DBMS > Amazon DocumentDB vs. Coveo vs. DataFS vs. Microsoft Azure AI Search

System Properties Comparison Amazon DocumentDB vs. Coveo vs. DataFS vs. Microsoft Azure AI Search

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Primary database model

Secondary database models

Graph DBMS

Vector DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	2.28
Rank	#114	Overall
	#11	Search engines

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Website

aws.amazon.com/documentdb

www.coveo.com

newdatabase.com

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

Technical documentation

aws.amazon.com/documentdb/resources

docs.coveo.com

dev.mobiland.com/Overview.xsp

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

Developer

Coveo

Mobiland AG

Microsoft

Initial release

2019

2012

2018

2015

Current release

1.1.263, October 2022

License

Commercial or Open Source

commercial

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.

Server operating systems

hosted

Windows

hosted

Data scheme

schema-free

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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.

Secondary indexes

yes

SQL

Support of SQL

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
Java
JavaScript
Python

.Net
C
C#
C++
VB.Net

C#
Java
JavaScript
Python

Server-side scripts

Stored procedures

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

yes

Proprietary Sharding system

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Atomic single-document operations

yes

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.

User concepts

Access control

Access rights for users and roles

granular access controls, API key management, content filters

Windows-Profile

yes

using Azure authentication

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More resources

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