DBMS > Apache IoTDB vs. Microsoft SQL Server vs. OceanBase vs. Tkrzw

System Properties Comparison Apache IoTDB vs. Microsoft SQL Server vs. OceanBase vs. Tkrzw

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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

A distributed, high available RDBMS compatible with Oracle and MySQL

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Wide column store

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	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

iotdb.apache.org

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

en.oceanbase.com

dbmx.net/tkrzw

Technical documentation

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

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

en.oceanbase.com/docs/oceanbase-database

Developer

Apache Software Foundation

Microsoft

OceanBase

previously Alibaba and Ant Group

Mikio Hirabayashi

Initial release

2018

1989

2010

2020

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

4.3.0, April 2024

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Commercial license available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Linux

Linux
macOS

Data scheme

yes

schema-free

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

APIs and other access methods

JDBC
Native API

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

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

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

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

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

none

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

Multi-source replication using Paxos

none

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

using specific database classes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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