DBMS > Blueflood vs. ClickHouse vs. Spark SQL

System Properties Comparison Blueflood vs. ClickHouse vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

Column-oriented Relational DBMS powering Yandex

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.00
Rank	#356	Overall
	#32	Time Series DBMS

Score	12.71
Rank	#46	Overall
	#30	Relational DBMS

Score	23.04
Rank	#35	Overall
	#21	Relational DBMS

Website

blueflood.io

clickhouse.tech

spark.apache.org/sql

Technical documentation

github.com/rackerlabs/blueflood/wiki

clickhouse.tech/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Rackspace

Clickhouse Inc.

Apache Software Foundation

Initial release

2013

2016

2014

Current release

v22.6.1.1985-stable, June 2022

3.3.0, June 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Scala

Server operating systems

Linux
OS X

FreeBSD
Linux
macOS

Linux
OS X
Windows

Data scheme

predefined scheme

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.

Secondary indexes

SQL

Support of SQL

Close to ANSI SQL

SQL-like DML and DDL statements

APIs and other access methods

HTTP REST

HTTP REST
JDBC
ODBC
Proprietary protocol

JDBC
ODBC

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

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

Access rights for users and roles

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