DBMS > Apache IoTDB vs. Microsoft SQL Server vs. openGemini vs. Realm vs. Tibero

System Properties Comparison Apache IoTDB vs. Microsoft SQL Server vs. openGemini vs. Realm vs. Tibero

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

Microsofts flagship relational DBMS

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

iotdb.apache.org

www.microsoft.com/en-us/sql-server

www.opengemini.org
github.com/openGemini

realm.io

us.tmaxsoft.com/products/tibero

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

docs.opengemini.org/guide

realm.io/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

Microsoft

Huawei and openGemini community

Realm, acquired by MongoDB in May 2019

TmaxSoft

Initial release

2018

1989

2022

2014

2003

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

1.1, July 2023

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and Assembler

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Android
Backend: server-less
iOS
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

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

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

.Net
Java

with Android only
Objective-C
React Native
Swift

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

runs within the applications so server-side scripts are unnecessary

Persistent Stored Procedure (PSM)

Triggers

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

none

horizontal partitioning

by range, hash, list or composite

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

yes, but depending on the SQL-Server Edition

yes

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

Foreign keys

Referential integrity

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-Memory realm

planned for next version

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Administrators and common users accounts

yes

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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