DBMS > HBase vs. Hive vs. Impala

System Properties Comparison HBase vs. Hive vs. Impala

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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

Analytic DBMS for Hadoop

Primary database model

Wide column store

Relational DBMS

Secondary database models

Document store

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

Score	42.45
Rank	#26	Overall
	#2	Wide column stores

Score	78.66
Rank	#17	Overall
	#11	Relational DBMS

Score	17.56
Rank	#40	Overall
	#25	Relational DBMS

Website

hbase.apache.org

hive.apache.org

www.cloudera.com/products/open-source/apache-hadoop/impala.html

Technical documentation

hbase.apache.org

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

docs.cloudera.com/documentation/enterprise/latest/topics/impala.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Apache Software Foundation

initially developed by Facebook

Cloudera

Initial release

2008

2012

2013

Current release

2.3.4, January 2021

3.1.2, August 2019

4.0.0, July 2021

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

All OS with a Java VM

Linux

Data scheme

schema-free, schema definition possible

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
PHP
Python

All languages supporting JDBC/ODBC

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

user defined functions and integration of map-reduce

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

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.

yes

User concepts

Access control

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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