DBMS > Oracle NoSQL vs. RDFox vs. Spark SQL

System Properties Comparison Oracle NoSQL vs. RDFox vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

High performance knowledge graph and semantic reasoning engine

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

Primary database model

Document store
Key-value store
Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Relational DBMS

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

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.oracle.com/database/nosql/technologies/nosql

www.oxfordsemantic.tech

spark.apache.org/sql

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.oxfordsemantic.tech

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

Developer

Oracle

Oxford Semantic Technologies

Apache Software Foundation

Initial release

2011

2017

2014

Current release

23.3, December 2023

6.0, Septermber 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

Open Source

Apache 2.0

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

Java

C++

Scala

Server operating systems

Linux
Solaris SPARC/x86

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

RDF schemas

yes

Typing

predefined data types such as float or date

optional

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

RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

JDBC
ODBC

Supported programming languages

C
C#
Go
Java
JavaScript (Node.js)
Python

C
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

replication via a shared file system

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

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

Foreign keys

Referential integrity

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

off heap cache

yes

User concepts

Access control

Access rights for users and roles

Roles, resources, and access types

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