DBMS > Fauna vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Teradata

System Properties Comparison Fauna vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. Sphinx vs. Teradata

Editorial information provided by DB-Engines

Name

Description

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

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

Globally distributed, horizontally scalable, multi-model database service

Open source search engine for searching in data from different sources, e.g. relational databases

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Search engine

Relational DBMS

Secondary database models

Vector DBMS

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.55
Rank	#151	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	5.52
Rank	#59	Overall
	#6	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	44.87
Rank	#22	Overall
	#15	Relational DBMS

Website

fauna.com

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

azure.microsoft.com/services/cosmos-db

sphinxsearch.com

www.teradata.com

Technical documentation

docs.fauna.com

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

learn.microsoft.com/azure/cosmos-db

sphinxsearch.com/docs

docs.teradata.com

Developer

Fauna, Inc.

Microsoft

Sphinx Technologies Inc.

Teradata

Initial release

2014

2015

2014

2001

1984

Current release

3.5.1, February 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

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.

Implementation language

Scala

C++

Server operating systems

hosted

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

hosted
Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

full-text index on all search fields

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary protocol

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

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

Identity management, authentication, and access control

yes

using Azure authentication

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

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