DBMS > Cassandra vs. Kdb vs. LevelDB vs. MonetDB

System Properties Comparison Cassandra vs. Kdb vs. LevelDB vs. MonetDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

High performance Time Series DBMS

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

A relational database management system that stores data in columns

Primary database model

Wide column store

Time Series DBMS
Vector DBMS

Key-value store

Relational DBMS

Secondary database models

Vector DBMS

starting with release V5

Relational DBMS

Document store
Spatial DBMS

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

Score	98.83
Rank	#12	Overall
	#1	Wide column stores

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

cassandra.apache.org

kx.com

github.com/google/leveldb

www.monetdb.org

Technical documentation

cassandra.apache.org/doc/latest

code.kx.com

github.com/google/leveldb/blob/main/doc/index.md

www.monetdb.org/Documentation

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Kx Systems, a division of First Derivatives plc

Google

MonetDB BV

Initial release

2008

2000

kdb was released 2000, kdb+ in 2003

2011

2004

Current release

4.1.3, July 2023

3.6, May 2018

1.23, February 2021

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free 32-bit version

Open Source

BSD

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.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Illumos
Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

schema-free

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.

yes

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

table attribute 'grouped'

yes

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like query language (q)

yes

SQL 2003 with some extensions

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

user defined functions

yes, in SQL, C, R

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per object

rights management via user accounts

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
» more

upon request
» more

News

An Introduction to David Jones-Gilardi, Developer Relations
11 June 2024

Introducing Tejas Kumar, Developer Relations Engineer
7 June 2024

Indexing All of Wikipedia on a Laptop
5 June 2024

An Introduction to Phil Nash, Developer Relations
3 June 2024

Focus on Creativity, Not Clusters: DataStax Mission Control in Action!
30 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.