DBMS > Cubrid vs. Drizzle vs. Google Cloud Bigtable vs. openGauss

System Properties Comparison Cubrid vs. Drizzle vs. Google Cloud Bigtable vs. openGauss

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Primary database model

Relational DBMS

Key-value store
Wide column store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Website

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

cloud.google.com/bigtable

gitee.com/opengauss
opengauss.org

Technical documentation

cubrid.org/manuals

cloud.google.com/bigtable/docs

docs.opengauss.org/en
gitee.com/opengauss/docs

Developer

CUBRID Corporation, CUBRID Foundation

Drizzle project, originally started by Brian Aker

Google

Huawei and openGauss community

Initial release

2008

2015

2019

Current release

11.0, January 2021

7.2.4, September 2012

3.0, March 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU GPL

commercial

Open Source

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

C++

C, C++, Java

Server operating systems

Linux
Windows

FreeBSD
Linux
OS X

hosted

Linux

Data scheme

yes

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

with proprietary extensions

ANSI SQL 2011

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JDBC

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

JDBC
ODBC

Supported programming languages

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

C
C++
Java
PHP

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

C
C++
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by range, list and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Transaction concepts

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

ACID

Atomic single-row operations

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
1 April 2015, Paul Andlinger

Has MySQL finally lost its mojo?
1 July 2013, Matthias Gelbmann

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