DBMS > IBM Db2 Event Store vs. Infobright vs. IRONdb vs. RDF4J

System Properties Comparison IBM Db2 Event Store vs. Infobright vs. IRONdb vs. RDF4J

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Editorial information provided by DB-Engines

Name

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

Description

Distributed Event Store optimized for Internet of Things use cases

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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.

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Time Series DBMS

RDF store

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.ibm.com/products/db2-event-store

ignitetech.com/softwarelibrary/infobrightdb

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

rdf4j.org

Technical documentation

www.ibm.com/docs/en/db2-event-store

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

rdf4j.org/documentation

Developer

IBM

Ignite Technologies Inc.; formerly InfoBright Inc.

Circonus LLC.

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

Initial release

2017

2005

2017

2004

Current release

2.0

V0.10.20, January 2018

License

Commercial or Open Source

commercial

free developer edition available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
Windows

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.

Secondary indexes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

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

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

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++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Java
PHP
Python

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

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

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

exploiting MySQL or PostgreSQL frontend capabilities

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