DBMS > MarkLogic vs. RDF4J vs. Sphinx vs. Teradata Aster vs. VoltDB

System Properties Comparison MarkLogic vs. RDF4J vs. Sphinx vs. Teradata Aster vs. VoltDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Operational and transactional Enterprise NoSQL database

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

Open source search engine for searching in data from different sources, e.g. relational databases

Platform for big data analytics on multistructured data sources and types

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

RDF store

Search engine

Relational DBMS

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

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	1.47
Rank	#157	Overall
	#73	Relational DBMS

Website

Technical documentation

docs.marklogic.com

rdf4j.org/documentation

sphinxsearch.com/docs

docs.voltdb.com

Developer

MarkLogic Corp.

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

Sphinx Technologies Inc.

Teradata

VoltDB Inc.

Initial release

2001

2004

2001

2005

2010

Current release

11.0, December 2022

3.5.1, February 2023

11.3, April 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL version 2, commercial licence available

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++

Java, C++

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Linux
OS X

for development

Data scheme

schema-free

Schema can be enforced

yes

RDF Schemas

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

yes

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

in Aster File Store

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL92

SQL-like query language (SphinxQL)

yes

only a subset of SQL 99

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Proprietary protocol

ADO.NET
JDBC
ODBC
OLE DB

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

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

Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C
C#
C++
Java
Python
R

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

yes

R packages

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

in-memory storage is supported as well

yes

The original contents of fields are not stored in the Sphinx index.

yes

Snapshots and command logging

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

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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More resources

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