DBMS > Hawkular Metrics vs. HyperSQL vs. Infobright vs. Microsoft Azure SQL Database

System Properties Comparison Hawkular Metrics vs. HyperSQL vs. Infobright vs. Microsoft Azure SQL Database

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Editorial information provided by DB-Engines

Name

Description

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	3.90
Rank	#85	Overall
	#46	Relational DBMS

Score	1.00
Rank	#193	Overall
	#90	Relational DBMS

Score	78.40
Rank	#15	Overall
	#10	Relational DBMS

Website

www.hawkular.org

hsqldb.org

ignitetech.com/softwarelibrary/infobrightdb

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

Technical documentation

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

hsqldb.org/web/hsqlDocsFrame.html

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

Developer

Community supported by Red Hat

Ignite Technologies Inc.; formerly InfoBright Inc.

Microsoft

Initial release

2014

2001

2005

2010

Current release

2.7.2, June 2023

V12

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

based on BSD license

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

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++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
Windows

hosted

Data scheme

schema-free

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.

yes

Secondary indexes

yes

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

APIs and other access methods

HTTP REST

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
ODBC

Supported programming languages

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC
Java

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

Java, SQL

Transact SQL

Triggers

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Source-replica replication

yes, with always 3 replicas available

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

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

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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4 January 2021, Paul Andlinger, Matthias Gelbmann

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12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

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