In PySpark, when joining two DataFrames, the how parameter specifies the type of join operation that will be performed. This parameter
is crucial because it determines how the rows from the two DataFrames are combined based on the specified join condition. Common types of join
operations include inner join, left join, right join, and outer join.
If the how parameter is not provided, PySpark will default to an inner join. This can lead to unexpected results if the join condition
is not met, as rows that do not satisfy the join condition will be excluded from the result.
Specifying the how parameter explicitly is important because it defines the logic of how you want to combine the data from the two
DataFrames. Depending on your data analysis needs, you might require different types of joins to get the desired results. For example, if you want to
include all records from one DataFrame regardless of whether they have a match in the other, you would use a left or right outer join. If you only
want records that have matches in both DataFrames, an inner join would be appropriate.
