DBMS > MonetDB vs. MySQL vs. RDFox vs. SingleStore vs. Spark SQL

System Properties Comparison MonetDB vs. MySQL vs. RDFox vs. SingleStore vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A relational database management system that stores data in columns

Widely used open source RDBMS

High performance knowledge graph and semantic reasoning engine

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key/Value like access via memcached API

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Relational DBMS

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.monetdb.org

www.mysql.com

www.oxfordsemantic.tech

www.singlestore.com

spark.apache.org/sql

Technical documentation

www.monetdb.org/Documentation

dev.mysql.com/doc

docs.oxfordsemantic.tech

docs.singlestore.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

MonetDB BV

Oracle

since 2010, originally MySQL AB, then Sun

Oxford Semantic Technologies

SingleStore Inc.

Apache Software Foundation

Initial release

2004

1995

2017

2013

2014

Current release

Dec2023 (11.49), December 2023

8.4.0, April 2024

6.0, Septermber 2022

8.5, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Mozilla Public License 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free developer edition available

Open Source

Apache 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 and C++

C++

C++, Go

Scala

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

64 bit version required

Linux
OS X
Windows

Data scheme

yes

RDF schemas

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

with proprietary extensions

yes

but no triggers and foreign keys

SQL-like DML and DDL statements

APIs and other access methods

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API
SPARQL 1.1

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

JDBC
ODBC

Supported programming languages

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

C
Java

Bash
C
C#
Java
JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes, in SQL, C, R

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding via remote tables

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

hash partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

replication via a shared file system

Source-replica replication

stores two copies of each physical data partition on two separate nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

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

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Users with fine-grained authorization concept

no user groups or roles

Roles, resources, and access types

Fine grained access control via users, groups and roles

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

F ree Tier and Enterprise Edition
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