DBMS > Apache Impala vs. ClickHouse vs. Hive vs. MarkLogic

System Properties Comparison Apache Impala vs. ClickHouse vs. Hive vs. MarkLogic

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

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

Operational and transactional Enterprise NoSQL database

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Secondary database models

Document store

Time Series DBMS

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Website

Technical documentation

impala.apache.org/impala-docs.html

clickhouse.com/docs

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

docs.marklogic.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

Apache Software Foundation

initially developed by Facebook

MarkLogic Corp.

Initial release

2013

2016

2012

2001

Current release

4.1.0, June 2022

v24.4.1.2088-stable, May 2024

3.1.3, April 2022

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

restricted free version is available

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.

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Implementation language

C++

Java

C++

Server operating systems

Linux

FreeBSD
Linux
macOS

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

schema-free

Schema can be enforced

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like DML and DDL statements

yes

SQL92

APIs and other access methods

JDBC
ODBC

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

JDBC
ODBC
Thrift

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Supported programming languages

All languages supporting JDBC/ODBC

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
Rust
Scala

3rd party library

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions and integration of map-reduce

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users, groups and roles

Role-based access control at the document and subdocument levels

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