DBMS > Microsoft SQL Server vs. Sadas Engine vs. Solr vs. Sphinx vs. Tkrzw

System Properties Comparison Microsoft SQL Server vs. Sadas Engine vs. Solr vs. Sphinx vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

A widely used distributed, scalable search engine based on Apache Lucene

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

Relational DBMS

Search engine

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

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

Technical documentation

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

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

solr.apache.org/resources.html

sphinxsearch.com/docs

Developer

Microsoft

SADAS s.r.l.

Apache Software Foundation

Sphinx Technologies Inc.

Mikio Hirabayashi

Initial release

1989

2006

2001

2020

Current release

SQL Server 2022, November 2022

8.0

9.6.0, April 2024

3.5.1, February 2023

0.9.3, August 2020

License

Commercial or Open Source

commercial

restricted free version is available

commercial

free trial version available

Open Source

Apache Version 2

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux
Windows

AIX
Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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 search fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

yes

Solr Parallel SQL Interface

SQL-like query language (SphinxQL)

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol

Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

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

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

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

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, but depending on the SQL-Server Edition

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

managed by 'Learn by Usage'

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

yes

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