{"id":2769,"date":"2026-07-21T06:00:00","date_gmt":"2026-07-21T13:00:00","guid":{"rendered":"https:\/\/www.virendrachandak.com\/techtalk\/?p=2769"},"modified":"2026-07-21T12:17:02","modified_gmt":"2026-07-21T19:17:02","slug":"what-is-clickhouse","status":"publish","type":"post","link":"https:\/\/www.virendrachandak.com\/techtalk\/what-is-clickhouse\/","title":{"rendered":"What Is ClickHouse, and When Should You Use It?"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">ClickHouse is an open-source columnar database built for <strong>analytics at massive scale<\/strong>: fast aggregation and filtering over very large datasets. ClickHouse may look like a relational database, but it\u2019s engineered for a fundamentally different purpose than Postgres or MySQL, and treating it as a drop-in replacement is the most common way to get poor results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This post covers what ClickHouse is, the workloads it is designed for, what it is used for, and when to stay with a traditional relational database instead.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What ClickHouse is<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse is an <strong>OLAP (online analytical processing)<\/strong> database. Instead of storing data by row, it stores each column separately. This design allows ClickHouse to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Read only the columns a query needs<\/li>\n\n\n\n<li>Compress each column aggressively<\/li>\n\n\n\n<li>Scan millions or billions of rows extremely quickly<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The result is analytical queries \u2014 counts, sums, averages, percentiles, time-series trends \u2014 that return in milliseconds even on massive datasets.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It was originally built at Yandex to power Metrica, a large web-analytics platform processing billions of events per day. It was open-sourced in 2016 and is now developed by ClickHouse Inc.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Column-oriented storage is the design decision that drives everything else about ClickHouse. The mechanics \u2014 and the habits it changes for anyone coming from a row based database \u2014 are a subject of their own. This post stays at the level of what ClickHouse is for.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">OLTP vs OLAP: Two different jobs<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Relational databases like Postgres and MySQL are OLTP (online transaction processing) systems. They handle an application&#8217;s live state: fetch one record, update an order, decrement a counter, etc. The workload is many small reads and writes of individual rows, wrapped in transactions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse is an OLAP system. It answers questions about large volumes of data: totals per region, percentiles per endpoint, trends over time, etc. The workload is fewer queries, each scanning a large range of rows and aggregating a few columns.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Category<\/th><th>OLTP \u2014 Postgres \/ MySQL<\/th><th>OLAP \u2014 ClickHouse<\/th><\/tr><\/thead><tbody><tr><td>Typical query<\/td><td>Fetch or update one record<\/td><td>Aggregate across millions of rows<\/td><\/tr><tr><td>Read pattern<\/td><td>A few rows, looked up by key<\/td><td>Large ranges, a few columns<\/td><\/tr><tr><td>Write pattern<\/td><td>Many small row-level inserts and updates<\/td><td>Large batches, append-only<\/td><\/tr><tr><td>Storage layout<\/td><td>Row-oriented<\/td><td>Column-oriented<\/td><\/tr><tr><td>Transactions<\/td><td>ACID, multi-statement<\/td><td>None \u2014 throughput over guarantees<\/td><\/tr><tr><td>Sweet spot<\/td><td>Application state<\/td><td>Analytics, reporting, dashboards<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The two are complementary. Most systems run an OLTP database for transactions and an OLAP database such as ClickHouse for analytics, rather than choosing one over the other.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What ClickHouse is used for<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse fits workloads where the main question is an aggregate over a large, append-mostly dataset. Common use cases include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Product, web, and clickstream analytics \u2014 page views, events, funnels, retention.<\/li>\n\n\n\n<li>Observability \u2014 storing and querying logs, metrics, and traces at scale.<\/li>\n\n\n\n<li>Real-time dashboards and business intelligence on top of large tables.<\/li>\n\n\n\n<li>Time-series and IoT data \u2014 sensor readings, application metrics, financial ticks.<\/li>\n\n\n\n<li>Business and financial reporting over orders, transactions, and usage events.<\/li>\n\n\n\n<li>Large-scale aggregation behind applications \u2014 usage metering, feature stores, ad and marketing analytics.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">When to use ClickHouse<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse is a good choice when most of the following are true:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The dataset is large and growing \u2014 tens of millions to billions of rows <\/li>\n\n\n\n<li>The data is mostly append-only.<\/li>\n\n\n\n<li>Queries aggregate, filter, and group over many rows rather than fetching individual records.<\/li>\n\n\n\n<li>Data can be inserted in batches, or a stream can be buffered into batches before it is written.<\/li>\n\n\n\n<li>Analytical results are needed in milliseconds to seconds<\/li>\n\n\n\n<li>The data is events, logs, metrics, clickstream, or time series.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">When not to use ClickHouse<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse is the wrong choice for transactional workloads. Use Postgres or MySQL when:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Individual rows are updated or deleted frequently, such as an order status, a user profile, or an inventory count. ClickHouse treats updates and deletes as rare, heavy operations.<\/li>\n\n\n\n<li>The workload looks up single records by key<\/li>\n\n\n\n<li>Multi-row ACID transactions are required, or the schema depends on enforced uniqueness and foreign keys.<\/li>\n\n\n\n<li>The dataset is small \u2014 a few million rows \u2014 where a well-indexed relational database is simpler and fast enough.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Many systems use both: a relational database as the transactional source of truth, with data streamed into ClickHouse for analytics. This arrangement \u2014 running the two side by side \u2014 is known as the two-database pattern.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Try ClickHouse in Docker<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse runs in a single Docker container with no configuration. Start the server and open a client:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs language-bash\">docker run -d --name clickhouse-playground \\\n  --ulimit nofile=262144:262144 \\\n  clickhouse\/clickhouse-server\n\ndocker exec -it clickhouse-playground clickhouse-client<\/code><\/span><\/pre>\n\n\n<p class=\"wp-block-paragraph\">ClickHouse includes a built-in row generator, <code>numbers()<\/code>, so query speed can be tested without loading any data. The following query aggregates one billion rows:<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php language-sql\">SELECT count(), round(avg(number)) <span class=\"hljs-keyword\">AS<\/span> avg_n, max(number) <span class=\"hljs-keyword\">AS<\/span> max_n\nFROM numbers(<span class=\"hljs-number\">1000000000<\/span>);<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p class=\"wp-block-paragraph\">On a typical laptop this returns in about 0.3 seconds. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Grouping a billion rows into buckets is similarly fast:<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"PHP\" data-shcb-language-slug=\"php\"><span><code class=\"hljs language-php language-sql\">SELECT number % <span class=\"hljs-number\">10<\/span> <span class=\"hljs-keyword\">AS<\/span> bucket, count() <span class=\"hljs-keyword\">AS<\/span> rows\nFROM numbers(<span class=\"hljs-number\">1000000000<\/span>)\nGROUP BY bucket\nORDER BY bucket;<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">PHP<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">php<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p class=\"wp-block-paragraph\">Both queries run with no indexes and no tuning. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Remove the container when finished:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs language-bash\">docker rm -f clickhouse-playground<\/code><\/span><\/pre>\n\n\n<p class=\"wp-block-paragraph\">That is the core of ClickHouse in a single container \u2014 no indexes, no tuning, and analytical queries over a billion rows in well under a second. Whether it fits a given workload comes down to the trade-offs above: an OLAP engine for large, append-mostly analytical data, not a replacement for a transactional database.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>ClickHouse is an open-source columnar database built for analytics at massive scale: fast aggregation and filtering over very large datasets. ClickHouse may look like a relational database, but it\u2019s engineered for a fundamentally different purpose than Postgres or MySQL, and treating it as a drop-in replacement is the most common way to get poor results.<\/p>\n<p>This post covers what ClickHouse is, the workloads it is designed for, what it is used for, and when to stay with a traditional relational database instead.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"What is ClickHouse? The open-source columnar OLAP database, explained. What it's for, when to use it, and when not to.","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[174],"tags":[189,175,180,151,178,188,173],"class_list":["post-2769","post","type-post","status-publish","format-standard","hentry","category-clickhouse","tag-analytics","tag-clickhouse","tag-columnar-database","tag-mysql","tag-olap","tag-oltp","tag-postgresql"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>What Is ClickHouse, and When Should You Use It? - Virendra&#039;s TechTalk<\/title>\n<meta name=\"description\" content=\"What is ClickHouse? The open-source columnar OLAP database, explained. 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