DBMS > Cubrid vs. DataFS vs. Google Cloud Bigtable vs. Google Cloud Spanner

System Properties Comparison Cubrid vs. DataFS vs. Google Cloud Bigtable vs. Google Cloud Spanner

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Editorial information provided by DB-Engines

Name

Description

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store
Wide column store

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.97
Rank	#189	Overall
	#86	Relational DBMS

Score	0.02
Rank	#369	Overall
	#18	Object oriented DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Website

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

newdatabase.com

cloud.google.com/bigtable

cloud.google.com/spanner

Technical documentation

cubrid.org/manuals

dev.mobiland.com/Overview.xsp

cloud.google.com/bigtable/docs

cloud.google.com/spanner/docs

Developer

CUBRID Corporation, CUBRID Foundation

Mobiland AG

Google

Initial release

2008

2018

2015

2017

Current release

11.0, January 2021

1.1.263, October 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Java

Server operating systems

Linux
Windows

Windows

hosted

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

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

yes

Query statements complying to ANSI 2011

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
Proprietary client DLL
WinRT client

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

Supported programming languages

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

.Net
C
C#
C++
VB.Net

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Triggers

yes

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication with 3 replicas for regional instances.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

Transaction concepts

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

ACID

Atomic single-row operations

ACID

Strict serializable isolation

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

Windows-Profile

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources

Recent citations in the 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

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