DBMS > Dragonfly vs. IRONdb vs. NCache vs. RDF4J

System Properties Comparison Dragonfly vs. IRONdb vs. NCache vs. RDF4J

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Open-Source and Enterprise in-memory Key-Value Store

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

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

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

www.circonus.com/solutions/time-series-database/

www.alachisoft.com/ncache

rdf4j.org

Technical documentation

www.dragonflydb.io/docs

docs.circonus.com/irondb/category/getting-started

www.alachisoft.com/resources/docs

rdf4j.org/documentation

Developer

DragonflyDB team and community contributors

Circonus LLC.

Alachisoft

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

Initial release

2023

2017

2005

2004

Current release

1.0, March 2023

V0.10.20, January 2018

5.3.3, April 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Enterprise Edition available

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.

Implementation language

C++

C and C++

C#, .NET, .NET Core, Java

Java

Server operating systems

Linux

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

scheme-free

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

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

yes

text, numeric, histograms

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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 query language (Circonus Analytics Query Language: CAQL)

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

Lua

yes, in Lua

support for stored procedures with SQL-Server CLR

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

configurable replication factor, datacenter aware

yes, with selectable consistency level

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 per node, eventual consistency across nodes

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of command blocks and scripts

optimistic locking and pessimistic locking

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

NCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
» more

Competitive advantages

NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
» more

Typical application scenarios

NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
» more

Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
» more

Market metrics

Market Leader in .NET Distributed Caching since 2005.
» more

Licensing and pricing models

NCache Open Source is free on an as-is basis without any support. NCache Enterprise...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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