DBMS > DataFS vs. Hawkular Metrics vs. Microsoft Azure SQL Database vs. STSdb

System Properties Comparison DataFS vs. Hawkular Metrics vs. Microsoft Azure SQL Database vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Graph DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

newdatabase.com

www.hawkular.org

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

github.com/STSSoft/STSdb4

Technical documentation

dev.mobiland.com/Overview.xsp

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

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

Developer

Mobiland AG

Community supported by Red Hat

Microsoft

STS Soft SC

Initial release

2018

2014

2010

2011

Current release

1.1.263, October 2022

V12

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPLv2, commercial license 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

Java

C++

Server operating systems

Windows

Linux
OS X
Windows

hosted

Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST

ADO.NET
JDBC
ODBC

.NET Client API

Supported programming languages

.Net
C
C#
C++
VB.Net

Go
Java
Python
Ruby

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

C#
Java

Server-side scripts

Stored procedures

Transact SQL

Triggers

no, except callback-events from server when changes happened

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes, with always 3 replicas available

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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.

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

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More resources

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12 December 2019, Paul Andlinger

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7 February 2017, Matthias Gelbmann

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