DBMS

RDF store

Native XML DBMS
Search engine

Search engine

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

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

rdf4j.org

www.searchxml.net/category/products

solr.apache.org

wakanda.github.io

Technical documentation

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

rdf4j.org/documentation

www.searchxml.net/support/handouts

solr.apache.org/resources.html

wakanda.github.io/doc

Developer

Circonus LLC.

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

informationpartners gmbh

Apache Software Foundation

Wakanda SAS

Initial release

2017

2004

2015

2006

2012

Current release

V0.10.20, January 2018

1.0

9.6.0, April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache Version 2

Open Source

AGPLv3, extended commercial license available

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++

Java

C++

Java

C++, JavaScript

Server operating systems

Linux

Linux
OS X
Unix
Windows

Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux
OS X
Windows

Data scheme

schema-free

yes

RDF Schemas

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

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

Solr Parallel SQL Interface

APIs and other access methods

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API
WebDAV
XQuery
XSLT

Java API
RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

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

Java
PHP
Python

C++

most other programming languages supported via APIs

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

JavaScript

Server-side scripts

Stored procedures

yes, in Lua

yes

on the application server

Java plugins

yes

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

yes

sychronisation to multiple collections

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency

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

multiple readers, single writer

optimistic locking

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

User concepts

Access control

Domain, group and role-based access control at the document level and for application services

yes

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