Imagine that you want to joint two tables, and filter by a column that comes from the table in the right side of the join. On ClickHouse the query a bit differently than what you’d do in other databases like Postgres will result in a great performance improvemen.
Let’s say one of the tables is this
events table, with 100M rows:
And the other table is this
products one, with ~2M rows
If you were in Postgres, you’d do this:
But that is slow. We’ve explained how to make joins like that faster in this guide. This would be the query avoce rewritten to join
events with a
products table that has a Join engine. As you’d expect, it’s much faster:
If you will always filter the result after making the join as in the query above, you don’t need to make a join at all. ClickHouse saves data column-by-column, so filtering by the values in a column is a very fast operation. If you rewrite the query as follows, it would be just as fast. And you wouldn’t have create a Join table for it:
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