DBMS > CouchDB vs. Firebase Realtime Database vs. GeoSpock vs. PouchDB vs. SQLite

System Properties Comparison CouchDB vs. Firebase Realtime Database vs. GeoSpock vs. PouchDB vs. SQLite

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

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.

Spatial and temporal data processing engine for extreme data scale

JavaScript DBMS with an API inspired by CouchDB

Widely used embeddable, in-process RDBMS

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Time Series DBMS

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

couchdb.apache.org

firebase.google.com/products/realtime-database

geospock.com

pouchdb.com

www.sqlite.org

Technical documentation

docs.couchdb.org/en/stable

firebase.google.com/docs/database

pouchdb.com/guides

www.sqlite.org/docs.html

Developer

Apache Software Foundation

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

Google

acquired by Google 2014

GeoSpock

Apache Software Foundation

Dwayne Richard Hipp

Initial release

2005

2012

2000

Current release

3.3.3, December 2023

2.0, September 2019

7.1.1, June 2019

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Public Domain

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, Javascript

JavaScript

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

hosted

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

server-less

Data scheme

schema-free

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

via views

yes

temporal, categorical

yes

via views

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

JDBC

HTTP REST

only for PouchDB Server
JavaScript API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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

JavaScript

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

View functions in JavaScript

limited functionality with using 'rules'

View functions in JavaScript

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Automatic sharding

Sharding

with a proxy-based framework, named couchdb-lounge

none

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

none

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

Immediate Consistency

Eventual 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

via file-system locks

Durability

Support for making data persistent

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

Access rights for users can be defined per table

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