DBMS > Drizzle vs. HBase vs. IRONdb vs. RDF4J vs. Riak KV

System Properties Comparison Drizzle vs. HBase vs. IRONdb vs. RDF4J vs. Riak KV

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

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.

Distributed, fault tolerant key-value store

Primary database model

with links between data sets and object tags for the creation of secondary indexes

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

hbase.apache.org

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

rdf4j.org

Technical documentation

hbase.apache.org/book.html

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

rdf4j.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Drizzle project, originally started by Brian Aker

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Circonus LLC.

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

OpenSource, formerly Basho Technologies

Initial release

2008

2017

2004

2009

Current release

7.2.4, September 2012

2.3.4, January 2021

V0.10.20, January 2018

3.2.0, December 2022

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache version 2

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache version 2, commercial enterprise edition

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 and C++

Java

Erlang

Server operating systems

FreeBSD
Linux
OS X

Linux
Unix
Windows

using Cygwin

Linux

Linux
OS X
Unix
Windows

Linux
OS X

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

with proprietary extensions

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

APIs and other access methods

JDBC

Java API
RESTful HTTP API
Thrift

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API
Native Erlang Interface

Supported programming languages

C
C++
Java
PHP

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes, in Lua

yes

Erlang

Triggers

hooks for callbacks inside the server can be used.

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

none

selectable replication factor

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 per node, eventual consistency across nodes

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

yes, using Riak Security

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