DBMS > HBase vs. Hive vs. MySQL vs. Oracle

System Properties Comparison HBase vs. Hive vs. MySQL vs. Oracle

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

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

Widely used open source RDBMS

Widely used RDBMS

Primary database model

Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

hbase.apache.org

hive.apache.org

www.mysql.com

www.oracle.com/database

Technical documentation

hbase.apache.org/book.html

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

dev.mysql.com/doc

docs.oracle.com/en/database

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

initially developed by Facebook

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

Initial release

2008

2012

1995

1980

Current release

2.3.4, January 2021

3.1.3, April 2022

8.3.0, January 2024

23c, September 2023

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free, schema definition possible

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

options to bring your own types, AVRO

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
PHP
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

user defined functions and integration of map-reduce

yes

proprietary syntax

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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.

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users, groups and roles

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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