DBMS > Apache IoTDB vs. Apache Phoenix vs. mSQL vs. OrigoDB vs. PostgreSQL

System Properties Comparison Apache IoTDB vs. Apache Phoenix vs. mSQL vs. OrigoDB vs. PostgreSQL

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

A scale-out RDBMS with evolutionary schema built on Apache HBase

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Time Series DBMS

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

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

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

iotdb.apache.org

phoenix.apache.org

hughestech.com.au/products/msql

origodb.com

www.postgresql.org

Technical documentation

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

phoenix.apache.org

origodb.com/docs

www.postgresql.org/docs

Developer

Apache Software Foundation

Hughes Technologies

Robert Friberg et al

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2018

2014

1994

2009

under the name LiveDB

1989

1989: Postgres, 1996: PostgreSQL

Current release

1.1.0, April 2023

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

4.4, October 2021

16.3, May 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

BSD

Cloud-based only

Only available as a cloud service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Unix
Windows

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

Linux
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Typing

predefined data types such as float or date

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

yes

specific XML-type available, but no XML query functionality.

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

standard with numerous extensions

APIs and other access methods

JDBC
Native API

JDBC

JDBC
ODBC

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

none

horizontal partitioning

client side managed; servers are not synchronized

partitioning by range, list and (since PostgreSQL 11) by hash

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

Multi-source replication
Source-replica replication

none

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Role based authorization

fine grained access rights according to SQL-standard

More information provided by the system vendor

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