DBMS > Amazon Redshift vs. Hawkular Metrics vs. Microsoft Azure SQL Database vs. OpenTSDB vs. Solr

System Properties Comparison Amazon Redshift vs. Hawkular Metrics vs. Microsoft Azure SQL Database vs. OpenTSDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

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.

Database as a Service offering with high compatibility to Microsoft SQL Server

Scalable Time Series DBMS based on HBase

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	1.68
Rank	#142	Overall
	#12	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

www.hawkular.org

azure.microsoft.com/en-us/products/azure-sql/database

opentsdb.net

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

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

docs.microsoft.com/en-us/azure/azure-sql

opentsdb.net/docs/build/html/index.html

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Community supported by Red Hat

Microsoft

currently maintained by Yahoo and other contributors

Apache Software Foundation

Initial release

2012

2014

2010

2011

2006

Current release

V12

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

LGPL

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

Java

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

numeric data for metrics, strings for tags

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

HTTP REST

ADO.NET
JDBC
ODBC

HTTP API
Telnet API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Go
Java
Python
Ruby

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Erlang
Go
Java
Python
R
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Transact SQL

Java plugins

Triggers

yes

via Hawkular Alerting

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

based on HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

yes, with always 3 replicas available

selectable replication factor

based on HBase

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

optimistic locking

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

fine grained access rights according to SQL-standard

yes

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.