DBMS > 4D vs. Amazon Redshift vs. FoundationDB vs. Spark SQL

System Properties Comparison 4D vs. Amazon Redshift vs. FoundationDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

Application development environment with integrated database management system

Large scale data warehouse service for use with business intelligence tools

Ordered key-value store. Core features are complimented by layers.

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

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

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

Score	2.68
Rank	#110	Overall
	#54	Relational DBMS

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.07
Rank	#188	Overall
	#31	Document stores
	#28	Key-value stores
	#86	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.4d.com

aws.amazon.com/redshift

github.com/apple/foundationdb

spark.apache.org/sql

Technical documentation

developer.4d.com

docs.aws.amazon.com/redshift

apple.github.io/foundationdb

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

Developer

4D, Inc

Amazon (based on PostgreSQL)

FoundationDB

Apache Software Foundation

Initial release

1984

2012

2013

2014

Current release

v20, April 2023

6.2.28, November 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

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

C++

Scala

Server operating systems

OS X
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

yes

some layers support typing

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

restricted

SQL

Support of SQL

yes

close to SQL 92

yes

does not fully support an SQL-standard

supported in specific SQL layer only

SQL-like DML and DDL statements

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in Python

in SQL-layer only

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

in SQL-layer only

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

fine grained access rights according to SQL-standard

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