# How ClickHouse became fast at joins

DevFeed: [How ClickHouse became fast at joins](<https://devfeed.tech/articles/how-clickhouse-became-fast-at-joins-5094.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/clickhouse-fast-joins>)

Author: Tom Schreiber

Published: 2026-06-03T00:00:00Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [Databases](<https://devfeed.tech/topics/databases.md>), [real-time](<https://devfeed.tech/topics/real-time.md>)

Tags: [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [performance](<https://devfeed.tech/tags/performance.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [scale](<https://devfeed.tech/tags/scale.md>), [sql](<https://devfeed.tech/tags/sql.md>)

## AI overview

ClickHouse became 26x faster on the TPC-H SF100 join-heavy analytical workload over two years. The article describes the engineering work behind this improvement, including parallel hash joins, join reordering, filter pushdown, execution optimizations, runtime filters, lazy column replication, and smarter join planning.

## Source excerpt

Over two years of focused join engineering, ClickHouse became 26x faster on the TPC-H SF100 join-heavy workload. Here's how parallel hash joins, runtime filters, lazy column replication, and smarter join planning got us there.