DBMS > BaseX vs. CouchDB vs. Google BigQuery vs. MonetDB

System Properties Comparison BaseX vs. CouchDB vs. Google BigQuery vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

Large scale data warehouse service with append-only tables

A relational database management system that stores data in columns

Primary database model

Native XML DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Document store
Spatial DBMS

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

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

basex.org

couchdb.apache.org

cloud.google.com/bigquery

www.monetdb.org

Technical documentation

docs.basex.org

docs.couchdb.org/en/stable

cloud.google.com/bigquery/docs

www.monetdb.org/Documentation

Developer

BaseX GmbH

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Google

MonetDB BV

Initial release

2007

2005

2010

2004

Current release

11.0, June 2024

3.3.3, December 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache version 2

commercial

Open Source

Mozilla Public License 2.0

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

Linux
OS X
Windows

Android
BSD
Linux
OS X
Solaris
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

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

via views

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP/JSON API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

user defined functions

in JavaScript

yes, in SQL, C, R

Triggers

yes

via events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

improved architecture with release 2.0

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

atomic operations within a single document possible

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Users with fine-grained authorization concept on 4 levels

Access rights for users can be defined per database

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

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