DBMS > Apache IoTDB vs. DolphinDB vs. Microsoft SQL Server vs. STSdb vs. TerminusDB

System Properties Comparison Apache IoTDB vs. DolphinDB vs. Microsoft SQL Server vs. STSdb vs. TerminusDB

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

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Microsofts flagship relational DBMS

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

Document store
RDF store

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

iotdb.apache.org

www.dolphindb.com

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

github.com/STSSoft/STSdb4

terminusdb.com

Technical documentation

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

docs.dolphindb.cn/en/help200/index.html

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

terminusdb.github.io/terminusdb/#

Developer

Apache Software Foundation

DolphinDB, Inc

Microsoft

STS Soft SC

DataChemist Ltd.

Initial release

2018

1989

2011

2018

Current release

1.1.0, April 2023

v2.00.4, January 2022

SQL Server 2022, November 2022

4.0.8, September 2015

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free community version available

commercial

restricted free version is available

Open Source

GPLv2, commercial license available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Prolog, Rust

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
Windows

Windows

Linux

Data scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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 (WOQL)

APIs and other access methods

JDBC
Native API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

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

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

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

C#
Java

JavaScript
Python

Server-side scripts

Stored procedures

yes

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

horizontal partitioning

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

none

Graph Partitioning

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

yes, but depending on the SQL-Server Edition

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory journaling

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

Administrators, Users, Groups

fine grained access rights according to SQL-standard

Role-based access control

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