DBMS > Apache Phoenix vs. CouchDB vs. Firebase Realtime Database vs. PostgreSQL vs. SWC-DB

System Properties Comparison Apache Phoenix vs. CouchDB vs. Firebase Realtime Database vs. PostgreSQL vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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.

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Time Series DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

phoenix.apache.org

couchdb.apache.org

firebase.google.com/products/realtime-database

www.postgresql.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

phoenix.apache.org

docs.couchdb.org/en/stable

firebase.google.com/docs/database

www.postgresql.org/docs

Developer

Apache Software Foundation

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

Google

acquired by Google 2014

PostgreSQL Global Development Group

www.postgresql.org/developer

Alex Kashirin

Initial release

2014

2005

2012

1989

1989: Postgres, 1996: PostgreSQL

2020

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

3.3.3, December 2023

16.3, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache version 2

commercial

Open Source

BSD

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Erlang

C++

Server operating systems

Linux
Unix
Windows

Android
BSD
Linux
OS X
Solaris
Windows

hosted

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

standard with numerous extensions

SQL-like query language

APIs and other access methods

JDBC

RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

Java
JavaScript
Objective-C

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

Server-side scripts

Stored procedures

user defined functions

View functions in JavaScript

limited functionality with using 'rules'

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

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

partitioning by range, list and (since PostgreSQL 11) by hash

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Access rights for users can be defined per database

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

More information provided by the system vendor

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