DBMS > Manticore Search vs. Microsoft SQL Server vs. Quasardb vs. SQLite vs. Ultipa

System Properties Comparison Manticore Search vs. Microsoft SQL Server vs. Quasardb vs. SQLite vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

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

Microsofts flagship relational DBMS

Distributed, high-performance timeseries database

Widely used embeddable, in-process RDBMS

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

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

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

manticoresearch.com

www.microsoft.com/en-us/sql-server

quasar.ai

www.sqlite.org

www.ultipa.com

Technical documentation

manual.manticoresearch.com

learn.microsoft.com/en-US/sql/sql-server

doc.quasar.ai/master

www.sqlite.org/docs.html

www.ultipa.com/document

Developer

Manticore Software

Microsoft

quasardb

Dwayne Richard Hipp

Ultipa

Initial release

2017

1989

2009

2000

2019

Current release

6.0, February 2023

SQL Server 2022, November 2022

3.14.1, January 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

GPL version 2

commercial

restricted free version is available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Public Domain

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux
Windows

BSD
Linux
OS X
Windows

server-less

Data scheme

Fixed schema

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

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

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

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

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

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

RESTful HTTP API

Supported programming languages

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

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

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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