DBMS > MonetDB vs. RocksDB vs. Spark SQL vs. Transbase

System Properties Comparison MonetDB vs. RocksDB vs. Spark SQL vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

A relational database management system that stores data in columns

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Key-value store

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	#148	Overall
	#68	Relational DBMS

Score	4.00
Rank	#84	Overall
	#11	Key-value stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

www.monetdb.org

rocksdb.org

spark.apache.org/sql

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

Technical documentation

www.monetdb.org/Documentation

github.com/facebook/rocksdb/wiki

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

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

Developer

MonetDB BV

Facebook, Inc.

Apache Software Foundation

Transaction Software GmbH

Initial release

2004

2013

2014

1987

Current release

Dec2023 (11.49), December 2023

8.11.4, April 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Mozilla Public License 2.0

Open Source

BSD

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

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Implementation language

C++

Scala

C and C++

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

C
C++
Go
Java
Perl
Python
Ruby

Java
Python
R
Scala

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

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

yes

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

fine grained access rights according to SQL-standard

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