DBMS > ClickHouse vs. Hive vs. Impala

System Properties Comparison ClickHouse vs. Hive vs. Impala

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Editorial information provided by DB-Engines

Name

Description

Column-oriented Relational DBMS powering Yandex

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

Analytic DBMS for Hadoop

Primary database model

Relational DBMS

Secondary database models

Time Series DBMS

Document store

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

Score	16.96
Rank	#39	Overall
	#24	Relational DBMS

Score	69.41
Rank	#18	Overall
	#12	Relational DBMS

Score	17.39
Rank	#37	Overall
	#23	Relational DBMS

Website

clickhouse.com

hive.apache.org

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

Technical documentation

clickhouse.com/docs

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

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

Developer

Clickhouse Inc.

Apache Software Foundation

initially developed by Facebook

Cloudera

Initial release

2016

2012

2013

Current release

v23.7.4.5-stable, August 2023

3.1.3, April 2022

4.1.0, June 2022

License

Commercial or Open Source

Open Source

Apache 2.0

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

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

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

Java

C++

Server operating systems

FreeBSD
Linux
macOS

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, when using the MergeTree engine

yes

SQL

Support of SQL

Close to ANSI SQL

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST
JDBC
ODBC
Proprietary protocol

JDBC
ODBC
Thrift

JDBC
ODBC

Supported programming languages

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Scala

3rd party library

C++
Java
PHP
Python

All languages supporting JDBC/ODBC

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions and integration of map-reduce

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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 rights for users and roles

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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