DBMS > Apache Kylin vs. EJDB vs. MonetDB vs. RavenDB vs. Snowflake

System Properties Comparison Apache Kylin vs. EJDB vs. MonetDB vs. RavenDB vs. Snowflake

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

A relational database management system that stores data in columns

Open Source Operational and Transactional Enterprise NoSQL Document Database

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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Trend Chart

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

kylin.apache.org

github.com/Softmotions/ejdb

www.monetdb.org

ravendb.net

www.snowflake.com

Technical documentation

kylin.apache.org/docs

github.com/Softmotions/ejdb/blob/master/README.md

www.monetdb.org/Documentation

ravendb.net/docs

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

Softmotions

MonetDB BV

Hibernating Rhinos

Snowflake Computing Inc.

Initial release

2015

2012

2004

2010

2014

Current release

3.1.0, July 2020

Dec2023 (11.49), December 2023

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2

Open Source

Mozilla Public License 2.0

Open Source

AGPL version 3, commercial license available

commercial

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

Server operating systems

Linux

server-less

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

hosted

Data scheme

yes

schema-free

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

ANSI SQL for queries (using Apache Calcite)

yes

SQL 2003 with some extensions

SQL-like query language (RQL)

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

in-process shared library

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes, in SQL, C, R

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write 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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

Users with fine-grained authorization concept, user roles and pluggable authentication

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