DBMS > HyperSQL vs. RDF4J vs. Sphinx vs. Yaacomo

System Properties Comparison HyperSQL vs. RDF4J vs. Sphinx vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

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

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Website

hsqldb.org

rdf4j.org

sphinxsearch.com

yaacomo.com

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

rdf4j.org/documentation

sphinxsearch.com/docs

Developer

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

Sphinx Technologies Inc.

Q2WEB GmbH

Initial release

2001

2004

2001

2009

Current release

2.7.2, June 2023

3.5.1, February 2023

License

Commercial or Open Source

Open Source

based on BSD license

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL version 2, commercial licence available

commercial

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++

Server operating systems

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Android
Linux
Windows

Data scheme

yes

RDF Schemas

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.

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language (SphinxQL)

yes

APIs and other access methods

HTTP API

JDBC via HTTP
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Proprietary protocol

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC
Java

Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Java, SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

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

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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