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DBMS > RDF4J vs. Spark SQL

System Properties Comparison RDF4J vs. Spark SQL

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Our visitors often compare RDF4J and Spark SQL with Neo4j, Microsoft Azure Cosmos DB and GraphDB.

Editorial information provided by DB-Engines
NameRDF4J infoformerly known as Sesame  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRDF storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.37
Rank#223  Overall
#10  RDF stores
Score16.76
Rank#35  Overall
#21  Relational DBMS
Websiterdf4j.orgspark.apache.org/­sql
Technical documentationdocs.rdf4j.orgspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release20042014
Current releasev2.4.3, May 2019
License infoCommercial or Open SourceOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScala
Server operating systemsLinux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.no
Secondary indexesyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statements
APIs and other access methodsJava API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
ODBC
Supported programming languagesJava
PHP
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesno
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Foreign keys infoReferential integrityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoIsolation support depends on the API usedno
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.no
User concepts infoAccess controlnono

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More resources
RDF4J infoformerly known as SesameSpark SQL
Conferences and events

Spark Summit Europe 2019
Amsterdam, Netherlands, 15-17 October 2019

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