DBMS > Bangdb vs. FileMaker vs. IRONdb vs. RDF4J vs. SWC-DB

System Properties Comparison Bangdb vs. FileMaker vs. IRONdb vs. RDF4J vs. SWC-DB

Editorial information provided by DB-Engines

Name

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

Description

Converged and high performance database for device data, events, time series, document and graph

FileMaker is a cross-platform RDBMS that includes a GUI 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.

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	0.15
Rank	#334	Overall
	#45	Document stores
	#31	Graph DBMS
	#31	Time Series DBMS

Score	49.73
Rank	#20	Overall
	#14	Relational DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.01
Rank	#387	Overall
	#13	Wide column stores

Website

bangdb.com

www.claris.com/filemaker

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

rdf4j.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.bangdb.com

www.claris.com/resources/documentation

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

rdf4j.org/documentation

Developer

Sachin Sinha, BangDB

Claris

a subsidiary of Apple

Circonus LLC.

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

Alex Kashirin

Initial release

2012

1983

2017

2004

2020

Current release

BangDB 2.0, October 2021

19.4.1, November 2021

V0.10.20, January 2018

0.5, April 2021

License

Commercial or Open Source

Open Source

BSD 3

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C and C++

Java

C++

Server operating systems

Linux

iOS

client part only
Linux
OS X
Windows

Linux

Linux
OS X
Unix
Windows

Linux

Data scheme

schema-free

yes

schema-free

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

A http query request can return the data in XML format

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

via plugins

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

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Filemaker WebDirect
JDBC
ODBC

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Java
Python

PHP

.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++

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Automatic, metric affinity per node

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication, warm standby

since Version 14

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

Tunable consistency, set CAP knob accordingly

Immediate consistency per node, eventual consistency across nodes

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

simple rights management via user accounts and connection to external directory services

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