DBMS > 4D vs. Apache IoTDB vs. DataFS vs. PostgreSQL vs. TerminusDB

System Properties Comparison 4D vs. Apache IoTDB vs. DataFS vs. PostgreSQL vs. TerminusDB

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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.

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Graph DBMS

Secondary database models

Graph DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Document store
RDF store

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Website

Technical documentation

developer.4d.com

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

dev.mobiland.com/Overview.xsp

www.postgresql.org/docs

terminusdb.github.io/terminusdb/#

Developer

4D, Inc

Apache Software Foundation

Mobiland AG

PostgreSQL Global Development Group

www.postgresql.org/developer

DataChemist Ltd.

Initial release

1984

2018

1989

1989: Postgres, 1996: PostgreSQL

2018

Current release

v20, April 2023

1.1.0, April 2023

1.1.263, October 2022

16.3, May 2024

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

BSD

Open Source

GPL V3

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.

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Implementation language

Java

Prolog, Rust

Server operating systems

OS X
Windows

All OS with a Java VM (>= 1.8)

Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

yes

standard with numerous extensions

SQL-like query language (WOQL)

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
Native API

.NET Client API
Proprietary client DLL
WinRT client

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

4D proprietary IDE
PHP

C
C#
C++
Go
Java
Python
Scala

.Net
C
C#
C++
VB.Net

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript
Python

Server-side scripts

Stored procedures

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Triggers

yes

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Proprietary Sharding system

partitioning by range, list and (since PostgreSQL 11) by hash

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Source-replica replication

other methods possible by using 3rd party extensions

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

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 journaling

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Users and groups

yes

Windows-Profile

fine grained access rights according to SQL-standard

Role-based access control

More information provided by the system vendor

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