DBMS > Apache Druid vs. Apache IoTDB vs. JaguarDB vs. Microsoft SQL Server

System Properties Comparison Apache Druid vs. Apache IoTDB vs. JaguarDB vs. Microsoft SQL Server

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Primary database model

Relational DBMS
Time Series DBMS

Time Series DBMS

Key-value store
Vector DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Website

druid.apache.org

iotdb.apache.org

www.jaguardb.com

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

Technical documentation

druid.apache.org/docs/latest/design

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

www.jaguardb.com/support.html

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

Developer

Apache Software Foundation and contributors

Apache Software Foundation

DataJaguar, Inc.

Microsoft

Initial release

2012

2018

2015

1989

Current release

29.0.1, April 2024

1.1.0, April 2023

3.3 July 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache license v2

Open Source

Apache Version 2.0

Open Source

GPL V3.0

commercial

restricted free version is 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++

Server operating systems

Linux
OS X
Unix

All OS with a Java VM (>= 1.8)

Linux

Linux
Windows

Data scheme

yes

schema-less columns are supported

yes

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 for querying

SQL-like query language

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

JDBC
Native API

JDBC
ODBC

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

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

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

Sharding

manual/auto, time-based

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

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

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

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

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency

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

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

yes

rights management via user accounts

fine grained access rights according to SQL-standard

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