DBMS > atoti vs. ClickHouse vs. Google Cloud Bigtable vs. InfluxDB

System Properties Comparison atoti vs. ClickHouse vs. Google Cloud Bigtable vs. InfluxDB

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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.

DBMS for storing time series, events and metrics

Primary database model

Object oriented DBMS

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

with GEO package

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Website

atoti.io

clickhouse.com

cloud.google.com/bigtable

www.influxdata.com/products/influxdb-overview

Technical documentation

docs.atoti.io

clickhouse.com/docs

cloud.google.com/bigtable/docs

docs.influxdata.com/influxdb

Developer

ActiveViam

Clickhouse Inc.

Google

Initial release

2016

2015

2013

Current release

v24.4.1.2088-stable, May 2024

2.7.6, April 2024

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache 2.0

commercial

Open Source

MIT-License; commercial enterprise version available

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.

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.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

Java

C++

Server operating systems

FreeBSD
Linux
macOS

hosted

Linux
OS X

through Homebrew

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

SQL

Support of SQL

Multidimensional Expressions (MDX)

Close to ANSI SQL (SQL/JSON + extensions)

SQL-like query language

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)

HTTP API
JSON over UDP

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
Rust
Scala

3rd party library

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Server-side scripts

Stored procedures

Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

key based and custom

Sharding

in enterprise version only

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

selectable replication factor

in enterprise version only

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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)

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Depending on used storage engine

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)

simple rights management via user accounts

More information provided by the system vendor

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