Why is ClickHouse so fast?

It was designed to be fast. Query execution performance has always been a top priority during the development process, but other important characteristics like user-friendliness, scalability, and security were also considered so ClickHouse could become a real production system.

"Secrets of ClickHouse Performance Optimizations" talk from Big Data Technology Conference, December 2019, offers a more technical take on the same topic.

ClickHouse was initially built as a prototype to do just a single task well: to filter and aggregate data as fast as possible. That’s what needs to be done to build a typical analytical report, and that’s what a typical GROUP BY query does. The ClickHouse team has made several high-level decisions that, when combined, made achieving this task possible:

Column-oriented storage: Source data often contain hundreds or even thousands of columns, while a report can use just a few of them. The system needs to avoid reading unnecessary columns to avoid expensive disk read operations.

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