"ClickHouse can't do joins" — is that actually true? 🏎️ A no-tuning, same-query benchmark across three platforms makes for a fun read. Title: Join me if you can: ClickHouse vs. Databricks vs. Snowflake — Part 1 URL: 🏎️ Overview A benchmark comparing ClickHouse, Databricks, and Snowflake on join-heavy SQL workloads using identical queries and data, from 721M to 7.2B rows — testing the conventional wisdom about join performance. ❓ Challenges Solved ClickHouse is known as a fast analytics DB, yet there's a persistent belief that "it can't do joins." ・That belief often becomes a reason to drop ClickHouse when choosing an analytics stack ・This post checks whether it's true via a same-conditions three-way comparison 💡 Methodology ・Reproduces an existing "coffee shop" benchmark that originally compared Databricks and Snowflake ・Runs the 17 join-heavy queries on ClickHouse Cloud with minimal changes ・Key detail: no tuning at all — not the queries, not the ClickHouse side ・Each query run 5 times with the fastest reported, across 2-16 node configs (AWS) 📊 Experimental Results ・721M rows: most queries finish in under 1 second — 3-5x faster than alternatives at lower cost ・1.4B rows: one query joins and processes 1.7 billion rows in just 0.5 seconds (competitors need 5-13s) ・7.2B rows: even complex queries finish in seconds, not minutes It shows ClickHouse handles large multi-table joins efficiently without special configuration. #ClickHouse# #Database#