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DBMS > ClickHouse vs. Google Cloud Bigtable vs. RocksDB vs. SQL.JS

System Properties Comparison ClickHouse vs. Google Cloud Bigtable vs. RocksDB 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.

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Port of SQLite to JavaScript

Primary database model

Relational DBMS

Key-value store
Wide column store

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	2.84
Rank	#97	Overall
	#16	Key-value stores

Score	0.53
Rank	#246	Overall
	#115	Relational DBMS

Website

cloud.google.com/bigtable

Technical documentation

clickhouse.com/docs

cloud.google.com/bigtable/docs

github.com/facebook/rocksdb/wiki

sql.js.org/documentation/index.html

Developer

Clickhouse Inc.

Google

Facebook, Inc.

Alon Zakai

enhancements implemented by others

Initial release

2016

2015

2013

2012

Current release

v24.6.2.17-stable, July 2024

9.4.0, June 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD

Open Source

Cloud-based only

Only available as a cloud service

no

yes

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.
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++

C++

JavaScript

Server operating systems

FreeBSD
Linux
macOS

hosted

Linux

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

no

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

no

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

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

C++ API
Java API

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

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

C
C++
Go
Java
Perl
Python
Ruby

JavaScript

Server-side scripts

Stored procedures

yes

no

no

no

Triggers

no

no

no

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

yes

no

no

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

none

Foreign keys

Referential integrity

no

no

no

yes

Transaction concepts

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

no

Atomic single-row operations

yes

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

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.

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

no

no

More information provided by the system vendor

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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

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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