DBMS > Oracle NoSQL vs. Spark SQL vs. StarRocks

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

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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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Document store
Key-value store
Relational DBMS

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	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Website

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

spark.apache.org/sql

www.starrocks.io

Technical documentation

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

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

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Oracle

Apache Software Foundation

The Linux Foundation

since Feb 2023

Initial release

2011

2014

2020

Current release

23.3, December 2023

3.5.0 ( 2.13), September 2023

2.5.3, March 2023

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

Apache Version 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

Scala

C++, Java

Server operating systems

Linux
Solaris SPARC/x86

Linux
OS X
Windows

Linux

Data scheme

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

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

yes

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

JDBC
MySQL protocol

Supported programming languages

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

Java
Python
R
Scala

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

horizontal partitioning (by range and hash)

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

Role based access control and fine grained access rights

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