DBMS > Hive vs. Lovefield vs. MonetDB vs. SWC-DB

System Properties Comparison Hive vs. Lovefield vs. MonetDB vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Embeddable relational database for web apps written in pure JavaScript

A relational database management system that stores data in columns

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Wide column store

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

hive.apache.org

google.github.io/lovefield

www.monetdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

github.com/google/lovefield/blob/master/docs/spec_index.md

www.monetdb.org/Documentation

Developer

Apache Software Foundation

initially developed by Facebook

Google

MonetDB BV

Alex Kashirin

Initial release

2012

2014

2004

2020

Current release

3.1.3, April 2022

2.1.12, February 2017

Dec2023 (11.49), December 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

Open Source

GPL V3

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

Java

JavaScript

C++

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

yes

SQL 2003 with some extensions

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Proprietary protocol
Thrift

Supported programming languages

C++
Java
PHP
Python

JavaScript

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

C++

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes, in SQL, C, R

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

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