DBMS > Oracle NoSQL vs. Spark SQL vs. STSdb vs. SWC-DB

System Properties Comparison Oracle NoSQL vs. Spark SQL vs. STSdb vs. SWC-DB

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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

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Key-value store
Relational DBMS

Relational DBMS

Key-value store

Wide column store

Secondary database models

Time Series 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	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

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

spark.apache.org/sql

github.com/STSSoft/STSdb4

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

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

Developer

Oracle

Apache Software Foundation

STS Soft SC

Alex Kashirin

Initial release

2011

2014

2011

2020

Current release

23.3, December 2023

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

0.5, April 2021

License

Commercial or Open Source

Open Source

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

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

Scala

C++

Server operating systems

Linux
Solaris SPARC/x86

Linux
OS X
Windows

Windows

Linux

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

optional

yes

primitive types and user defined types (classes)

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

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

.NET Client API

Proprietary protocol
Thrift

Supported programming languages

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

Java
Python
R
Scala

C#
Java

C++

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Sharding

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

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

Foreign keys

Referential integrity

Transaction concepts

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

configurable

ACID within a storage node (=shard)

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

User concepts

Access control

Access rights for users and roles

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