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DBMS > ClickHouse vs. Newts vs. Riak KV vs. SQL.JS

System Properties Comparison ClickHouse vs. Newts vs. Riak KV vs. SQL.JS

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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.

Time Series DBMS based on Cassandra

Distributed, fault tolerant key-value store

Port of SQLite to JavaScript

Primary database model

Relational DBMS

Time Series DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

opennms.github.io/newts

Technical documentation

clickhouse.com/docs

github.com/OpenNMS/newts/wiki

www.tiot.jp/riak-docs/riak/kv/latest

sql.js.org/documentation/index.html

Developer

Clickhouse Inc.

OpenNMS Group

OpenSource, formerly Basho Technologies

Alon Zakai

enhancements implemented by others

Initial release

2016

2014

2009

2012

Current release

v24.4.2.141-stable, June 2024

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

Cloud-based only

Only available as a cloud service

no

no

no

no

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++

Java

Erlang

JavaScript

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X
Windows

Linux
OS X

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-free

schema-free

yes

Typing

predefined data types such as float or date

yes

yes

no

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.

no

no

no

no

Secondary indexes

yes

no

restricted

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

no

no

yes

SQL-92 is not fully supported

APIs and other access methods

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

HTTP REST
Java API

HTTP API
Native Erlang Interface

JavaScript API

Supported programming languages

C#

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

Java

C

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript

Server-side scripts

Stored procedures

yes

no

Erlang

no

Triggers

no

no

yes

pre-commit hooks and post-commit hooks

no

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

based on Cassandra

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

based on Cassandra

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

no

yes

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

none

Foreign keys

Referential integrity

no

no

no

links between data sets can be stored

yes

Transaction concepts

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

no

no

no

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

Durability

Support for making data persistent

yes

yes

yes

no

except by serializing a db to a file

In-memory capabilities

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

yes

no

yes

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.

no

yes, using Riak Security

no

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Related products and services

3rd parties

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

Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
» more

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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