DBMS > Amazon CloudSearch vs. Amazon Redshift vs. Apache Impala

System Properties Comparison Amazon CloudSearch vs. Amazon Redshift vs. Apache Impala

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Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

Primary database model

Search engine

Relational DBMS

Secondary database models

Document store

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

Score	1.87
Rank	#140	Overall
	#12	Search engines

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	14.03
Rank	#40	Overall
	#24	Relational DBMS

Website

aws.amazon.com/cloudsearch

aws.amazon.com/redshift

impala.apache.org

Technical documentation

docs.aws.amazon.com/cloudsearch

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

Developer

Amazon

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Initial release

2012

2013

Current release

4.1.0, June 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Linux

Data scheme

yes

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.

Secondary indexes

yes

all search fields are automatically indexed

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes

automatic partitioning across Amazon Search Instance as required

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

managed transparently by AWS

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

authentication via encrypted signatures

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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