DBMS > Cubrid vs. FatDB vs. OrigoDB vs. Spark SQL

System Properties Comparison Cubrid vs. FatDB vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A fully ACID in-memory object graph database

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

Primary database model

Relational DBMS

Document store
Key-value store

Document store
Object oriented DBMS

Relational DBMS

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

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

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

origodb.com

spark.apache.org/sql

Technical documentation

cubrid.org/manuals

origodb.com/docs

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

Developer

CUBRID Corporation, CUBRID Foundation

FatCloud

Robert Friberg et al

Apache Software Foundation

Initial release

2008

2012

2009

under the name LiveDB

2014

Current release

11.0, January 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache 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

C, C++, Java

Scala

Server operating systems

Linux
Windows

Windows

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

Via inetgration in SQL Server

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

.NET Client API
HTTP API
LINQ

JDBC
ODBC

Supported programming languages

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

.Net

Java
Python
R
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

yes

via applications

yes

Triggers

yes

via applications

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

depending on model

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

Write ahead log

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Can implement custom security layer via applications

Role based authorization

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