DBMS > Apache Kylin vs. GBase vs. MonetDB vs. NSDb vs. Oracle NoSQL

System Properties Comparison Apache Kylin vs. GBase vs. MonetDB vs. NSDb vs. Oracle NoSQL

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

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A relational database management system that stores data in columns

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Relational DBMS

Time Series DBMS

Document store
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	1.18
Rank	#172	Overall
	#79	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

kylin.apache.org/docs

www.monetdb.org/Documentation

nsdb.io/Architecture

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

General Data Technology Co., Ltd.

MonetDB BV

Oracle

Initial release

2015

2004

2017

2011

Current release

3.1.0, July 2020

GBase 8a, GBase 8s, GBase 8c

Dec2023 (11.49), December 2023

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla Public License 2.0

Open Source

Apache Version 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

C, Java, Python

Java, Scala

Java

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Linux
Solaris SPARC/x86

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

optional

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

all fields are automatically indexed

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

Standard with numerous extensions

yes

SQL 2003 with some extensions

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Supported programming languages

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

Java
Scala

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding via remote tables

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Access rights for users and roles

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