DBMS > 4D vs. Amazon Neptune vs. Apache IoTDB vs. Apache Kylin vs. GigaSpaces

System Properties Comparison 4D vs. Amazon Neptune vs. Apache IoTDB vs. Apache Kylin vs. GigaSpaces

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Fast, reliable graph database built for the cloud

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

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Primary database model

Document store
Object oriented DBMS

Values are user defined objects

Secondary database models

Graph DBMS
Search engine

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Website

www.4d.com

aws.amazon.com/neptune

iotdb.apache.org

kylin.apache.org

www.gigaspaces.com

Technical documentation

developer.4d.com

aws.amazon.com/neptune/developer-resources

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

kylin.apache.org/docs

docs.gigaspaces.com/latest/landing.html

Developer

4D, Inc

Amazon

Apache Software Foundation

Apache Software Foundation, originally contributed from eBay Inc

Gigaspaces Technologies

Initial release

1984

2017

2018

2015

2000

Current release

v20, April 2023

1.1.0, April 2023

3.1.0, July 2020

15.5, September 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Java, C++, .Net

Server operating systems

OS X
Windows

hosted

All OS with a Java VM (>= 1.8)

Linux

Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

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.

yes

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

ANSI SQL for queries (using Apache Calcite)

SQL-99 for query and DML statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
Native API

JDBC
ODBC
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Supported programming languages

4D proprietary IDE
PHP

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

.Net
C++
Java
Python
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Foreign keys

Referential integrity

yes

Relationships 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

with encyption-at-rest

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 can be defined via the AWS Identity and Access Management (IAM)

yes

Role-based access control

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More resources

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