What is the recommended approach to prevent 'join explosion' when performing a left join in PySpark?

The recommended approach to preventing 'join explosion' when performing a left join in PySpark is to ensure the join key in the right DataFrame is unique. When you perform a left join, each row from the left DataFrame is matched with one or more rows from the right DataFrame based on the join key. If the join key in the right DataFrame contains duplicate values, this can lead to a situation where a single row in the left DataFrame corresponds to multiple rows in the right DataFrame, resulting in an inflated result set known as 'join explosion.'

By ensuring that the join key in the right DataFrame is unique, you effectively limit the number of resulting rows to match each row in the left DataFrame to only one corresponding row in the right DataFrame. This helps to maintain data integrity and efficiency during the join operation, resulting in a dataset that is both manageable and logically sound.

Other possible approaches, such as dropping duplicates after the join, may not fully prevent the issue but rather address the symptoms. Switching to an inner join removes unmatched rows from the result set instead of addressing the fundamental problem at hand. Using a right join, on the other hand, flips the join operation and still does not resolve the underlying issue of duplicate keys