DBMS > Apache IoTDB vs. JaguarDB vs. Microsoft SQL Server vs. NSDb vs. STSdb

System Properties Comparison Apache IoTDB vs. JaguarDB vs. Microsoft SQL Server vs. NSDb vs. STSdb

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

Performant, highly scalable DBMS for AI and IoT applications

Microsofts flagship relational DBMS

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Time Series DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
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	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

iotdb.apache.org

www.jaguardb.com

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

nsdb.io

github.com/STSSoft/STSdb4

Technical documentation

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

www.jaguardb.com/support.html

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

nsdb.io/Architecture

Developer

Apache Software Foundation

DataJaguar, Inc.

Microsoft

STS Soft SC

Initial release

2018

2015

1989

2017

2011

Current release

1.1.0, April 2023

3.3 July 2023

SQL Server 2022, November 2022

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL V3.0

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

the server part. Clients available in other languages

C++

Java, Scala

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

Linux
Windows

Linux
macOS

Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

yes

primitive types and user defined types (classes)

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

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL-like query language

APIs and other access methods

JDBC
Native API

JDBC
ODBC

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

gRPC
HTTP REST
WebSocket

.NET Client API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

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

Java
Scala

C#
Java

Server-side scripts

Stored procedures

yes

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

Sharding

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

Multi-source replication

yes, but depending on the SQL-Server Edition

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

Eventual Consistency

Immediate Consistency

Eventual 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

Using Apache Lucene

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

rights management via user accounts

fine grained access rights according to SQL-standard

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