DBMS > Kinetica vs. MonetDB vs. RocksDB vs. SAP HANA

System Properties Comparison Kinetica vs. MonetDB vs. RocksDB vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

A relational database management system that stores data in columns

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

www.kinetica.com

www.monetdb.org

rocksdb.org

www.sap.com/products/hana.html

Technical documentation

docs.kinetica.com

www.monetdb.org/Documentation

github.com/facebook/rocksdb/wiki

help.sap.com/hana

Developer

Kinetica

MonetDB BV

Facebook, Inc.

SAP

Initial release

2012

2004

2013

2010

Current release

7.1, August 2021

Dec2023 (11.49), December 2023

9.4.0, June 2024

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Mozilla Public License 2.0

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

C++

Server operating systems

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Appliance or cloud-service

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

yes

SQL 2003 with some extensions

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

JDBC
ODBC

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

SQLScript, R

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding via remote tables

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Source-replica replication available in experimental status

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

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.

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

yes

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