DBMS > Citus vs. ClickHouse vs. Firebase Realtime Database vs. Hazelcast vs. Linter

System Properties Comparison Citus vs. ClickHouse vs. Firebase Realtime Database vs. Hazelcast vs. Linter

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Column-oriented Relational DBMS powering Yandex

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

A widely adopted in-memory data grid

RDBMS for high security requirements

Primary database model

Secondary database models

Document store

Time Series DBMS

Document store

JSON support with IMDG 3.12

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

Score	1.36
Rank	#143	Overall
	#72	Relational DBMS

Score	7.96
Rank	#50	Overall
	#30	Relational DBMS

Score	16.95
Rank	#36	Overall
	#5	Document stores

Score	9.09
Rank	#47	Overall
	#5	Key-value stores

Score	0.12
Rank	#299	Overall
	#135	Relational DBMS

Website

www.citusdata.com

clickhouse.tech

firebase.google.com/products/realtime-database

hazelcast.com

linter.ru/en

Technical documentation

docs.citusdata.com

clickhouse.tech/docs

firebase.google.com/docs/database

hazelcast.org/imdg/docs

Developer

Yandex LLC

Google

acquired by Google 2014

Hazelcast

relex.ru/en

Initial release

2010

2016

2012

2008

1990

Current release

8.1, December 2018

v20.11.3.3-stable, November 2020

4.1.1, December 2020

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2; commercial licenses available

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.

Implementation language

C++

Java

C and C++

Server operating systems

Linux

FreeBSD
Linux
macOS

hosted

All OS with a Java VM

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Data scheme

yes

schema-free

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.

yes

specific XML type available, but no XML query functionality

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

Close to ANSI SQL

SQL-like query language

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST
JDBC
ODBC
Proprietary protocol

Android
iOS
JavaScript API
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Java
JavaScript
Objective-C

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

limited functionality with using 'rules'

yes

Event Listeners, Executor Services

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Callbacks are triggered when data changes

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

Replicated Map

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

one or two-phase-commit; repeatable reads; read commited

ACID

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

yes, based on authentication and database rules

Role-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Hazelcast is an in-memory computing platform that runs applications with extremely...
» more

Competitive advantages

Hazelcast has an optimized, multi-threaded architecture that enables the highest...
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Typical application scenarios

Hazelcast powers many types of large-scale, data processing applications. A sample...
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Key customers

Hazelcast customers include 50 of the world’s largest financial services companies,...
» more

Licensing and pricing models

Hazelcast provides both open source and commercial software. The open source offering...
» more

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

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More resources

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