DBMS > DataFS vs. Graph Engine vs. HEAVY.AI vs. Oracle Rdb

System Properties Comparison DataFS vs. Graph Engine vs. HEAVY.AI vs. Oracle Rdb

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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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

newdatabase.com

www.graphengine.io

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

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

dev.mobiland.com/Overview.xsp

www.graphengine.io/docs/manual

docs.heavy.ai

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Mobiland AG

Microsoft

HEAVY.AI, Inc.

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2018

2010

2016

1984

Current release

1.1.263, October 2022

5.10, January 2022

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache Version 2; enterprise edition available

commercial

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

.NET and C

C++ and CUDA

Server operating systems

Windows

.NET

Linux

HP Open VMS

Data scheme

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

yes

Flexible Schema (defined schema, partial schema, 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

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

JDBC
ODBC
Thrift
Vega

Supported programming languages

.Net
C
C#
C++
VB.Net

C#
C++
F#
Visual Basic

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

yes

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

horizontal partitioning

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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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