DBMS > ClickHouse vs. HarperDB vs. Kdb vs. QuestDB

System Properties Comparison ClickHouse vs. HarperDB vs. Kdb vs. QuestDB

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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.

High performance Time Series DBMS

A high performance open source SQL database for time series data

Primary database model

Relational DBMS

Document store

Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Time Series DBMS

Relational 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	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	2.52
Rank	#109	Overall
	#9	Time Series DBMS

Website

Technical documentation

clickhouse.com/docs

docs.harperdb.io/docs

code.kx.com

questdb.io/docs

Developer

Clickhouse Inc.

HarperDB

Kx Systems, a division of First Derivatives plc

QuestDB Technology Inc

Initial release

2016

2017

2000

kdb was released 2000, kdb+ in 2003

2014

Current release

v24.4.1.2088-stable, May 2024

3.1, August 2021

3.6, May 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

free 32-bit version

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.

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

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

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X

Linux
OS X
Solaris
Windows

Linux
macOS
Windows

Data scheme

yes

dynamic schema

yes

schema-free via InfluxDB Line Protocol

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.

yes

Secondary indexes

yes

table attribute 'grouped'

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like data manipulation statements

SQL-like query language (q)

SQL with time-series 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

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

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

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
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

over HTTP

Server-side scripts

Stored procedures

yes

Custom Functions

since release 3.1

user defined functions

Triggers

yes

with views

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

horizontal partitioning

horizontal partitioning (by timestamps)

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

Source-replica replication

Source-replica replication with eventual consistency

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

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 for single-table writes

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

through memory mapped files

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

rights management via user accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

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

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

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

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Financial tick data Industrial IoT Application Metrics Monitoring
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

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

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
» more

Licensing and pricing models

upon request
» more

Open source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more

News

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.