Using Spark to merge two or more files content and manipulate the content - apache-spark

Question

Can I use Spark to do the following?
I have three files to merge and change the contents:
First File called column_header.tsv with this content:
first_name last_name address zip_code browser_type
Second file called data_file.tsv with this content:
John Doe 111 New Drive, Ca 11111 34
Mary Doe 133 Creator Blvd, NY 44499 40
Mike Coder 13 Jumping Street UT 66499 28
Third file called browser_type.tsv with content:
34 Chrome
40 Safari
28 FireFox
The final_output.tsv file after Spark processing the above should have this contents:
first_name last_name address zip_code browser_type
John Doe 111 New Drive, Ca 11111 Chrome
Mary Doe 133 Creator Blvd, NY 44499 Safari
Mike Coder 13 Jumping Street UT 66499 FireFox
Is this do able using Spark? Also I will consider Sed or Awk if it is possible use the tools. I know the above is possible with Python but I will prefer using Spark to do the data manipulation and changes. Any suggestions? Thanks in advance.

Answer 1

Here it is in awk, just in case. Notice the file order:
$ awk 'NR==FNR{ a[$1]=$2;next }{ $NF=($NF in a?a[$NF]:$NF) }1' file3 file1 file2
Output:
first_name last_name address zip_code browser_type
John Doe 111 New Drive, Ca 11111 Chrome
Mary Doe 133 Creator Blvd, NY 44499 Safari
Mike Coder 13 Jumping Street UT 66499 FireFox
Explained:
NR==FNR { # process browser_type file
a[$1]=$2 # remember remember the second of ...
next } # skip to the next record
{ # process the other files
$NF=( $NF in a ? a[$NF] : $NF) } # replace last field with browser from a
1 # implicit print

Answer 2

It is possible. Read header:
with open("column_header.tsv") as fr:
columns = fr.readline().split()
Read data_file.tsv:
users = spark.read.option("delimiter", "\t").csv("data_file.tsv").toDF(*columns)
Read called browser_type.tsv:
browsers = spark.read.csv("called browser_type.tsv") \
.toDF("browser_type", "browser_name")
Join:
users.join(browser, "browser_type", "left").write.csv(path)