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ClickHouse
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<h2 id="history">History</h2> <p>ClickHouse’s technology was first developed over 10 years ago at <a href="/facts/Yandex/RSCatUyn">Yandex</a>, Russia's largest technology company.<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a> 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 <a href="/facts/Yandex.Metrica/u7s1yxLf">Yandex.Metrica</a>. </p><p>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 <a href="/facts/Column-oriented_DBMS/RHleA9BY">column-oriented DBMS</a>, 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. </p><p>In 2016, the ClickHouse project was released as <a href="/facts/Open-source_software/HzTaB8E9">open-source software</a> under the <a href="/facts/Apache_License/5AeIjas8">Apache 2 license</a> 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 outperformed them with a throughput of hundreds of millions of rows per second. </p><p>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. <a class="footnote-ref" id="fnref:3" href="#fn:3"><sup>3</sup></a> ClickHouse was also implemented at <a href="/facts/CERN/y1gwRchm">CERN's</a> <a href="/facts/LHCb_experiment/PAlpBtnl">LHCb experiment</a> to store and process <a href="/facts/Metadata/qt35z79C">metadata</a> on 10 billion events with over 1000 attributes per event. </p> <h2 id="features">Features</h2> <p>The main features of the ClickHouse DBMS are:<a class="footnote-ref" id="fnref:4" href="#fn:4"><sup>4</sup></a> </p> <ul><li><i>True column-oriented <a href="/facts/Database/VYcpkevb">DBMS</a>.</i> Nothing is stored with the values. For example, constant-length values are supported to avoid storing their length "number" next to the values.</li> <li><i>Linear scalability.</i> It's possible to extend a cluster by adding servers.</li> <li><i>Fault tolerance.</i> 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.</li> <li><i>Capability to store and process petabytes of data.</i></li> <li><i><a href="/facts/SQL/UjQLJcYm">SQL</a> support.</i> ClickHouse supports an extended <a href="/facts/SQL/UjQLJcYm">SQL</a>-like language that includes arrays and nested data structures, approximate and <a href="/facts/Uniform_Resource_Identifier/UjyqxTpA">URI</a> functions, and the availability to connect an external key-value store.</li> <li><i>High performance.</i><a class="footnote-ref" id="fnref:5" href="#fn:5"><sup>5</sup></a> <ul><li>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 <a href="/facts/Central_processing_unit/Jxp1bqMV">CPU</a> performance.</li> <li>Sampling and approximate calculations are supported.</li> <li>Parallel and distributed query processing is available (including <a href="/facts/Join_(SQL)/Cxl5lxcR">JOINs</a>).</li></ul></li> <li><i>Data compression.</i></li> <li><i><a href="/facts/Hard_disk_drive/kekLSGSE">Hard disk drive</a> (HDD) optimization.</i> The system can process data that doesn't fit in <a href="/facts/Random-access_memory/DuD4bxh6">random-access memory</a> (RAM).</li> <li><i>Clients for <a href="/facts/Database/VYcpkevb">database</a> (DB) connectivity.</i> Database connection options include the console client, the <a href="/facts/Hypertext_Transfer_Protocol/TWtvf7JD">HTTP</a> <a href="/facts/Application_programming_interface/GMlN4vUr">API</a>, or one of the <a href="/facts/Wrapper_library/b61otW6r">wrappers</a> (wrappers are available for <a href="/facts/Python_(programming_language)/YbuGqofa">Python</a>, <a href="/facts/PHP/vU6YRJX0">PHP</a>,<a class="footnote-ref" id="fnref:6" href="#fn:6"><sup>6</sup></a> <a href="/facts/Node.js/FKzhV4Qa">NodeJS</a>,<a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a> <a href="/facts/Perl/fx31kjlT">Perl</a>,<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a> <a href="/facts/Ruby_(programming_language)/BeYSaPSR">Ruby</a><a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a> and <a href="/facts/R_(programming_language)/LSrkr8K8">R</a><a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a>). <a href="/facts/ODBC_driver/2dV2Fm5j">ODBC driver</a> and <a href="/facts/JDBC_driver/o74mE7E7">JDBC driver</a> are also available for ClickHouse.<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a><a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a></li></ul> <h2 id="limitations">Limitations</h2> <p>ClickHouse has some features that can be considered disadvantages: </p> <ul><li>There is no support for <a href="/facts/Database_transaction/q1Etrsl8">transactions</a>.</li> <li>Lack of full-fledged UPDATE/DELETE implementation.</li></ul> <h2 id="use-cases">Use cases</h2> <p>ClickHouse was designed for <a href="/facts/Online_analytical_processing/VNNbIlD0">OLAP</a> queries.<a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a> ClickHouse performs well when: </p> <ul><li>It works with a small number of tables that contain a large number of columns.</li> <li>Queries use a large number of rows extracted from the <a href="/facts/Database/VYcpkevb">DB</a>, but only a small subset of columns.</li> <li>Queries are relatively rare (usually around 100 requests per second per server).</li> <li>Column values are fairly small, usually consisting of numbers and short strings (for example, 60 <a href="/facts/Byte/BNHUp9Qq">bytes</a> per <a href="/facts/Uniform_Resource_Locator/pPD5g7Wq">URL</a>).</li> <li>High throughput is required when processing a single query (up to billions of rows per second per server).</li> <li>A query result is mostly filtered or aggregated.</li> <li>Data update uses a simple scenario (usually batch-only, without complicated transactions).</li></ul> <p>For simple queries, latencies of 50 ms are typical. </p><p>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. </p><p>ClickHouse can also be used as an internal data warehouse for in-house analysts. ClickHouse can store data from different systems (such as <a href="/facts/Apache_Hadoop/oUS1G2R7">Hadoop</a> or certain logs) and analysts can build internal dashboards with the data or perform real-time analysis for business purposes. </p> <h2 id="benchmark-results">Benchmark results</h2> <p>According to <a href="/facts/Benchmark_(computing)/XSS42cRe">benchmark</a> tests conducted by its developers,<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a> for <a href="/facts/Online_analytical_processing/VNNbIlD0">OLAP</a> queries ClickHouse is more than 100 times faster than <a href="/facts/Apache_Hive/G3M79J6b">Hive</a> (a <a href="/facts/Database/VYcpkevb">DBMS</a> based on the <a href="/facts/Apache_Hadoop/oUS1G2R7">Hadoop</a> technology stack) or <a href="/facts/MySQL/QEEDNBDP">MySQL</a> (a common <a href="/facts/Relational_database_management_system/pEKGmJLy">RDBMS</a>). </p> <h2 id="see-also">See also</h2> <ul><li><a href="/facts/List_of_column-oriented_DBMSes/6v256DXb">List of column-oriented DBMSes</a></li></ul> <h2 id="external-links">External links</h2> <ul><li><a href="https://clickhouse.com/">ClickHouse official website</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>"ClickHouse Raises $250M Series B to Scale Groundbreaking OLAP Database Management System Globally". 28 October 2021. <a href="https://www.businesswire.com/news/home/20211028005287/en/ClickHouse-Raises-250M-Series-B-To-Scale-Groundbreaking-OLAP-Database-Management-System-Globally" target="_blank">https://www.businesswire.com/news/home/20211028005287/en/ClickHouse-Raises-250M-Series-B-To-Scale-Groundbreaking-OLAP-Database-Management-System-Globally</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>"Yandex, Russia's biggest technology company, celebrates 20 years". The Economist. 30 September 2017. <a href="https://www.economist.com/business/2017/09/30/yandex-russias-biggest-technology-company-celebrates-20-years" target="_blank">https://www.economist.com/business/2017/09/30/yandex-russias-biggest-technology-company-celebrates-20-years</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Lardinois, Frederic (2022-12-06). "ClickHouse launches ClickHouse Cloud, extends its Series B". TechCrunch. Retrieved 2023-07-24. <a href="https://techcrunch.com/2022/12/06/clickhouse-launches-clickhouse-cloud-extends-its-series-b/" target="_blank">https://techcrunch.com/2022/12/06/clickhouse-launches-clickhouse-cloud-extends-its-series-b/</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>"ClickHouse Guide". clickhouse.yandex. Retrieved 2016-11-10. <a href="https://clickhouse.yandex/reference_en.html#Distinctive%20features%20of%20ClickHouse" target="_blank">https://clickhouse.yandex/reference_en.html#Distinctive%20features%20of%20ClickHouse</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>"Performance comparison of analytical DBMS". clickhouse.yandex. Retrieved 2016-11-10. <a href="https://clickhouse.com/benchmark/dbms/" target="_blank">https://clickhouse.com/benchmark/dbms/</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>"smi2/phpClickHouse". GitHub. Retrieved 2016-11-10. <a href="https://github.com/smi2/phpClickHouse" target="_blank">https://github.com/smi2/phpClickHouse</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>"apla/node-clickhouse". GitHub. Retrieved 2016-11-10. <a href="https://github.com/apla/node-clickhouse" target="_blank">https://github.com/apla/node-clickhouse</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>"elcamlost/perl-DBD-ClickHouse". GitHub. Retrieved 2016-11-10. <a href="https://github.com/elcamlost/perl-DBD-ClickHouse" target="_blank">https://github.com/elcamlost/perl-DBD-ClickHouse</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>"archan937/clickhouse". GitHub. Retrieved 2016-11-10. <a href="https://github.com/archan937/clickhouse" target="_blank">https://github.com/archan937/clickhouse</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>"hannesmuehleisen/clickhouse-r". GitHub. Retrieved 2016-11-10. <a href="https://github.com/hannesmuehleisen/clickhouse-r" target="_blank">https://github.com/hannesmuehleisen/clickhouse-r</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>"ClickHouse/clickhouse-odbc". GitHub. 13 December 2021. <a href="https://github.com/ClickHouse/clickhouse-odbc" target="_blank">https://github.com/ClickHouse/clickhouse-odbc</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>"ClickHouse/clickhouse-jdbc". GitHub. 11 December 2021. <a href="https://github.com/ClickHouse/clickhouse-jdbc" target="_blank">https://github.com/ClickHouse/clickhouse-jdbc</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>"ClickHouse Guide". clickhouse.yandex. Retrieved 2016-11-10. <a href="https://clickhouse.yandex/reference_en.html#Distinctive%20features%20of%20ClickHouse" target="_blank">https://clickhouse.yandex/reference_en.html#Distinctive%20features%20of%20ClickHouse</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>"Performance comparison of analytical DBMS". clickhouse.yandex. Retrieved 2016-11-10. <a href="https://clickhouse.com/benchmark/dbms/" target="_blank">https://clickhouse.com/benchmark/dbms/</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> </ol>