DBMS > Amazon CloudSearch vs. Apache Impala vs. eXtremeDB vs. MySQL vs. PouchDB

System Properties Comparison Amazon CloudSearch vs. Apache Impala vs. eXtremeDB vs. MySQL vs. PouchDB

Editorial information provided by DB-Engines

Name

Description

A hosted search engine service by Amazon with the data stored in Amazons cloud

Analytic DBMS for Hadoop

Natively in-memory DBMS with options for persistency, high-availability and clustering

Widely used open source RDBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Search engine

Relational DBMS

Relational DBMS
Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Document store

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	1.85
Rank	#137	Overall
	#12	Search engines

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	0.74
Rank	#223	Overall
	#103	Relational DBMS
	#18	Time Series DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

aws.amazon.com/cloudsearch

Technical documentation

docs.aws.amazon.com/cloudsearch

impala.apache.org/impala-docs.html

www.mcobject.com/docs/extremedb.htm

dev.mysql.com/doc

pouchdb.com/guides

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

McObject

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2012

2013

2001

1995

2012

Current release

4.1.0, June 2022

8.2, 2021

8.4.0, April 2024

7.1.1, June 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C++

C and C++

JavaScript

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

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

Data scheme

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.

support of XML interfaces available

yes

Secondary indexes

yes

all search fields are automatically indexed

yes

via views

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with the option: eXtremeSQL

yes

with proprietary extensions

APIs and other access methods

HTTP API

JDBC
ODBC

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
Java
Lua
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

View functions in JavaScript

Triggers

yes

by defining events

yes

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

horizontal partitioning / sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

selectable replication factor

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

table locks or row locks depending on storage engine

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

authentication via encrypted signatures

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

IoT application across all markets: Industrial Control, Netcom, Telecom, Defense,...
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Key customers

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Market metrics

With hundreds of customers and over 30 million devices/applications using the product...
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Licensing and pricing models

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