DBMS > Citus vs. ClickHouse vs. Microsoft Azure SQL Database vs. QuestDB

System Properties Comparison Citus vs. ClickHouse vs. Microsoft Azure SQL Database vs. QuestDB

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Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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.

Database as a Service offering with high compatibility to Microsoft SQL Server

A high performance open source SQL database for time series data

Primary database model

Relational DBMS

Time Series DBMS

Secondary database models

Document store

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

www.citusdata.com

clickhouse.com

azure.microsoft.com/en-us/products/azure-sql/database

questdb.io

Technical documentation

docs.citusdata.com

clickhouse.com/docs

docs.microsoft.com/en-us/azure/azure-sql

questdb.io/docs

Developer

Clickhouse Inc.

Microsoft

QuestDB Technology Inc

Initial release

2010

2016

2010

2014

Current release

8.1, December 2018

v24.4.1.2088-stable, May 2024

V12

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache 2.0

commercial

Open Source

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

Implementation language

C++

Java (Zero-GC), C++, Rust

Server operating systems

Linux

FreeBSD
Linux
macOS

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free via InfluxDB Line Protocol

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL with time-series extensions

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

ADO.NET
JDBC
ODBC

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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#
Java
JavaScript (Node.js)
PHP
Python
Ruby

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

horizontal partitioning (by timestamps)

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

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

yes, with always 3 replicas available

Source-replica replication with eventual consistency

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

ACID

ACID for single-table writes

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

through memory mapped files

User concepts

Access control

fine grained access rights according to SQL-standard

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.

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Relational model with native time series support Column-based storage and time partitioned...
» more

Competitive advantages

High ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more

Typical application scenarios

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Banks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more

Licensing and pricing models

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

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