DBMS > ClickHouse vs. Dolt vs. QuestDB vs. Spark SQL

System Properties Comparison ClickHouse vs. Dolt vs. QuestDB vs. Spark SQL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Column-oriented Relational DBMS powering Yandex

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

A high performance open source SQL database for time series data

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store

Relational DBMS

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

Score	12.32
Rank	#46	Overall
	#30	Relational DBMS

Score	0.62
Rank	#218	Overall
	#106	Relational DBMS

Score	1.19
Rank	#161	Overall
	#12	Time Series DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

clickhouse.tech

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

questdb.io

spark.apache.org/sql

Technical documentation

clickhouse.tech/docs

docs.dolthub.com

questdb.io/docs/introduction

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

Developer

Clickhouse Inc.

DoltHub Inc

QuestDB Limited

Apache Software Foundation

Initial release

2016

2018

2014

Current release

v22.3.3.44-lts, April 2022

3.2.0, October 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

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.

Implementation language

C++

Java

Scala

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free via InfluxDB Line Protocol

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.

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL

yes

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST
JDBC
ODBC
Proprietary protocol

CLI Client
HTTP REST

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC

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
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

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

over HTTP

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

currently in alpha release

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by timestamps)

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

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

Configurable consistency for N replicas

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

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

Access rights for users and roles

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

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

Real-time data ingestion and istant SQL queries for time series High performance...
» more

Typical application scenarios

Application metrics Financial market data and algo trading DevOps monitoring Real-time...
» more

Licensing and pricing models

Apache 2.0.
» more

News

QuestDB 6.3 Release Highlights
9 May 2022

Time Series Data Analytics with QuestDB and Cube
26 April 2022

Demo of live crypto data streamed with QuestDB and Grafana
12 April 2022

Calling on our community members to help us support Ukraine
7 March 2022

QuestDB 6.2 January release, SQL JIT compiler
27 January 2022

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.