DBMS > Microsoft Access vs. MySQL vs. Splice Machine vs. XTDB

System Properties Comparison Microsoft Access vs. MySQL vs. Splice Machine vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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.)

Widely used open source RDBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	105.40
Rank	#11	Overall
	#8	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Score	0.09
Rank	#351	Overall
	#47	Document stores

Website

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

www.mysql.com

splicemachine.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

developer.microsoft.com/en-us/access

dev.mysql.com/doc

splicemachine.com/how-it-works

www.xtdb.com/docs

Developer

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Splice Machine

Juxt Ltd.

Initial release

1992

1995

2014

2019

Current release

1902 (16.0.11328.20222), March 2019

8.3.0, January 2024

3.1, March 2021

1.19, September 2021

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL 3.0, commercial license available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C++

C and C++

Java

Clojure

Server operating systems

Windows

Not a real database server, but making use of DLLs

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

with proprietary extensions

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
Native Spark Datasource
ODBC

HTTP REST
JDBC

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

yes

proprietary syntax

yes

Java

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

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

Users with fine-grained authorization concept

no user groups or roles

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

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