ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP) that allows users to generate analytical reports using SQL queries in real-time. ClickHouse Inc. is headquartered in the Bay Area of California, United States with the subsidiary, ClickHouse B.V., based in Amsterdam, Netherlands.
In September of 2021 in San Francisco, CA, ClickHouse incorporated to house the open source technology with an initial $50 million investment from Index Ventures and Benchmark Capital with participation by Yandex N.V.[2] and others. On October 28, 2021 the company received Series B funding totaling $250 million at an valuation of $2 billion from Coatue Management, Altimeter Capital, and other investors. The company continues to build the open source project and engineering cloud technology.
ClickHouse’s technology was first developed over 10 years ago at Yandex, Russia's largest technology company.[3] In 2009, Alexey Milovidov and developers started an experimental project to check the hypothesis if it was viable to generate analytical reports in real-time from non-aggregated data that is also constantly added in real-time. The developers spent 3 years to prove this hypothesis, and in 2012 ClickHouse launched in production for the first time to power Yandex.Metrica, the second-largest web analytics platform in the world, after Google Analytics.
Unlike custom data structures used before, ClickHouse was applicable more generally to work as a database management system. The power and utility of ClickHouse offered a true column-oriented DBMS, it allowed for systems to generate reports from petabytes of raw data with sub-second latencies. ClickHouse was widely adopted at Yandex including for Yandex.Tank load testing tool and Yandex.Market to monitor site accessibility and KPIs.
In 2016, the ClickHouse project was released as open-source software under the Apache 2 license in June 2016 to power analytical use cases around the globe. The systems at the time offered a server throughput of a hundred thousand rows per second, ClickHouse out performed that speed with a throughput of hundreds of millions of rows per second.
Since ClickHouse became available as open source in 2016, its popularity has grown exponentially, as evidenced through adoption by industry-leading companies like Uber, Comcast, eBay, and Cisco. ClickHouse was also implemented at CERN'sLHCb experiment to store and process metadata on 10 billion events with over 1000 attributes per event.
True column-oriented DBMS. Nothing is stored with the values. For example, constant-length values are supported to avoid storing their length "number" next to the values.
Linear scalability. It's possible to extend a cluster by adding servers.
Fault tolerance. The system is a cluster of shards, where each shard is a group of replicas. ClickHouse uses asynchronous multi-master replication. Data is written to any available replica, then distributed to all the remaining replicas. ZooKeeper is used for coordinating processes, but it's not involved in query processing and execution.
Capability to store and process petabytes of data.
SQL support. ClickHouse supports an extended SQL-like language that includes arrays and nested data structures, approximate and URI functions, and the availability to connect an external key-value store.
Vector calculations are used. Data is not only stored by columns, but is processed by vectors (parts of columns). This approach allows it to achieve high CPU performance.
Sampling and approximate calculations are supported.
Parallel and distributed query processing is available (including JOINs).
It works with a small number of tables that contain a large number of columns.
Queries can use a large number of rows extracted from the DB, but only a small subset of columns.
Queries are relatively rare (usually around 100 RPS per server).
For simple queries, latencies of about 50 ms are allowed.
Column values are fairly small, usually consisting of numbers and short strings (for example, 60 bytes per URL).
High throughput is required when processing a single query (up to billions of rows per second per server).
A query result is mostly filtered or aggregated.
Data update uses a simple scenario (usually batch-only, without complicated transactions).
One of the common cases for ClickHouse is server log analysis. After setting regular data uploads to ClickHouse (it's recommended to insert data in fairly large batches with more than 1000 rows), it's possible to analyze incidents with instant queries or monitor a service's metrics, such as error rates, response times, and so on.
ClickHouse can also be used as an internal data warehouse for in-house analysts. ClickHouse can store data from different systems (such as Hadoop or certain logs) and analysts can build internal dashboards with the data or perform real-time analysis for business purposes.
Benchmark results
According to benchmark tests conducted by its developers,[5] for OLAP queries ClickHouse is more than 100 times faster than Hive (a DBMS based on the Hadoop technology stack) or MySQL (a common RDBMS).
^"ClickHouse/clickhouse-odbc". GitHub. 13 December 2021.
^"ClickHouse/clickhouse-jdbc". GitHub. 11 December 2021.
External links
ClickHouse official website
January 26, 2023
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This article has multiple issues Please help improve it or discuss these issues on the talk page Learn how and when to remove these template messages This article may contain excessive or inappropriate references to self published sources Please help improve it by removing references to unreliable sources where they are used inappropriately March 2022 Learn how and when to remove this template message This article may rely excessively on sources too closely associated with the subject potentially preventing the article from being verifiable and neutral Please help improve it by replacing them with more appropriate citations to reliable independent third party sources March 2022 Learn how and when to remove this template message Some of this article s listed sources may not be reliable Please help this article by looking for better more reliable sources Unreliable citations may be challenged or deleted March 2022 Learn how and when to remove this template message This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources ClickHouse news newspapers books scholar JSTOR January 2022 Learn how and when to remove this template message Learn how and when to remove this template message ClickHouse is an open source column oriented DBMS columnar database management system for online analytical processing OLAP that allows users to generate analytical reports using SQL queries in real time ClickHouse Inc is headquartered in the Bay Area of California United States with the subsidiary ClickHouse B V based in Amsterdam Netherlands ClickhouseDeveloper s ClickHouse Inc Initial releaseJune 15 2016 6 years ago 2016 06 15 Stable releasev22 9 3 18 stable September 30 2022 3 months ago 2022 09 30 1 Repositorygithub wbr com wbr ClickHouse wbr ClickHouse wbr Written inC Operating systemLinux FreeBSD macOSLicenseApache License 2 0Websiteclickhouse wbr comIn September of 2021 in San Francisco CA ClickHouse incorporated to house the open source technology with an initial 50 million investment from Index Ventures and Benchmark Capital with participation by Yandex N V 2 and others On October 28 2021 the company received Series B funding totaling 250 million at an valuation of 2 billion from Coatue Management Altimeter Capital and other investors The company continues to build the open source project and engineering cloud technology Contents 1 History 2 Features 3 Limitations 4 Use cases 5 Benchmark results 6 See also 7 References 8 External linksHistory EditClickHouse s technology was first developed over 10 years ago at Yandex Russia s largest technology company 3 In 2009 Alexey Milovidov and developers started an experimental project to check the hypothesis if it was viable to generate analytical reports in real time from non aggregated data that is also constantly added in real time The developers spent 3 years to prove this hypothesis and in 2012 ClickHouse launched in production for the first time to power Yandex Metrica the second largest web analytics platform in the world after Google Analytics Unlike custom data structures used before ClickHouse was applicable more generally to work as a database management system The power and utility of ClickHouse offered a true column oriented DBMS it allowed for systems to generate reports from petabytes of raw data with sub second latencies ClickHouse was widely adopted at Yandex including for Yandex Tank load testing tool and Yandex Market to monitor site accessibility and KPIs In 2016 the ClickHouse project was released as open source software under the Apache 2 license in June 2016 to power analytical use cases around the globe The systems at the time offered a server throughput of a hundred thousand rows per second ClickHouse out performed that speed with a throughput of hundreds of millions of rows per second Since ClickHouse became available as open source in 2016 its popularity has grown exponentially as evidenced through adoption by industry leading companies like Uber Comcast eBay and Cisco ClickHouse was also implemented at CERN s LHCb experiment to store and process metadata on 10 billion events with over 1000 attributes per event Features EditThis section relies excessively on references to primary sources Please improve this section by adding secondary or tertiary sources Find sources ClickHouse news newspapers books scholar JSTOR March 2021 Learn how and when to remove this template message The main features of the ClickHouse DBMS are 4 True column oriented DBMS Nothing is stored with the values For example constant length values are supported to avoid storing their length number next to the values Linear scalability It s possible to extend a cluster by adding servers Fault tolerance The system is a cluster of shards where each shard is a group of replicas ClickHouse uses asynchronous multi master replication Data is written to any available replica then distributed to all the remaining replicas ZooKeeper is used for coordinating processes but it s not involved in query processing and execution Capability to store and process petabytes of data SQL support ClickHouse supports an extended SQL like language that includes arrays and nested data structures approximate and URI functions and the availability to connect an external key value store High performance 5 Vector calculations are used Data is not only stored by columns but is processed by vectors parts of columns This approach allows it to achieve high CPU performance Sampling and approximate calculations are supported Parallel and distributed query processing is available including JOINs Data compression Hard disk drive HDD optimization The system can process data that doesn t fit in random access memory RAM Clients for database DB connectivity Database connection options include the console client the HTTP API or one of the wrappers wrappers are available for Python PHP 6 NodeJS 7 Perl 8 Ruby 9 and R 10 ODBC driver and JDBC driver are also available for ClickHouse 11 12 Limitations EditClickHouse has some features that can be considered disadvantages There is no support for transactions Lack of full fledged UPDATE DELETE implementation Use cases EditClickHouse was designed for OLAP queries 4 It works with a small number of tables that contain a large number of columns Queries can use a large number of rows extracted from the DB but only a small subset of columns Queries are relatively rare usually around 100 RPS per server For simple queries latencies of about 50 ms are allowed Column values are fairly small usually consisting of numbers and short strings for example 60 bytes per URL High throughput is required when processing a single query up to billions of rows per second per server A query result is mostly filtered or aggregated Data update uses a simple scenario usually batch only without complicated transactions One of the common cases for ClickHouse is server log analysis After setting regular data uploads to ClickHouse it s recommended to insert data in fairly large batches with more than 1000 rows it s possible to analyze incidents with instant queries or monitor a service s metrics such as error rates response times and so on ClickHouse can also be used as an internal data warehouse for in house analysts ClickHouse can store data from different systems such as Hadoop or certain logs and analysts can build internal dashboards with the data or perform real time analysis for business purposes Benchmark results EditAccording to benchmark tests conducted by its developers 5 for OLAP queries ClickHouse is more than 100 times faster than Hive a DBMS based on the Hadoop technology stack or MySQL a common RDBMS See also EditList of column oriented DBMSesReferences Edit Release 22 9 3 18 stable Github Retrieved 10 October 2022 ClickHouse Raises 250M Series B to Scale Groundbreaking OLAP Database Management System Globally 28 October 2021 Yandex Russia s biggest technology company celebrates 20 years The Economist 30 September 2017 a b ClickHouse Guide clickhouse yandex Retrieved 2016 11 10 a b Performance comparison of analytical DBMS clickhouse yandex Retrieved 2016 11 10 smi2 phpClickHouse GitHub Retrieved 2016 11 10 apla node clickhouse GitHub Retrieved 2016 11 10 elcamlost perl DBD ClickHouse GitHub Retrieved 2016 11 10 archan937 clickhouse GitHub Retrieved 2016 11 10 hannesmuehleisen clickhouse r GitHub Retrieved 2016 11 10 ClickHouse clickhouse odbc GitHub 13 December 2021 ClickHouse clickhouse jdbc GitHub 11 December 2021 External links EditClickHouse official website Retrieved from https en wikipedia org w index php title ClickHouse amp oldid 1117398816, wikipedia, wiki, book, books, library,
