DBMS > 4D vs. FatDB vs. GridGain vs. JaguarDB vs. Quasardb

System Properties Comparison 4D vs. FatDB vs. GridGain vs. JaguarDB vs. Quasardb

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

Application development environment with integrated database management system

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Performant, highly scalable DBMS for AI and IoT applications

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store
Key-value store

Key-value store
Relational DBMS

Key-value store
Vector DBMS

Time Series DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

Technical documentation

developer.4d.com

www.gridgain.com/docs/index.html

www.jaguardb.com/support.html

doc.quasar.ai/master

Developer

4D, Inc

FatCloud

GridGain Systems, Inc.

DataJaguar, Inc.

quasardb

Initial release

1984

2012

2007

2015

2009

Current release

v20, April 2023

GridGain 8.5.1

3.3 July 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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, C++, .Net

C++

the server part. Clients available in other languages

C++

Server operating systems

OS X
Windows

Windows

Linux
OS X
Solaris
Windows

Linux

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

SQL

Support of SQL

yes

close to SQL 92

Via inetgration in SQL Server

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

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

JDBC
ODBC

HTTP API

Supported programming languages

4D proprietary IDE
PHP

C#
C++
Java
PHP
Python
Ruby
Scala

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

via applications

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

Triggers

yes

via applications

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

yes (replicated cache)

Multi-source replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

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

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Users and groups

Can implement custom security layer via applications

Security Hooks for custom implementations

rights management via user accounts

Cryptographically strong user authentication and audit trail

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More resources

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