DBMS > 4D vs. Infobright vs. Spark SQL vs. Transbase

System Properties Comparison 4D vs. Infobright vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Spark SQL is a component on top of 'Spark Core' for structured data processing

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	1.00
Rank	#193	Overall
	#90	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

www.4d.com

ignitetech.com/softwarelibrary/infobrightdb

spark.apache.org/sql

www.transaction.de/en/products/transbase.html

Technical documentation

developer.4d.com

spark.apache.org/docs/latest/sql-programming-guide.html

www.transaction.de/en/products/transbase/features.html

Developer

4D, Inc

Ignite Technologies Inc.; formerly InfoBright Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

1984

2005

2014

1987

Current release

v20, April 2023

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

Apache 2.0

commercial

free development license

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

Scala

C and C++

Server operating systems

OS X
Windows

Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

close to SQL 92

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

ADO.NET
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

4D proprietary IDE
PHP

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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2 March 2016, Paul Andlinger

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