DBMS > Amazon Redshift vs. EXASOL vs. IRONdb vs. OceanBase vs. RDF4J

System Properties Comparison Amazon Redshift vs. EXASOL vs. IRONdb vs. OceanBase vs. RDF4J

Editorial information provided by DB-Engines

Name

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

Description

Large scale data warehouse service for use with business intelligence tools

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

A distributed, high available RDBMS compatible with Oracle and MySQL

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

Primary database model

Secondary database models

Document store
Wide column store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

aws.amazon.com/redshift

www.exasol.com

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

en.oceanbase.com

rdf4j.org

Technical documentation

docs.aws.amazon.com/redshift

www.exasol.com/resources

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

en.oceanbase.com/docs/oceanbase-database

rdf4j.org/documentation

Developer

Amazon (based on PostgreSQL)

Exasol

Circonus LLC.

OceanBase

previously Alibaba and Ant Group

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

Initial release

2012

2000

2017

2010

2004

Current release

V0.10.20, January 2018

4.3.0, April 2024

License

Commercial or Open Source

commercial

Open Source

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

C and C++

C++

Java

Server operating systems

hosted

Linux

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

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

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

yes

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

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

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes, in Lua

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

configurable replication factor, datacenter aware

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

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

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.