DBMS > Apache IoTDB vs. IRONdb vs. mSQL vs. OrigoDB vs. VelocityDB

System Properties Comparison Apache IoTDB vs. IRONdb vs. mSQL vs. OrigoDB vs. VelocityDB

Editorial information provided by DB-Engines

Name

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

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Graph DBMS
Object oriented DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

iotdb.apache.org

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

hughestech.com.au/products/msql

origodb.com

velocitydb.com

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

origodb.com/docs

velocitydb.com/UserGuide

Developer

Apache Software Foundation

Circonus LLC.

Hughes Technologies

Robert Friberg et al

VelocityDB Inc

Initial release

2018

2017

1994

2009

under the name LiveDB

2011

Current release

1.1.0, April 2023

V0.10.20, January 2018

4.4, October 2021

7.x

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

commercial

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

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

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

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
Native API

HTTP API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

.Net

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

yes

yes, in Lua

yes

Triggers

yes

Domain Events

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Automatic, metric affinity per node

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

configurable replication factor, datacenter aware

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate consistency per node, eventual consistency across nodes

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

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

yes

Role based authorization

Based on Windows Authentication

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