DBMS > ClickHouse vs. HarperDB vs. Microsoft Azure AI Search vs. MonetDB

System Properties Comparison ClickHouse vs. HarperDB vs. Microsoft Azure AI Search vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

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.

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

Search-as-a-service for web and mobile app development

A relational database management system that stores data in columns

Primary database model

Secondary database models

Time Series DBMS

Vector DBMS

Document store
Spatial DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

clickhouse.com

www.harperdb.io

azure.microsoft.com/en-us/services/search

www.monetdb.org

Technical documentation

clickhouse.com/docs

docs.harperdb.io/docs

learn.microsoft.com/en-us/azure/search

www.monetdb.org/Documentation

Developer

Clickhouse Inc.

HarperDB

Microsoft

MonetDB BV

Initial release

2016

2017

2015

2004

Current release

v24.4.1.2088-stable, May 2024

3.1, August 2021

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.

Implementation language

C++

Node.js

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

dynamic schema

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like data manipulation statements

yes

SQL 2003 with some extensions

APIs and other access methods

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

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
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
Rust
Scala

3rd party library

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C#
Java
JavaScript
Python

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

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

the nodes on which a table resides can be defined

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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. 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 and roles

yes

using Azure authentication

fine grained access rights according to SQL-standard

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