DBMS > Amazon DocumentDB vs. Blueflood vs. CouchDB vs. Microsoft SQL Server vs. PouchDB

System Properties Comparison Amazon DocumentDB vs. Blueflood vs. CouchDB vs. Microsoft SQL Server vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Scalable TimeSeries DBMS based on Cassandra

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

Microsofts flagship relational DBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Graph DBMS
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

aws.amazon.com/documentdb

blueflood.io

couchdb.apache.org

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

pouchdb.com

Technical documentation

aws.amazon.com/documentdb/resources

github.com/rax-maas/blueflood/wiki

docs.couchdb.org/en/stable

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

pouchdb.com/guides

Developer

Rackspace

Apache Software Foundation

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

Microsoft

Apache Software Foundation

Initial release

2019

2013

2005

1989

2012

Current release

3.3.3, December 2023

SQL Server 2022, November 2022

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Erlang

C++

JavaScript

Server operating systems

hosted

Linux
OS X

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

schema-free

predefined scheme

schema-free

yes

schema-free

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

via views

SQL

Support of SQL

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HTTP REST

RESTful HTTP/JSON API

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

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript

Server-side scripts

Stored procedures

View functions in JavaScript

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

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

improved architecture with release 2.0

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

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

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

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

atomic operations within a single document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 for users and roles

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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