DBMS > Apache Phoenix vs. Microsoft Azure Table Storage vs. PouchDB vs. searchxml vs. SQLite

System Properties Comparison Apache Phoenix vs. Microsoft Azure Table Storage vs. PouchDB vs. searchxml vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

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

JavaScript DBMS with an API inspired by CouchDB

DBMS for structured and unstructured content wrapped with an application server

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Wide column store

Document store

Native XML DBMS
Search engine

Relational DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

phoenix.apache.org

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

pouchdb.com

www.searchxml.net/category/products

www.sqlite.org

Technical documentation

phoenix.apache.org

pouchdb.com/guides

www.searchxml.net/support/handouts

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Microsoft

Apache Software Foundation

informationpartners gmbh

Dwayne Richard Hipp

Initial release

2014

2012

2015

2000

Current release

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

7.1.1, June 2019

1.0

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

commercial

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

JavaScript

C++

Server operating systems

Linux
Unix
Windows

hosted

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

Windows

server-less

Data scheme

yes

late-bound, schema-on-read capabilities

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.

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

RESTful HTTP API
WebDAV
XQuery
XSLT

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

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

JavaScript

C++

most other programming languages supported via APIs

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

user defined functions

View functions in JavaScript

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

yes

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

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

sychronisation to multiple collections

none

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

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

Access rights based on private key authentication or shared access signatures

Domain, group and role-based access control at the document level and for application services

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