Consider a warc.csv file related data. An indicative line is:

$2017-03-22$T$22$:$13$:$57$Z$,$

Columns in order: first the warc date, the warc record id$,$ the warc type $($e$.$g$.$ metadata,

response, etc$),$ the content length, the public IP address, the target URL, the server running

the site $($eg apache, nginx, etc$),$ and finally the overall content of the page with the entire

HTML DOM.

Using RDDs write a Pyhton code to answer the following.

Task $1$:

For the time range between $2017-03-2222$:$00$ and $2017-03-2223$:$00,$ find the $5$ most used

servers. Results to be given in descending order of servers.

Tips:

For this query you will need to filter out the records that have null values so that

they are not taken into account in the calculation.

You will need to process the date with an appropriate Python library.

Task $3$:

Perform Task $1$ using DataFrames$/$Spark SQL and parquet file

Task $3$:

Please give instruction on how to measure the execution time for Task$1$:

Map$/$Reduce$-$RDD API

Spark SQL to CSV files $($you need to define the schema of each file$)$

Spark SQL to parquet file