DBMS > Apache Cassandra vs. Dragonfly vs. KairosDB vs. RDF4J

System Properties Comparison Apache Cassandra vs. Dragonfly vs. KairosDB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Distributed Time Series DBMS based on Cassandra or H2

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Secondary database models

Vector DBMS

starting with release V5

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

Score	98.94
Rank	#11	Overall
	#1	Wide column stores

Score	0.44
Rank	#255	Overall
	#38	Key-value stores

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

cassandra.apache.org

github.com/dragonflydb/dragonfly
www.dragonflydb.io

github.com/kairosdb/kairosdb

rdf4j.org

Technical documentation

cassandra.apache.org/doc/latest

www.dragonflydb.io/docs

kairosdb.github.io

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

DragonflyDB team and community contributors

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2008

2023

2013

2004

Current release

5.0-rc1, July 2024

1.0, March 2023

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

BSL 1.1

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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++

Java

Server operating systems

BSD
Linux
OS X
Windows

Linux

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

scheme-free

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

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

restricted

only equality queries, not always the best performing solution

yes

SQL

Support of SQL

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

APIs and other access methods

Proprietary protocol

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

Proprietary protocol

RESP - REdis Serialization Protocol

Graphite protocol
HTTP REST
Telnet API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Java
JavaScript

Node.js
PHP
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

Triggers

yes

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

Atomic execution of command blocks and scripts

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Password-based authentication

simple password-based access control

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
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Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
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Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
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Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
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Market metrics

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

Licensing and pricing models

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