DBMS > ClickHouse vs. HBase vs. InfluxDB vs. Kinetica

System Properties Comparison ClickHouse vs. HBase vs. InfluxDB vs. Kinetica

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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.

Wide-column store based on Apache Hadoop and on concepts of BigTable

DBMS for storing time series, events and metrics

Fully vectorized database across both GPUs and CPUs

Primary database model

Secondary database models

Time Series DBMS

Spatial DBMS

with GEO package

Spatial DBMS
Time Series DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Website

clickhouse.com

hbase.apache.org

www.influxdata.com/products/influxdb-overview

www.kinetica.com

Technical documentation

clickhouse.com/docs

hbase.apache.org/book.html

docs.influxdata.com/influxdb

docs.kinetica.com

Developer

Clickhouse Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kinetica

Initial release

2016

2008

2013

2012

Current release

v24.4.1.2088-stable, May 2024

2.3.4, January 2021

2.7.6, April 2024

7.1, August 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache version 2

Open Source

MIT-License; commercial enterprise version available

commercial

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.

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.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Java

C, C++

Server operating systems

FreeBSD
Linux
macOS

Linux
Unix
Windows

using Cygwin

Linux
OS X

through Homebrew

Linux

Data scheme

yes

schema-free, schema definition possible

schema-free

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

Numeric data and Strings

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 (SQL/JSON + extensions)

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

HTTP API
JSON over UDP

JDBC
ODBC
RESTful HTTP API

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#
C++
Groovy
Java
PHP
Python
Scala

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

in enterprise version only

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Multi-source replication
Source-replica replication

selectable replication factor

in enterprise version only

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Single row ACID (across millions of columns)

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

Depending on used storage engine

yes

GPU vRAM or System RAM

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

simple rights management via user accounts

Access rights for users and roles on table level

More information provided by the system vendor

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Market metrics

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Licensing and pricing models

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