DBMS > MonetDB vs. OpenEdge vs. Oracle Rdb vs. Spark SQL

System Properties Comparison MonetDB vs. OpenEdge vs. Oracle Rdb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A relational database management system that stores data in columns

Application development environment with integrated database management system

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

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.monetdb.org

www.progress.com/openedge

www.oracle.com/database/technologies/related/rdb.html

spark.apache.org/sql

Technical documentation

www.monetdb.org/Documentation

documentation.progress.com/output/ua/OpenEdge_latest

www.oracle.com/database/technologies/related/rdb-doc.html

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

Developer

MonetDB BV

Progress Software Corporation

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

Initial release

2004

1984

2014

Current release

Dec2023 (11.49), December 2023

OpenEdge 12.2, March 2020

7.4.1.1, 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Mozilla Public License 2.0

commercial

Open Source

Apache 2.0

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

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

HP Open VMS

Linux
OS X
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

yes

close to SQL 92

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

Supported programming languages

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

Progress proprietary ABL (Advanced Business Language)

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes, in SQL, C, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding via remote tables

horizontal partitioning

since Version 11.4

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Source-replica replication

none

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, on a single node

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Users and groups

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More resources

Recent citations in the news

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