DBMS > IRONdb vs. RDF4J vs. SAP HANA vs. SAP SQL Anywhere vs. Solr

System Properties Comparison IRONdb vs. RDF4J vs. SAP HANA vs. SAP SQL Anywhere vs. Solr

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.

In-memory, column based data store. Available as appliance or cloud service

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

Spatial DBMS

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

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

rdf4j.org

www.sap.com/products/hana.html

www.sap.com/products/technology-platform/sql-anywhere.html

solr.apache.org

Technical documentation

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

rdf4j.org/documentation

help.sap.com/hana

help.sap.com/docs/SAP_SQL_Anywhere

solr.apache.org/resources.html

Developer

Circonus LLC.

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

SAP

formerly Sybase

Apache Software Foundation

Initial release

2017

2004

2010

1992

2006

Current release

V0.10.20, January 2018

2.0 SPS07 (April 4, 2023), April 2023

17, July 2015

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

also available as a cloud based 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

Server operating systems

Linux

Linux
OS X
Unix
Windows

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris
Windows

All OS with a Java VM

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

Data scheme

schema-free

yes

RDF Schemas

yes

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

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

supports customizable data types and automatic typing

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)

yes

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

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

Java API
RESTful HTTP/JSON 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
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes, in Lua

yes

SQLScript, R

yes, in C/C++, Java, .Net or Perl

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

none

yes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

none

yes

Source-replica replication

Database mirroring

yes

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

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

ACID

optimistic locking

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

yes

fine grained access rights according to SQL-standard

yes

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