Spark RDD – Mapping with extra arguments

You can use an anonymous function either directly in a flatMap

 json_data_rdd.flatMap(lambda j: processDataLine(j, arg1, arg2))

or to curry processDataLine

 f = lambda j: processDataLine(j, arg1, arg2)
 json_data_rdd.flatMap(f)

You can generate processDataLine like this:

 def processDataLine(arg1, arg2):
     def _processDataLine(dataline):
         return ... # Do something with dataline, arg1, arg2
     return _processDataLine

 json_data_rdd.flatMap(processDataLine(arg1, arg2))

toolz library provides useful curry decorator:

 from toolz.functoolz import curry

 @curry
 def processDataLine(arg1, arg2, dataline): 
     return ... # Do something with dataline, arg1, arg2

 json_data_rdd.flatMap(processDataLine(arg1, arg2))

Note that I’ve pushed dataline argument to the last position. It is not required but this way we don’t have to use keyword args.

Finally there is functools.partial already mentioned by Avihoo Mamka in the comments.