DBMS > Drizzle vs. Hive vs. openGauss

System Properties Comparison Drizzle vs. Hive 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

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Website

hive.apache.org

gitee.com/opengauss
opengauss.org

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

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

Developer

Drizzle project, originally started by Brian Aker

Apache Software Foundation

initially developed by Facebook

Huawei and openGauss community

Initial release

2008

2012

2019

Current release

7.2.4, September 2012

3.1.3, April 2022

3.0, March 2022

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2

Open Source

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

C++

Java

C, C++, Java

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

Linux

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

yes

with proprietary extensions

SQL-like DML and DDL statements

ANSI SQL 2011

APIs and other access methods

JDBC

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

C++
Java
PHP
Python

C
C++
Java

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles

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

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More resources

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