DBMS > Apache Kylin vs. Dragonfly vs. RDF4J

System Properties Comparison Apache Kylin vs. Dragonfly vs. RDF4J

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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

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

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

Primary database model

Relational DBMS

Key-value store

RDF store

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

Score	1.54
Rank	#174	Overall
	#83	Relational DBMS

Score	0.14
Rank	#365	Overall
	#57	Key-value stores

Score	1.06
Rank	#214	Overall
	#9	RDF stores

Website

kylin.apache.org

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

rdf4j.org

Technical documentation

kylin.apache.org/docs

www.dragonflydb.io/docs

rdf4j.org/documentation

Developer

Apache Software Foundation, originally contributed from eBay Inc

DragonflyDB team and community contributors

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

Initial release

2015

2023

2004

Current release

3.1.0, July 2020

1.0, March 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSL 1.1

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

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Implementation language

Java

C++

Java

Server operating systems

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

scheme-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

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protocol

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

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
PHP
Python

Server-side scripts

Stored procedures

Lua

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

Password-based authentication

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