DBMS > Apache IoTDB vs. Cubrid vs. Firebase Realtime Database vs. mSQL vs. MySQL

System Properties Comparison Apache IoTDB vs. Cubrid vs. Firebase Realtime Database vs. mSQL vs. MySQL

Editorial information provided by DB-Engines

Name

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used open source RDBMS

Primary database model

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

iotdb.apache.org

cubrid.com (korean)
cubrid.org (english)

firebase.google.com/products/realtime-database

hughestech.com.au/products/msql

www.mysql.com

Technical documentation

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

cubrid.org/manuals

firebase.google.com/docs/database

dev.mysql.com/doc

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Google

acquired by Google 2014

Hughes Technologies

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2018

2008

2012

1994

1995

Current release

1.1.0, April 2023

11.0, January 2021

4.4, October 2021

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C, C++, Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

hosted

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

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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

yes

SQL

Support of SQL

SQL-like query language

yes

A subset of ANSI SQL is implemented

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

yes

with proprietary extensions

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
JavaScript
Objective-C

C
C++
Delphi
Java
Perl
PHP
Tcl

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Java Stored Procedures

limited functionality with using 'rules'

yes

proprietary syntax

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

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

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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

Source-replica replication

none

Multi-source replication
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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

none

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

Users with fine-grained authorization concept

no user groups or roles

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