DBMS > Altibase vs. Microsoft Access vs. Microsoft SQL Server vs. Solr vs. SWC-DB

System Properties Comparison Altibase vs. Microsoft Access vs. Microsoft SQL Server vs. Solr vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Microsofts flagship relational DBMS

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Search engine

Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

Time Series DBMS

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

Score	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

altibase.com

www.microsoft.com/en-us/microsoft-365/access

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

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

developer.microsoft.com/en-us/access

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

solr.apache.org/resources.html

Developer

Altibase

Microsoft

Apache Software Foundation

Alex Kashirin

Initial release

1999

1992

1989

2006

2020

Current release

v7.3, 2023, August 2023

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

9.6.0, April 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

C++

Server operating systems

AIX
HP-UX
Linux

Windows

Not a real database server, but making use of DLLs

Linux
Windows

All OS with a Java VM

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

Linux

Data scheme

yes

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

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

SQL

Support of SQL

ANSI SQL-92

yes

but not compliant to any SQL standard

yes

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol

ADO.NET
DAO
ODBC
OLE DB

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

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

C
C++
Java

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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++

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

since Access 2010 using the ACE-engine

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

Java plugins

Triggers

yes

since Access 2010 using the ACE-engine

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes, but depending on the SQL-Server Edition

yes

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

but no files for transaction logging

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

fine grained access rights according to SQL-standard

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

yes

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