How to skip more then one lines of header in RDD in Spark

Question

Data in my first RDD is like

1253
545553
12344896
1 2 1
1 43 2
1 46 1
1 53 2

Now the first 3 integers are some counters that I need to broadcast. After that all the lines have the same format like

1 2 1
1 43 2

I will map all those values after 3 counters to a new RDD after doing some computation with them in function. But I'm not able to understand how to separate those first 3 values and map the rest normally.

My Python code is like this

documents = sc.textFile("file.txt").map(lambda line: line.split(" "))

final_doc = documents.map(lambda x: (int(x[0]), function1(int(x[1]), int(x[2])))).reduceByKey(lambda x, y: x + " " + y)

It works only when first 3 values are not in the text file but with them it gives error.

I don't want to skip those first 3 values, but store them in 3 broadcast variables and then pass the remaining dataset in map function.

And yes the text file has to be in that format only. I cannot remove those 3 values/counters

Function1 is just doing some computation and returning the values.

Solution 1

1. Imports for Python 2

   from __future__ import print_function
   

2. Prepare dummy data:

   s = "1253\n545553\n12344896\n1 2 1\n1 43 2\n1 46 1\n1 53 2"
   with open("file.txt", "w") as fw: fw.write(s)
   

3. Read raw input:

   raw = sc.textFile("file.txt")
   

4. Extract header:

   header = raw.take(3)
   print(header)
   ### [u'1253', u'545553', u'12344896']
   

5. Filter lines:

   • using zipWithIndex

     content = raw.zipWithIndex().filter(lambda kv: kv[1] > 2).keys()
     print(content.first())
     ## 1 2 1
     

   • using mapPartitionsWithIndex

     from itertools import islice
     
     content = raw.mapPartitionsWithIndex(
         lambda i, iter: islice(iter, 3, None) if i == 0 else iter)
     
     print(content.first())
     ## 1 2 1
     

NOTE: All credit goes to pzecevic and Sean Owen (see linked sources).

Solution 2

In my case I have a csv file like below

----- HEADER START -----
We love to generate headers
#who needs comment char?
----- HEADER END -----

colName1,colName2,...,colNameN
val__1.1,val__1.2,...,val__1.N

Took me a day to figure out

val rdd = spark.read.textFile(pathToFile)  .rdd
  .zipWithIndex() // get tuples (line, Index)
  .filter({case (line, index) => index > numberOfLinesToSkip})
  .map({case (line, index) => l}) //get rid of index
val ds = spark.createDataset(rdd) //convert rdd to dataset
val df=spark.read.option("inferSchema", "true").option("header", "true").csv(ds) //parse csv

Sorry code in scala, however can be easily converted to python