DBMS > Apache Spark (SQL) vs. RDFox vs. Trino

System Properties Comparison Apache Spark (SQL) vs. RDFox vs. Trino

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Editorial information provided by DB-Engines

Name

Description

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

High performance knowledge graph and semantic reasoning engine

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Relational DBMS

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	20.40
Rank	#29	Overall
	#18	Relational DBMS

Score	0.23
Rank	#296	Overall
	#24	Graph DBMS
	#12	RDF stores

Score	5.18
Rank	#60	Overall
	#34	Relational DBMS

Website

spark.apache.org/sql

www.oxfordsemantic.tech

trino.io

Technical documentation

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

docs.oxfordsemantic.tech

trino.io/broadcast
trino.io/docs/current

Developer

Apache Software Foundation

Oxford Semantic Technologies

Trino Software Foundation

Initial release

2014

2017

2012

2020 rebranded from PrestoSQL

Current release

3.5.0 ( 2.13), September 2023

6.0, Septermber 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
macOS
Windows

Linux
macOS

for devlopment

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

depending on connected data-source

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API
SPARQL 1.1

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

Java
Python
R
Scala

C
Java

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes, depending on connected data-source

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

none

replication via a shared file system

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

depending on connected data-source

Foreign keys

Referential integrity

Transaction concepts

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

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

depending on connected data-source

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Roles, resources, and access types

SQL standard access control

More information provided by the system vendor

News

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