DBMS > Adabas vs. Microsoft Azure AI Search vs. MySQL vs. Sphinx vs. Transwarp Hippo

System Properties Comparison Adabas vs. Microsoft Azure AI Search vs. MySQL vs. Sphinx vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

OLTP - DBMS for mainframes and Linux/Unix/Windows environments

used typically together with the Natural programming platform

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

Widely used open source RDBMS

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Multivalue DBMS

Search engine

Relational DBMS

Key/Value like access via memcached API

Search engine

Vector DBMS

Secondary database models

Vector DBMS

Document store
Spatial DBMS

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

Score	2.64
Rank	#104	Overall
	#1	Multivalue DBMS

Score	5.75
Rank	#58	Overall
	#7	Search engines

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	5.97
Rank	#56	Overall
	#5	Search engines

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

www.softwareag.com/en_corporate/platform/adabas-natural.html

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

www.mysql.com

sphinxsearch.com

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

dev.mysql.com/doc

sphinxsearch.com/docs

Developer

Software AG

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Sphinx Technologies Inc.

Initial release

1971

2015

1995

2001

2023

Current release

9.0.0, July 2024

3.5.1, February 2023

1.0, May 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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

C and C++

C++

Server operating systems

BS2000
Linux
Unix
Windows
z/OS
z/VSE

hosted

FreeBSD
Linux
OS X
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

full-text index on all search fields

SQL

Support of SQL

yes

with add-on product Adabas SQL Gateway

yes

with proprietary extensions

SQL-like query language (SphinxQL)

APIs and other access methods

HTTP API

with add-on software Adabas SOA Gateway
SOAP-based API

with add-on software Adabas SOA Gateway

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol

RESTful HTTP API

Supported programming languages

Natural

C#
Java
JavaScript
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
Python

Server-side scripts

Stored procedures

in Natural

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas Vista

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with add-on product Event Replicator

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

only with OS-specific tools (e.g. IBM RACF, CA Top Secret)

yes

using Azure authentication

Users with fine-grained authorization concept

no user groups or roles

Role based access control and fine grained access rights

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