DBMS > Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. SAP Adaptive Server vs. Sphinx vs. Tkrzw

System Properties Comparison Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB vs. SAP Adaptive Server vs. Sphinx vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

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

Globally distributed, horizontally scalable, multi-model database service

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Search engine

Key-value store

Secondary database models

Vector DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

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	35.09
Rank	#25	Overall
	#17	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

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

azure.microsoft.com/services/cosmos-db

www.sap.com/products/technology-platform/sybase-ase.html

sphinxsearch.com

dbmx.net/tkrzw

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

help.sap.com/docs/SAP_ASE

sphinxsearch.com/docs

Developer

Microsoft

SAP, Sybase

Sphinx Technologies Inc.

Mikio Hirabayashi

Initial release

2015

2014

1987

2001

2020

Current release

16.0

3.5.1, February 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 2.0

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 and C++

C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

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.

Secondary indexes

yes

All properties auto-indexed by default

yes

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

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

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

Proprietary protocol

Supported programming languages

C#
Java
JavaScript
Python

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

JavaScript

Java and Transact-SQL

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

horizontal partitioning

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

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

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

using specific database classes

User concepts

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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