Emit multiple pairs in map operation

Let's say I have rows of phone call records the format: ``` [CallingUser, ReceivingUser, Duration] ``` If I want to know the total amount of time that a given user has been on the phone (sum of Duration where the User was the CallingUser or the ReceivingUser). Effectively, for a given record, I would like to create 2 pairs `(CallingUser, Duration)` and `(ReceivingUser, Duration)`. What is the most efficient way to do this? I can add 2 `RDDs` together, but I am unclear if this is a good approach: ``` #Sample Data: callData = sc.parallelize([["User1", "User2", 2], ["User1", "User3", 4], ["User2", "User1", 8] ]) calls = callData.map(lambda record: (record[0], record[2])) #The potentially inefficient map in question: calls += callData.map(lambda record: (record[1], record[2])) reduce = calls.reduceByKey(lambda a, b: a + b) ```