{"id":2762,"date":"2026-08-18T05:30:00","date_gmt":"2026-08-18T12:30:00","guid":{"rendered":"https:\/\/www.virendrachandak.com\/techtalk\/?p=2762"},"modified":"2026-08-18T07:02:09","modified_gmt":"2026-08-18T14:02:09","slug":"clickhouse-for-mysql-developers","status":"publish","type":"post","link":"https:\/\/www.virendrachandak.com\/techtalk\/clickhouse-for-mysql-developers\/","title":{"rendered":"ClickHouse for MySQL Developers: What&#8217;s Different and Why"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">To a developer coming from MySQL, ClickHouse looks deceptively familiar. It uses SQL, with tables, columns, <code>SELECT<\/code>, <code>WHERE<\/code>, and <code>GROUP BY<\/code>, and a client connects and runs queries immediately. But the first <code>UPDATE<\/code> of a single row breaks the illusion. ClickHouse isn&#8217;t MySQL with a faster engine \u2014 it&#8217;s a database built for a different job entirely.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <a href=\"https:\/\/www.virendrachandak.com\/techtalk\/what-is-clickhouse\/\">previous post<\/a> covers what ClickHouse is and when to use it; this one covers what a MySQL developer needs to know before using it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The core idea: columns, not rows<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">MySQL stores data <strong>row by row<\/strong>. A <code>users<\/code> row \u2014 id, name, email, created_at \u2014 sits together on disk. That&#8217;s perfect for OLTP: &#8220;fetch user 42,&#8221; &#8220;update user 42&#8217;s email.&#8221; One record is touched at a time.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse stores data <strong>column by column<\/strong>. Every <code>id<\/code> is stored together, every <code>name<\/code> together, every <code>created_at<\/code> together.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That single design choice explains almost everything else:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>SELECT count(*), avg(amount) FROM orders WHERE year = 2025<\/code><\/strong> only reads the <code>amount<\/code> and <code>year<\/code> columns \u2014 it never touches any other columns in the table. On a billion-row table, that&#8217;s the difference between seconds and minutes.<\/li>\n\n\n\n<li><strong>Columns compress well.<\/strong> A column of country codes or timestamps has a lot of repetition, so ClickHouse compresses it 10-30x. Less disk read means faster queries.<\/li>\n\n\n\n<li><strong>Reading a single full row is <em>slow<\/em><\/strong> \u2014 ClickHouse has to jump across every column file to reassemble it. This is why &#8220;give me user 42&#8221; is the wrong question to ask ClickHouse.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Columnar storage is only half the story. ClickHouse also uses <a href=\"https:\/\/clickhouse.com\/resources\/engineering\/vectorized-query-execution\" target=\"_blank\" rel=\"noopener noreferrer\">vectorized execution<\/a> \u2014 it processes thousands of values from a column at once, using SIMD instructions on the CPU, rather than evaluating a query row by row. MySQL walks rows one at a time; ClickHouse operates on whole columns in bulk. Columnar layout, heavy compression, and vectorized execution together are what make it fast.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">MySQL is for <em>transactions<\/em> \u2014 many small reads and writes of individual rows. ClickHouse is for <em>analytics<\/em> \u2014 scanning huge ranges to aggregate. The two are complementary and are typically run side by side.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What&#8217;s different from MySQL<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">1. No PRIMARY KEY in the MySQL sense<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In MySQL, the primary key enforces uniqueness and builds a B-tree index. In ClickHouse, the closest concept is the <code>ORDER BY<\/code> clause of the table \u2014 and <strong>it does not enforce uniqueness at all.<\/strong><\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-1\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript language-sql\">CREATE TABLE events\n(\n    event_date  <span class=\"hljs-built_in\">Date<\/span>,\n    user_id     UInt64,\n    event_type  <span class=\"hljs-built_in\">String<\/span>,\n    amount      Decimal(<span class=\"hljs-number\">10<\/span>, <span class=\"hljs-number\">2<\/span>)\n)\nENGINE = MergeTree\nORDER BY (event_date, user_id);<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-1\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p class=\"wp-block-paragraph\"><code>ORDER BY<\/code> sorts the data on disk and builds a <strong>sparse index<\/strong> \u2014 by default ClickHouse stores one index entry per 8,192 rows, not one per row. That&#8217;s why the index is tiny and why ClickHouse is built to scan ranges, not pluck single rows.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ORDER BY doesn&#8217;t just sort \u2014 it defines how ClickHouse physically organizes data into parts, which makes it the main lever for query speed. It should match how the table is filtered (here: by event_date, then user_id), not be chosen for uniqueness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Two identical rows can be inserted and ClickHouse will keep both. Enforcing uniqueness is the application&#8217;s responsibility, not the engine&#8217;s.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2. UPDATE and DELETE are not everyday operations<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In MySQL, rows are updated constantly. In ClickHouse, the data is designed to be <strong>append-only<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Updates and deletes exist (<code>ALTER TABLE ... UPDATE<\/code>, plus a lightweight <code>DELETE<\/code> \u2014 and, on recent versions, a lightweight <code>UPDATE<\/code>), but historically they&#8217;re &#8220;mutations&#8221; \u2014 asynchronous, heavy operations that rewrite large chunks of data. They&#8217;re meant for occasional corrections (GDPR deletes, backfills), not for an application&#8217;s normal write path.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Even with lightweight DELETE\/UPDATE, ClickHouse still behaves like an append-only system. Mutations mark rows for removal and clean them up during merges. They&#8217;re not row-store updates \u2014 they&#8217;re background transformations of columnar data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A design that needs frequent row updates is either modeled wrong for ClickHouse, or calls for a special engine like <code>ReplacingMergeTree<\/code> (which deduplicates rows with the same <code>ORDER BY<\/code> key \u2014 <em>eventually<\/em>, during background merges).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The pattern is to append events rather than update state, then compute the current state at query time.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">3. Insert in big batches, not row by row<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">MySQL is fine with thousands of single-row <code>INSERT<\/code> statements. ClickHouse is the opposite: every insert creates a small &#8220;part&#8221; (a set of files on disk) that must later be merged in the background. Thousands of tiny inserts become thousands of tiny parts, overwhelming the background merges; past a threshold ClickHouse stops accepting inserts and returns a <code>Too many parts<\/code> error.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Inserts should go in big batches \u2014 tens of thousands of rows at a time, ideally. For a genuine stream of single rows, <strong>asynchronous inserts<\/strong> (<code>async_insert = 1<\/code>, with <code>wait_for_async_insert = 0<\/code>) let ClickHouse buffer them server-side.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">4. There are no transactions<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">There is no <code>BEGIN<\/code>\/<code>COMMIT<\/code>\/<code>ROLLBACK<\/code> wrapping multiple statements as in MySQL. ClickHouse trades transactional guarantees for raw analytical throughput. Data that needs ACID transactions belongs in Postgres or MySQL, and is then streamed into ClickHouse for analysis.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">5. Joins work, but denormalize first<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Joins are supported and the SQL looks familiar, but large joins are memory-hungry because ClickHouse loads the right-hand table into memory. The idiomatic approach is to <strong>denormalize<\/strong> \u2014 store a wide, flat table \u2014 because that aligns with columnar physics: fewer lookups, fewer random accesses, more sequential scans. It is an analytics schema, not an OLTP schema.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For small lookup tables, <strong>dictionaries<\/strong> are a fast, in-memory key-value replacement for a join.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">6. SELECT * is the most expensive query<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">In MySQL, <code>SELECT *<\/code> costs little \u2014 the whole row already sits together on disk, so reading every column is barely more work than reading one. In ClickHouse it forces the engine to read every column file and reconstruct full rows \u2014 the opposite of what a columnar system is built for. Columnar databases reward precision: name only the columns a query actually needs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Data types: mostly familiar, a few new habits<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The behavioral differences above are the real adjustment. The type system is mostly familiar \u2014 most MySQL types map directly to a ClickHouse equivalent:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table>\n<thead>\n<tr><th>MySQL<\/th><th>ClickHouse<\/th><th>Note<\/th><\/tr>\n<\/thead>\n<tbody>\n<tr><td><code>VARCHAR(255)<\/code> \/ <code>TEXT<\/code><\/td><td><code>String<\/code><\/td><td>No length limit, no separate type<\/td><\/tr>\n<tr><td><code>INT<\/code>, <code>BIGINT<\/code><\/td><td><code>Int32<\/code>, <code>Int64<\/code> (or <code>UInt32<\/code>, <code>UInt64<\/code>)<\/td><td>Pick signed\/unsigned and width explicitly<\/td><\/tr>\n<tr><td><code>DATETIME<\/code><\/td><td><code>DateTime<\/code> \/ <code>DateTime64<\/code><\/td><td><code>DateTime64<\/code> for sub-second precision<\/td><\/tr>\n<tr><td><code>DECIMAL(10,2)<\/code><\/td><td><code>Decimal(10, 2)<\/code><\/td><td>Same idea<\/td><\/tr>\n<tr><td><code>NULL<\/code> columns<\/td><td><code>Nullable(String)<\/code><\/td><td>Nullability is a <em>wrapper<\/em> \u2014 and it costs performance, so avoid it where possible<\/td><\/tr>\n<\/tbody>\n<\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">A few ClickHouse-specific types worth knowing on day one:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>LowCardinality(String)<\/code><\/strong> \u2014 wrap any column with a smallish set of distinct values (statuses, country codes, event types). It dictionary-encodes the column and dramatically speeds up filtering and grouping.<\/li>\n\n\n\n<li><strong><code>Enum8<\/code> \/ <code>Enum16<\/code><\/strong> \u2014 like MySQL enums, stored as tiny integers.<\/li>\n\n\n\n<li><strong><code>Date32<\/code>, <code>IPv4<\/code>, <code>IPv6<\/code><\/strong> \u2014 analytics-oriented types for long historical date ranges and for compact, fast IP storage and operations.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Try it in Docker: build a MergeTree table<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">No install, no cluster. Just Docker:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs language-bash\">docker run -d --name clickhouse-playground \\\n  -p 8123:8123 -p 9000:9000 \\\n  --ulimit nofile=262144:262144 \\\n  clickhouse\/clickhouse-server<\/code><\/span><\/pre>\n\n\n<p class=\"wp-block-paragraph\">Open a SQL client inside the container:<\/p>\n\n\n<pre class=\"wp-block-code\"><span><code class=\"hljs language-bash\">docker exec -it clickhouse-playground clickhouse-client<\/code><\/span><\/pre>\n\n\n<p class=\"wp-block-paragraph\">Now create a table, load a million rows with a built-in generator, and run an aggregation:<\/p>\n\n\n<pre class=\"wp-block-code\" aria-describedby=\"shcb-language-2\" data-shcb-language-name=\"JavaScript\" data-shcb-language-slug=\"javascript\"><span><code class=\"hljs language-javascript language-sql\">CREATE TABLE events\n(\n    event_date <span class=\"hljs-built_in\">Date<\/span>,\n    user_id    UInt64,\n    event_type LowCardinality(<span class=\"hljs-built_in\">String<\/span>),\n    amount     Decimal(<span class=\"hljs-number\">10<\/span>, <span class=\"hljs-number\">2<\/span>)\n)\nENGINE = MergeTree\nORDER BY (event_date, user_id);\n\nINSERT INTO events\nSELECT\n    today() - rand() % <span class=\"hljs-number\">365<\/span>,\n    rand() % <span class=\"hljs-number\">100000<\/span>,\n    -- arrays are <span class=\"hljs-number\">1<\/span>-indexed <span class=\"hljs-keyword\">in<\/span> ClickHouse, hence the leading <span class=\"hljs-number\">1<\/span> +\n    &#91;<span class=\"hljs-string\">'click'<\/span>, <span class=\"hljs-string\">'view'<\/span>, <span class=\"hljs-string\">'purchase'<\/span>]&#91;<span class=\"hljs-number\">1<\/span> + rand() % <span class=\"hljs-number\">3<\/span>],\n    (rand() % <span class=\"hljs-number\">10000<\/span>) \/ <span class=\"hljs-number\">100<\/span>\nFROM numbers(<span class=\"hljs-number\">1000000<\/span>);\n\n-- Aggregate a million rows <span class=\"hljs-keyword\">in<\/span> milliseconds\nSELECT\n    event_type,\n    count() AS events,\n    round(sum(amount), <span class=\"hljs-number\">2<\/span>) AS total\nFROM events\nGROUP BY event_type\nORDER BY total DESC;<\/code><\/span><small class=\"shcb-language\" id=\"shcb-language-2\"><span class=\"shcb-language__label\">Code language:<\/span> <span class=\"shcb-language__name\">JavaScript<\/span> <span class=\"shcb-language__paren\">(<\/span><span class=\"shcb-language__slug\">javascript<\/span><span class=\"shcb-language__paren\">)<\/span><\/small><\/pre>\n\n\n<p class=\"wp-block-paragraph\">That <code>GROUP BY<\/code> over a million rows returns almost instantly \u2014 and it scales to billions because it only reads the columns the query names.<\/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\"><\/p>\n\n\n\n<h2 class=\"wp-block-heading\">ClickHouse in one paragraph<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ClickHouse is a <strong>columnar, append-only, analytics database<\/strong>. It reads only the columns a query names, compresses them hard, and is built to scan enormous ranges to compute aggregates \u2014 not to fetch or update individual rows. Data is inserted in big batches, updates are rare, the data is sorted with <code>ORDER BY<\/code> rather than indexed row by row, and the transactional source of truth stays in MySQL or Postgres. ClickHouse serves as the reporting half of the stack.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Everything else \u2014 engines, materialized views, distributed tables \u2014 builds on this foundation.<\/p>\n\n","protected":false},"excerpt":{"rendered":"<p>ClickHouse uses SQL and looks familiar, but it is not &#8220;MySQL but faster.&#8221; What a MySQL developer needs to know before using it \u2014 columnar storage, no enforced primary key, append-only writes, batch inserts, and denormalized joins \u2014 with runnable Docker examples.<\/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":"To a MySQL developer, ClickHouse looks familiar: tables, columns, SELECT, WHERE, GROUP BY. Then you UPDATE a single row \u2014 and the illusion breaks.\n\nIt isn't \"MySQL but faster.\" It's a columnar, append-only analytics engine built for a completely different job, and the habits that serve you well in MySQL will actively work against you here.\n\nMy new post walks a MySQL developer through the 6 differences that matter most:\n\u2022 No primary key in the MySQL sense (and no enforced uniqueness)\n\u2022 UPDATE\/DELETE aren't everyday operations\n\u2022 Insert in big batches, not row by row\n\u2022 No transactions\n\u2022 Joins work \u2014 but denormalize first\n\u2022 SELECT * is the most expensive query you can run\n\nEvery example is runnable in Docker \u2014 including a million rows aggregated in milliseconds.\n\n#ClickHouse #MySQL #DataEngineering #OLAP #Analytics #SQL #Database","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":[189,175,180,151,178,188,176],"class_list":["post-2762","post","type-post","status-publish","format-standard","hentry","category-clickhouse","tag-analytics","tag-clickhouse","tag-columnar-database","tag-mysql","tag-olap","tag-oltp","tag-sql"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - 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