DBMS > HBase vs. IBM Db2 warehouse vs. IRONdb vs. RavenDB vs. RDF4J

System Properties Comparison HBase vs. IBM Db2 warehouse vs. IRONdb vs. RavenDB vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Cloud-based data warehousing service

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

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

hbase.apache.org

www.ibm.com/products/db2/warehouse

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

ravendb.net

rdf4j.org

Technical documentation

hbase.apache.org/book.html

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

ravendb.net/docs

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

IBM

Circonus LLC.

Hibernating Rhinos

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

Initial release

2008

2014

2017

2010

2004

Current release

2.3.4, January 2021

V0.10.20, January 2018

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

AGPL version 3, commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

Linux

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free, schema definition possible

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

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

SQL-like query language (RQL)

APIs and other access methods

Java API
RESTful HTTP API
Thrift

.NET Client API
JDBC
ODBC
OLE DB

HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

PL/SQL, SQL PL

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

configurable replication factor, datacenter aware

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

ACID, Cluster-wide transaction available

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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More resources

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Cloudera's HBase PaaS offering now supports Complex Transactions
11 August 2021, Krishna Maheshwari (sponsor)

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13 May 2013, Paul Andlinger

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