DBMS > Apache IoTDB vs. Citus vs. H2 vs. InfluxDB vs. Microsoft SQL Server

System Properties Comparison Apache IoTDB vs. Citus vs. H2 vs. InfluxDB vs. Microsoft SQL Server

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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

DBMS for storing time series, events and metrics

Microsofts flagship relational DBMS

Primary database model

Secondary database models

Document store

Spatial DBMS

with GEO package

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	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

iotdb.apache.org

www.citusdata.com

www.h2database.com

www.influxdata.com/products/influxdb-overview

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

Technical documentation

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

docs.citusdata.com

www.h2database.com/html/main.html

docs.influxdata.com/influxdb

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

Developer

Apache Software Foundation

Thomas Mueller

Microsoft

Initial release

2018

2010

2005

2013

1989

Current release

1.1.0, April 2023

8.1, December 2018

2.2.220, July 2023

2.7.6, April 2024

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

AGPL, commercial license also available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

MIT-License; commercial enterprise version available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux

All OS with a Java VM

Linux
OS X

through Homebrew

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

standard, with numerous extensions

yes

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

HTTP API
JSON over UDP

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

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Java Stored Procedures and User-Defined Functions

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

none

Sharding

in enterprise version only

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

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

Source-replica replication

other methods possible by using 3rd party extensions

With clustering: 2 database servers on different computers operate on identical copies of a database

selectable replication factor

in enterprise version only

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

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

yes, multi-version concurrency control (MVCC)

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

Depending on used storage engine

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

simple rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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