DBMS > CouchDB vs. Firebase Realtime Database vs. NSDb vs. PouchDB vs. Snowflake

System Properties Comparison CouchDB vs. Firebase Realtime Database vs. NSDb vs. PouchDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

JavaScript DBMS with an API inspired by CouchDB

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

couchdb.apache.org

firebase.google.com/products/realtime-database

nsdb.io

pouchdb.com

www.snowflake.com

Technical documentation

docs.couchdb.org/en/stable

firebase.google.com/docs/database

nsdb.io/Architecture

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

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

Google

acquired by Google 2014

Apache Software Foundation

Snowflake Computing Inc.

Initial release

2005

2012

2017

2012

2014

Current release

3.3.3, December 2023

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Apache Version 2.0

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java, Scala

JavaScript

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS

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

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

via views

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

gRPC
HTTP REST
WebSocket

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Supported programming languages

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

Java
JavaScript
Objective-C

Java
Scala

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

View functions in JavaScript

limited functionality with using 'rules'

View functions in JavaScript

user defined functions

Triggers

yes

Callbacks are triggered when data changes

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

by using IndexedDB, WebSQL or LevelDB as backend

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

Access rights for users can be defined per database

yes, based on authentication and database rules

Users with fine-grained authorization concept, user roles and pluggable authentication

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