DBMS > MarkLogic vs. Memcached vs. RDF4J

System Properties Comparison MarkLogic vs. Memcached vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Operational and transactional Enterprise NoSQL database

In-memory key-value store, originally intended for caching

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

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

RDF store

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

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	19.42
Rank	#32	Overall
	#4	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.marklogic.com

www.memcached.org

rdf4j.org

Technical documentation

docs.marklogic.com

github.com/memcached/memcached/wiki

rdf4j.org/documentation

Developer

MarkLogic Corp.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

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

Initial release

2001

2003

2004

Current release

11.0, December 2022

1.6.25, March 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

BSD license

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++

Java

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

Schema can be enforced

schema-free

yes

RDF Schemas

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

yes

SQL

Support of SQL

yes

SQL92

APIs and other access methods

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

Java
PHP
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Repcached, a Memcached patch, provides this functionallity

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

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, with Range Indexes

User concepts

Access control

Role-based access control at the document and subdocument levels

yes

using SASL (Simple Authentication and Security Layer) protocol

More information provided by the system vendor

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Related products and services

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More resources

DB-Engines blog posts

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3 February 2014, Matthias Gelbmann

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3 October 2012, Matthias Gelbmann, Paul Andlinger

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