DBMS > Apache Kylin vs. atoti vs. Lovefield vs. MonetDB

System Properties Comparison Apache Kylin vs. atoti vs. Lovefield vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

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

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Embeddable relational database for web apps written in pure JavaScript

A relational database management system that stores data in columns

Primary database model

Relational DBMS

Object oriented DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

kylin.apache.org

atoti.io

google.github.io/lovefield

www.monetdb.org

Technical documentation

kylin.apache.org/docs

docs.atoti.io

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

www.monetdb.org/Documentation

Developer

Apache Software Foundation, originally contributed from eBay Inc

ActiveViam

Google

MonetDB BV

Initial release

2015

2014

2004

Current release

3.1.0, July 2020

2.1.12, February 2017

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free versions available

Open Source

Apache 2.0

Open Source

Mozilla Public License 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

Java

JavaScript

Server operating systems

Linux

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

tested with Chrome, Firefox, IE, Safari

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

ANSI SQL for queries (using Apache Calcite)

Multidimensional Expressions (MDX)

SQL-like query language

via JavaScript builder pattern

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

JavaScript

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

Server-side scripts

Stored procedures

Python

yes, in SQL, C, R

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

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

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

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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