DBMS > ClickHouse vs. Dolt vs. Snowflake vs. SQLite vs. SQream DB

System Properties Comparison ClickHouse vs. Dolt vs. Snowflake vs. SQLite vs. SQream DB

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.

A MySQL compatible DBMS with Git-like versioning of data and schema

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

Widely used embeddable, in-process RDBMS

a GPU-based, columnar RDBMS for big data analytics workloads

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	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	0.93
Rank	#194	Overall
	#90	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Website

clickhouse.com

github.com/dolthub/dolt
www.dolthub.com

www.snowflake.com

www.sqlite.org

sqream.com

Technical documentation

clickhouse.com/docs

docs.dolthub.com

docs.snowflake.net/manuals/index.html

www.sqlite.org/docs.html

docs.sqream.com

Developer

Clickhouse Inc.

DoltHub Inc

Snowflake Computing Inc.

Dwayne Richard Hipp

SQream Technologies

Initial release

2016

2018

2014

2000

2017

Current release

v24.6.2.17-stable, July 2024

3.46.1 (13 August 2024), August 2024

2022.1.6, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Open Source

Public Domain

commercial

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.

Implementation language

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS
Windows

hosted

server-less

Linux

Data scheme

yes

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

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes, ANSI Standard SQL Types

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-92 is not fully supported

yes

APIs and other access methods

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

CLI Client
HTTP REST

CLI Client
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net
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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript (Node.js)
Python

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

user defined functions

user defined functions in Python

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

none

yes

none

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

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

via file-system locks

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.

Only one user is configurable, and must be specified in the config file at startup

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

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