DBMS > Citus vs. HBase vs. JaguarDB vs. NCache vs. RDF4J

System Properties Comparison Citus vs. HBase vs. JaguarDB vs. NCache vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Wide-column store based on Apache Hadoop and on concepts of BigTable

Performant, highly scalable DBMS for AI and IoT applications

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

Relational DBMS

Wide column store

Key-value store
Vector DBMS

Key-value store

RDF store

Secondary database models

Document store

Document store
Search engine

Using distributed Lucene

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.citusdata.com

hbase.apache.org

www.jaguardb.com

www.alachisoft.com/ncache

rdf4j.org

Technical documentation

docs.citusdata.com

hbase.apache.org/book.html

www.jaguardb.com/support.html

www.alachisoft.com/resources/docs

rdf4j.org/documentation

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

DataJaguar, Inc.

Alachisoft

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

Initial release

2010

2008

2015

2005

2004

Current release

8.1, December 2018

2.3.4, January 2021

3.3 July 2023

5.3.3, April 2024

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache version 2

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

C#, .NET, .NET Core, Java

Java

Server operating systems

Linux

Linux
Unix
Windows

using Cygwin

Linux

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free, schema definition possible

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

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

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP API
Thrift

JDBC
ODBC

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

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
C#
C++
Groovy
Java
PHP
Python
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

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

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Coprocessors in Java

support for stored procedures with SQL-Server CLR

yes

Triggers

yes

Notifications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

Multi-source replication

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

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

optimistic locking and pessimistic locking

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

rights management via user accounts

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

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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

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