DBMS > 4D vs. EsgynDB vs. Greenplum vs. GridDB vs. GridGain

System Properties Comparison 4D vs. EsgynDB vs. Greenplum vs. GridDB vs. GridGain

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Scalable in-memory time series database optimized for IoT and Big Data

GridGain is an in-memory computing platform, built on Apache Ignite

Primary database model

Relational DBMS

Time Series DBMS

Key-value store
Relational DBMS

Secondary database models

Document store
Spatial DBMS

Key-value store
Relational DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Website

Technical documentation

developer.4d.com

docs.greenplum.org

docs.griddb.net

www.gridgain.com/docs/index.html

Developer

4D, Inc

Esgyn

Pivotal Software Inc.

Toshiba Corporation

GridGain Systems, Inc.

Initial release

1984

2015

2005

2013

2007

Current release

v20, April 2023

7.0.0, September 2023

5.1, August 2022

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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

C++, Java

C++

Java, C++, .Net

Server operating systems

OS X
Windows

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

since Version 4.2

yes

Secondary indexes

yes

SQL

Support of SQL

yes

close to SQL 92

yes

SQL92, SQL-like TQL (Toshiba Query Language)

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

ADO.NET
JDBC
ODBC

JDBC
ODBC

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Multi-source replication between multi datacenters

Source-replica replication

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID at container level

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

fine grained access rights according to SQL-standard

Access rights for users can be defined per database

Security Hooks for custom implementations

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

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2 March 2016, Paul Andlinger

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