Spark specify multiple column conditions for dataframe join

There is a Spark column/expression API join for such case:

Leaddetails.join(
    Utm_Master, 
    Leaddetails("LeadSource") <=> Utm_Master("LeadSource")
        && Leaddetails("Utm_Source") <=> Utm_Master("Utm_Source")
        && Leaddetails("Utm_Medium") <=> Utm_Master("Utm_Medium")
        && Leaddetails("Utm_Campaign") <=> Utm_Master("Utm_Campaign"),
    "left"
)

The <=> operator in the example means “Equality test that is safe for null values”.

The main difference with simple Equality test (===) is that the first one is safe to use in case one of the columns may have null values.