DBMS > Microsoft Azure AI Search vs. Quasardb vs. SiteWhere vs. SQLite

System Properties Comparison Microsoft Azure AI Search vs. Quasardb vs. SiteWhere vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Search-as-a-service for web and mobile app development

Distributed, high-performance timeseries database

M2M integration platform for persisting/querying time series data

Widely used embeddable, in-process RDBMS

Primary database model

Search engine

Time Series DBMS

Relational DBMS

Secondary database models

Vector DBMS

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

Score	5.71
Rank	#64	Overall
	#8	Search engines

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Website

azure.microsoft.com/en-us/services/search

quasar.ai

github.com/sitewhere/sitewhere

www.sqlite.org

Technical documentation

learn.microsoft.com/en-us/azure/search

doc.quasar.ai/master

sitewhere1.sitewhere.io/index.html

www.sqlite.org/docs.html

Developer

Microsoft

quasardb

SiteWhere

Dwayne Richard Hipp

Initial release

2015

2009

2010

2000

Current release

3.14.1, January 2024

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Common Public Attribution License Version 1.0

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X
Windows

server-less

Data scheme

yes

schema-free

predefined scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

HTTP API

HTTP REST

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
Java
JavaScript
Python

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

Sharding

Implicit feature of the cloud service

Sharding

consistent hashing

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Source-replica replication with selectable replication factor

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

via file-system locks

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

yes

using Azure authentication

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

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