DBMS > Kinetica vs. Manticore Search vs. Microsoft Azure AI Search vs. SAP SQL Anywhere vs. SQLite

System Properties Comparison Kinetica vs. Manticore Search vs. Microsoft Azure AI Search vs. SAP SQL Anywhere vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Multi-storage database for search, including full-text search.

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Time Series DBMS

using the Manticore Columnar Library

Vector DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.kinetica.com

manticoresearch.com

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

www.sap.com/products/technology-platform/sql-anywhere.html

www.sqlite.org

Technical documentation

docs.kinetica.com

manual.manticoresearch.com

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

help.sap.com/docs/SAP_SQL_Anywhere

www.sqlite.org/docs.html

Developer

Kinetica

Manticore Software

Microsoft

SAP

formerly Sybase

Dwayne Richard Hipp

Initial release

2012

2017

2015

1992

2000

Current release

7.1, August 2021

6.0, February 2023

17, July 2015

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2

commercial

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, C++

C++

Server operating systems

Linux

FreeBSD
Linux
macOS
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows

server-less

Data scheme

yes

Fixed schema

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

RESTful HTTP API

ADO.NET
HTTP API
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C#
Java
JavaScript
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

user defined functions

yes, in C/C++, Java, .Net or Perl

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Synchronous replication based on Galera library

yes

Implicit feature of the cloud service

Source-replica replication

Database mirroring

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

yes

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

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

yes

using Azure authentication

fine grained access rights according to SQL-standard

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