DBMS > Amazon DynamoDB vs. CouchDB vs. Kinetica vs. Microsoft Azure Table Storage vs. MySQL

System Properties Comparison Amazon DynamoDB vs. CouchDB vs. Kinetica vs. Microsoft Azure Table Storage vs. MySQL

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Fully vectorized database across both GPUs and CPUs

A Wide Column Store for rapid development using massive semi-structured datasets

Widely used open source RDBMS

Primary database model

Document store
Key-value store

Key/Value like access via memcached API

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/dynamodb

couchdb.apache.org

www.kinetica.com

azure.microsoft.com/en-us/services/storage/tables

www.mysql.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.couchdb.org/en/stable

docs.kinetica.com

dev.mysql.com/doc

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Kinetica

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2005

2012

1995

Current release

3.3.3, December 2023

7.1, August 2021

8.4.0, April 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Erlang

C, C++

C and C++

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

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

via views

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Java
JavaScript (Node.js)
Python

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

View functions in JavaScript

user defined functions

yes

proprietary syntax

Triggers

yes

by integration with AWS Lambda

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Source-replica replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

ACID across one or more tables within a single AWS account and region

atomic operations within a single document possible

optimistic locking

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users can be defined per database

Access rights for users and roles on table level

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

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