DBMS > 4D vs. DataFS vs. GeoMesa vs. HEAVY.AI

System Properties Comparison 4D vs. DataFS vs. GeoMesa vs. HEAVY.AI

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

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.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Primary database model

Secondary database models

Graph DBMS

Spatial DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Website

www.4d.com

newdatabase.com

www.geomesa.org

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

Technical documentation

developer.4d.com

dev.mobiland.com/Overview.xsp

www.geomesa.org/documentation/stable/user/index.html

docs.heavy.ai

Developer

4D, Inc

Mobiland AG

CCRi and others

HEAVY.AI, Inc.

Initial release

1984

2018

2014

2016

Current release

v20, April 2023

1.1.263, October 2022

5.0.0, May 2024

5.10, January 2022

License

Commercial or Open Source

commercial

Open Source

Apache License 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

Scala

C++ and CUDA

Server operating systems

OS X
Windows

Windows

Linux

Data scheme

yes

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

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

SQL

Support of SQL

yes

close to SQL 92

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

.NET Client API
Proprietary client DLL
WinRT client

JDBC
ODBC
Thrift
Vega

Supported programming languages

4D proprietary IDE
PHP

.Net
C
C#
C++
VB.Net

All languages supporting JDBC/ODBC/Thrift
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

depending on storage layer

Sharding

Round robin

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

depending on storage layer

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

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.

depending on storage layer

yes

User concepts

Access control

Users and groups

Windows-Profile

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

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More resources

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Recent citations in the news

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

HEAVY.AI Partners with Bain, Maxar, and Nvidia to Provide Digital Twins for Telecom Networks
16 February 2023, Datanami

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14 April 2023, InfoWorld

The insideBIGDATA IMPACT 50 List for Q4 2023
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provided by Google News

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