DBMS > ClickHouse vs. Microsoft SQL Server vs. Snowflake vs. Spark SQL

System Properties Comparison ClickHouse vs. Microsoft SQL Server vs. Snowflake vs. Spark SQL

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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.

Microsofts flagship relational DBMS

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

clickhouse.com

www.microsoft.com/en-us/sql-server

www.snowflake.com

spark.apache.org/sql

Technical documentation

clickhouse.com/docs

learn.microsoft.com/en-US/sql/sql-server

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Clickhouse Inc.

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2016

1989

2014

Current release

v24.3.2.23-lts, April 2024

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

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.

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.
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.

Implementation language

C++

Scala

Server operating systems

FreeBSD
Linux
macOS

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL-like DML and DDL statements

APIs and other access methods

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

CLI Client
JDBC
ODBC

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
Rust
Scala

3rd party library

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

key based and custom

tables can be distributed across several files (horizontal partitioning); sharding through federation

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes, but depending on the SQL-Server Edition

yes

none

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

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. 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

Users with fine-grained authorization concept, user roles and pluggable authentication

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