DBMS > 4D vs. AnzoGraph DB vs. Apache IoTDB

System Properties Comparison 4D vs. AnzoGraph DB vs. Apache IoTDB

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

Primary database model

Relational DBMS

Graph DBMS
RDF store

Time Series DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	0.27
Rank	#302	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Website

www.4d.com

cambridgesemantics.com/anzograph

iotdb.apache.org

Technical documentation

developer.4d.com

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

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

Developer

4D, Inc

Cambridge Semantics

Apache Software Foundation

Initial release

1984

2018

Current release

v20, April 2023

2.3, January 2021

1.1.0, April 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache Version 2.0

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

Server operating systems

OS X
Windows

Linux

All OS with a Java VM (>= 1.8)

Data scheme

yes

Schema-free and OWL/RDFS-schema support

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

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
Native API

Supported programming languages

4D proprietary IDE
PHP

C++
Java
Python

C
C#
C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

yes

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication in MPP-Cluster

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

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Eventual Consistency
Strong Consistency with Raft

Foreign keys

Referential integrity

yes

not needed in graphs

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

Users and groups

Access rights for users and roles

yes

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