DBMS > Microsoft SQL Server vs. Solr vs. Sphinx vs. Tibero

System Properties Comparison Microsoft SQL Server vs. Solr vs. Sphinx vs. Tibero

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Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

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 secure RDBMS, designed for easy portability from Oracle

Primary database model

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Score	6.03
Rank	#60	Overall
	#6	Search engines

Score	1.81
Rank	#143	Overall
	#65	Relational DBMS

Website

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

solr.apache.org

sphinxsearch.com

us.tmaxsoft.com/products/tibero

Technical documentation

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

solr.apache.org/resources.html

sphinxsearch.com/docs

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Microsoft

Apache Software Foundation

Sphinx Technologies Inc.

TmaxSoft

Initial release

1989

2006

2001

2003

Current release

SQL Server 2022, November 2022

9.5.0, February 2024

3.5.1, February 2023

6, April 2015

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

GPL version 2, commercial licence available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Java

C++

C and Assembler

Server operating systems

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

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

Solr Parallel SQL Interface

SQL-like query language (SphinxQL)

yes

APIs and other access methods

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

Java API
RESTful HTTP/JSON API

Proprietary protocol

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

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

.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
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

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

Java plugins

Persistent Stored Procedure (PSM)

Triggers

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

yes

none

Multi-source replication
Source-replica replication

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

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

planned for next version

User concepts

Access control

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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