DBMS > 4D vs. Amazon Redshift vs. Apache Kylin vs. MonetDB vs. Riak KV

System Properties Comparison 4D vs. Amazon Redshift vs. Apache Kylin vs. MonetDB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

Large scale data warehouse service for use with business intelligence tools

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

A relational database management system that stores data in columns

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Spatial DBMS

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

Score	2.58
Rank	#108	Overall
	#54	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.4d.com

aws.amazon.com/redshift

kylin.apache.org

www.monetdb.org

Technical documentation

developer.4d.com

docs.aws.amazon.com/redshift

kylin.apache.org/docs

www.monetdb.org/Documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

4D, Inc

Amazon (based on PostgreSQL)

Apache Software Foundation, originally contributed from eBay Inc

MonetDB BV

OpenSource, formerly Basho Technologies

Initial release

1984

2012

2015

2004

2009

Current release

v20, April 2023

3.1.0, July 2020

Dec2023 (11.49), December 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Mozilla Public License 2.0

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Erlang

Server operating systems

OS X
Windows

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X

Data scheme

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

Secondary indexes

yes

restricted

yes

restricted

SQL

Support of SQL

yes

close to SQL 92

yes

does not fully support an SQL-standard

ANSI SQL for queries (using Apache Calcite)

yes

SQL 2003 with some extensions

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

4D proprietary IDE
PHP

All languages supporting JDBC/ODBC

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes, in SQL, C, R

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding via remote tables

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

none

Source-replica replication available in experimental status

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

links between data sets can be stored

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

yes, using Riak Security

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