How to iterate a RDD and remove the field if it exist in a list using PySpark

Question

I have a list which contains a couple of string values/field names. I also have a Spark RDD, I'd like to iterate the rdd and remove any field name that exists in the list. For example:

field_list = ["name_1", "name_2"]

RDD looks like this:

[Row(field_1=1, field_2=Row(field_3=[Row(field_4=[Row(name_1='apple', name_2='banana', name_3='F'), Row(name_1='tomato', name_2='eggplant', name_3='F')])]))]

I'm not very familiar with RDD, I understand that I can use map() to perform iteration, but how can I add the conditions, if it finds "name_1" or "name_2" which exists in the field_list, then remove the value and the field, so the expected result is a new RDD looks like:

[Row(field_1=1, field_2=Row(field_3=[Row(field_4=[Row(name_3='F'), Row(name_3='F')])]))]

Answer 1

You could recreate the whole structure, but without fields which you don't need. I'm not sure, maybe there's a better method, but looking at the Row documentation we see that it's limited on methods.

Inputs:

from pyspark.sql import Row
rdd = spark.sparkContext.parallelize([
    Row(field_1=1, field_2=Row(field_3=[Row(field_4=[Row(name_1='apple', name_2='banana', name_3='F'), Row(name_1='tomato', name_2='eggplant', name_3='F')])]))
])
print(rdd.collect())
# [Row(field_1=1, field_2=Row(field_3=[Row(field_4=[Row(name_1='apple', name_2='banana', name_3='F'), Row(name_1='tomato', name_2='eggplant', name_3='F')])]))]

field_list = ["name_1", "name_2"]

Script:

F4 = Row('field_4')
F3 = Row('field_3')
F2 = Row('field_1', 'field_2')
def transform(row):
    f3 = []
    for x in row['field_2']['field_3']:
        f4 = []
        for y in x['field_4']:
            Names = Row(*(set(y.asDict()) - set(field_list)))
            f4.append(Names(*[y[n] for n in Names]))
        f3.append(F4(f4))
    return F2(row['field_1'], F3(f3))

rdd = rdd.map(transform)

print(rdd.collect())
# [Row(field_1=1, field_2=Row(field_3=[Row(field_4=[Row(name_3='F'), Row(name_3='F')])]))]