Spark RDD – Mapping with extra arguments

Question

Is it possible to pass extra arguments to the mapping function in pySpark?
Specifically, I have the following code recipe:

raw_data_rdd = sc.textFile("data.json", use_unicode=True)
json_data_rdd = raw_data_rdd.map(lambda line: json.loads(line))
mapped_rdd = json_data_rdd.flatMap(processDataLine)

The function processDataLine takes extra arguments in addition to the JSON object, as:

def processDataLine(dataline, arg1, arg2)

How can I pass the extra arguments arg1 and arg2 to the flaMap function?

Asked By: Stan

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Answer 1

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(dataline, 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.

Answered By: zero323

Spark RDD – Mapping with extra arguments

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