DBMS > Apache Impala vs. ClickHouse vs. Kdb vs. Quasardb

System Properties Comparison Apache Impala vs. ClickHouse vs. Kdb vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.

High performance Time Series DBMS

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Document store

Time Series DBMS

Relational DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	7.71
Rank	#49	Overall
	#2	Time Series DBMS
	#1	Vector DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

Technical documentation

impala.apache.org/impala-docs.html

clickhouse.com/docs

code.kx.com

doc.quasar.ai/master

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Clickhouse Inc.

Kx Systems, a division of First Derivatives plc

quasardb

Initial release

2013

2016

2000

kdb was released 2000, kdb+ in 2003

2009

Current release

4.1.0, June 2022

v24.4.1.2088-stable, May 2024

3.6, May 2018

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

free 32-bit version

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

Linux

FreeBSD
Linux
macOS

Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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'

yes

with tags

SQL

Support of SQL

SQL-like DML and DDL statements

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language (q)

SQL-like query language

APIs and other access methods

JDBC
ODBC

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

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

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

Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

similar paradigm used for internal processing

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

by using LevelDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

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.

rights management via user accounts

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

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

Typical application scenarios

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

Key customers

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

Market metrics

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

Licensing and pricing models

upon request
» more

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