DBMS > Apache Spark (SQL) vs. Cubrid

System Properties Comparison Apache Spark (SQL) vs. Cubrid

Please select another system to include it in the comparison.

Our visitors often compare Apache Spark (SQL) and Cubrid with PostgreSQL, MySQL and MongoDB.

Editorial information provided by DB-Engines

Name

Description

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

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Primary database model

Relational DBMS

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

Score	17.80
Rank	#31	Overall
	#19	Relational DBMS

Score	0.87
Rank	#201	Overall
	#94	Relational DBMS

Website

spark.apache.org/sql

cubrid.com (korean)
cubrid.org (english)

Technical documentation

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

cubrid.org/manuals

Developer

Apache Software Foundation

CUBRID Corporation, CUBRID Foundation

Initial release

2014

2008

Current release

3.5.0 ( 2.13), September 2023

11.0, January 2021

License

Commercial or Open Source

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

Scala

C, C++, Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

Data scheme

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

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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.

User concepts

Access control

fine grained access rights according to SQL-standard

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

18 Top Big Data Tools and Technologies to Know About in 2025
22 January 2025, TechTarget

Introducing generative AI troubleshooting for Apache Spark in AWS Glue (preview)
22 November 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

The 6 Best Apache Spark Courses on Udemy to Consider for 2025
1 January 2025, Solutions Review

provided by Google News

CUBRID: A FOSS RDBMS to Realise Your Next Big Idea
26 April 2018, Open Source For You

NHN Willing to Be More Open
24 November 2008, 코리아타임스

provided by Google News

Share this page