DBMS > Apache IoTDB vs. SQLite

System Properties Comparison Apache IoTDB vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

A lightweight IoT native database management system deployable on the device and syncing the collected data with the cloud and integrated with Hadoop and Spark

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Relational DBMS

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Trend Chart

Score	0.19
Rank	#296	Overall
	#24	Time Series DBMS

Score	130.20
Rank	#9	Overall
	#6	Relational DBMS

Website

iotdb.apache.org

www.sqlite.org

Technical documentation

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2018

2000

Current release

3.36.0 (18 June 2021), June 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

Java

Server operating systems

All OS with a Java VM (>= 1.8)

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

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 with synchronizing with the cloud

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

via file-system locks

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

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