DBMS > AlaSQL vs. Amazon DocumentDB vs. Amazon Redshift vs. Hawkular Metrics

System Properties Comparison AlaSQL vs. Amazon DocumentDB vs. Amazon Redshift vs. Hawkular Metrics

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Large scale data warehouse service for use with business intelligence tools

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Document store
Relational DBMS

Document store

Relational DBMS

Time Series DBMS

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Trend Chart

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

alasql.org

aws.amazon.com/documentdb

aws.amazon.com/redshift

www.hawkular.org

Technical documentation

github.com/AlaSQL/alasql

aws.amazon.com/documentdb/resources

docs.aws.amazon.com/redshift

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Andrey Gershun & Mathias R. Wulff

Amazon (based on PostgreSQL)

Community supported by Red Hat

Initial release

2014

2019

2012

2014

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache 2.0

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

Java

Server operating systems

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

hosted

Linux
OS X
Windows

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

yes

restricted

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

APIs and other access methods

JavaScript API

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

HTTP REST

Supported programming languages

JavaScript

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

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

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

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 rights for users and roles

fine grained access rights according to SQL-standard

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