DBMS > ClickHouse vs. Firebolt vs. RDF4J

System Properties Comparison ClickHouse vs. Firebolt vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Column-oriented Relational DBMS powering Yandex

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

RDF store

Secondary database models

Time Series DBMS

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

Score	15.19
Rank	#39	Overall
	#24	Relational DBMS

Score	2.07
Rank	#152	Overall
	#71	Relational DBMS

Score	1.02
Rank	#226	Overall
	#9	RDF stores

Website

clickhouse.com

www.firebolt.io

rdf4j.org

Technical documentation

clickhouse.com/docs

docs.firebolt.io

rdf4j.org/documentation

Developer

Clickhouse Inc.

Firebolt Analytics Inc.

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2016

2020

2004

Current release

v23.7.4.5-stable, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Server operating systems

FreeBSD
Linux
macOS

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

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, when using the MergeTree engine

yes

SQL

Support of SQL

Close to ANSI SQL

yes

APIs and other access methods

HTTP REST
JDBC
ODBC
Proprietary protocol

.Net
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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

Go
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

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

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

More information provided by the system vendor

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