DBMS > Amazon DocumentDB vs. Amazon Redshift vs. Hawkular Metrics vs. Warp 10

System Properties Comparison Amazon DocumentDB vs. Amazon Redshift vs. Hawkular Metrics vs. Warp 10

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Editorial information provided by DB-Engines

Name

Description

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.

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store

Relational DBMS

Time Series DBMS

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.06
Rank	#351	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/documentdb

aws.amazon.com/redshift

www.hawkular.org

www.warp10.io

Technical documentation

aws.amazon.com/documentdb/resources

docs.aws.amazon.com/redshift

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

www.warp10.io/content/02_Getting_started

Developer

Amazon (based on PostgreSQL)

Community supported by Red Hat

SenX

Initial release

2019

2012

2014

2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache License 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

Java

Server operating systems

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

yes

does not fully support an SQL-standard

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC

HTTP REST

HTTP API
Jupyter
WebSocket

Supported programming languages

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

yes

WarpScript

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

selectable replication factor

based on Cassandra

selectable replication factor

based on HBase

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

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

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

Atomic single-document operations

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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