DBMS > DataFS vs. Hawkular Metrics vs. HEAVY.AI

System Properties Comparison DataFS vs. Hawkular Metrics vs. HEAVY.AI

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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.

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Primary database model

Object oriented DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.07
Rank	#362	Overall
	#17	Object oriented DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Website

newdatabase.com

www.hawkular.org

github.com/heavyai/heavydb
www.heavy.ai

Technical documentation

dev.mobiland.com/Overview.xsp

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

docs.heavy.ai

Developer

Mobiland AG

Community supported by Red Hat

HEAVY.AI, Inc.

Initial release

2018

2014

2016

Current release

1.1.263, October 2022

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++ and CUDA

Server operating systems

Windows

Linux
OS X
Windows

Linux

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

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

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

HTTP REST

JDBC
ODBC
Thrift
Vega

Supported programming languages

.Net
C
C#
C++
VB.Net

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

based on Cassandra

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

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.

yes

User concepts

Access control

Windows-Profile

fine grained access rights according to SQL-standard

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