DBMS > AlaSQL vs. Amazon Redshift vs. Apache Impala vs. Ehcache

System Properties Comparison AlaSQL vs. Amazon Redshift vs. Apache Impala vs. Ehcache

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

A widely adopted Java cache with tiered storage options

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Document store

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Website

alasql.org

aws.amazon.com/redshift

impala.apache.org

www.ehcache.org

Technical documentation

github.com/AlaSQL/alasql

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

www.ehcache.org/documentation

Developer

Andrey Gershun & Mathias R. Wulff

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Terracotta Inc, owned by Software AG

Initial release

2014

2012

2013

2009

Current release

4.1.0, June 2022

3.10.0, March 2022

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available

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

JavaScript

C++

Java

Server operating systems

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

hosted

Linux

All OS with a Java VM

Data scheme

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

JDBC
ODBC

JCache

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC

Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor

yes

by using Terracotta Server

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

none

Immediate Consistency

Eventual Consistency

Tunable Consistency (Strong, Eventual, Weak)

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

yes

only for local storage and DOM-storage

ACID

yes

supports JTA and can work as an XA resource

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

using a tiered cache-storage approach

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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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