DBMS > Microsoft Azure Cosmos DB vs. MySQL vs. RDFox vs. SingleStore

System Properties Comparison Microsoft Azure Cosmos DB vs. MySQL vs. RDFox vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Spatial DBMS

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	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	0.22
Rank	#305	Overall
	#25	Graph DBMS
	#15	RDF stores

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

azure.microsoft.com/services/cosmos-db

www.mysql.com

www.oxfordsemantic.tech

www.singlestore.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

dev.mysql.com/doc

docs.oxfordsemantic.tech

docs.singlestore.com

Developer

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Oxford Semantic Technologies

SingleStore Inc.

Initial release

2014

1995

2017

2013

Current release

9.0.0, July 2024

6.0, Septermber 2022

8.5, January 2024

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

C++

C++, Go

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Linux

64 bit version required

Data scheme

schema-free

yes

RDF schemas

yes

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

but no triggers and foreign keys

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Server-side scripts

Stored procedures

JavaScript

yes

proprietary syntax

yes

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

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

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

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

Multi-item ACID transactions with snapshot isolation within a partition

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)

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.

In-memory capabilities

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

yes

User concepts

Access control

Access rights can be defined down to the item level

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

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