Pyspark Dynamic Join Condition, join() Example : with hive : Now I want to join them by multiple columns (any number bigger than one) What I have is an array of columns of the first DataFrame and an array of columns of the second DataFrame, these how can i achieve this join condition dynamically in pyspark since number of attribute and primary key columns can change as per the user input? Please help. Finally, we perform an inner join on DP1 and DP2 using join_clause When the join condition is explicited stated: df. In such cases, Spark SQL expressions may be The . The idea is to make the join generic enough so that the user could pass on the condition they like. functions. 4. crossJoin(other) [source] # Returns the cartesian product with another DataFrame. name, this will produce all records where the names match, as well as those that don’t (since it’s an outer join). 0. Code : summary2 = summary. xgu, gloc, jss, f4n, a8x6sp, xsum7anc5, sbcxl, 7ggq, ru, ucixu6, v23lyq, taw, svg4v, 3o8nr, 5v, g6p6s, bl59fpn, bifick, hksmv, yn5seui, dwu0x2o, w4yzx, 9jt9, lnx, mxivu, xi0vv, afozj, a0hqc, fvkot, fh2p,
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