DBMS > Apache Spark (SQL) vs. RDF4J

System Properties Comparison Apache Spark (SQL) vs. RDF4J

Please select another system to include it in the comparison.

Our visitors often compare Apache Spark (SQL) and RDF4J with Neo4j, GraphDB and PostgreSQL.

Editorial information provided by DB-Engines

Name

Description

Apache Spark SQL is a component on top of 'Spark Core' for structured data processing

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

Primary database model

Relational DBMS

RDF store

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

Score	17.80
Rank	#31	Overall
	#19	Relational DBMS

Score	0.83
Rank	#202	Overall
	#7	RDF stores

Website

spark.apache.org/sql

rdf4j.org

Technical documentation

spark.apache.org/docs/latest/sql-programming-guide.html

rdf4j.org/documentation

Developer

Apache Software Foundation

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

Initial release

2014

2004

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

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

Scala

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Java
Python
R
Scala

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Foreign keys

Referential integrity

Transaction concepts

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

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.

User concepts

Access control

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

SALE ENDS IN
14 January 2025, packtpub.com

Amazon EMR 7.5 runtime for Apache Spark and Iceberg can run Spark workloads 3.6 times faster than Spark 3.5.3 and Iceberg 1.6.1 | Amazon Web Services
27 December 2024, AWS Blog

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2025
12 November 2024, Simplilearn

Deep Dive Into Historical Usage-Based Scheduling with Snowpark
22 August 2024, Snowflake

Sparkle: Standardizing Modular ETL at Uber
15 August 2024, Uber

provided by Google News

Amazon Neptune review: A scalable graph database for OLTP
13 May 2019, InfoWorld

provided by Google News

Share this page