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

$2017-03-22T22$:$13$:$57Z,,,$ response, $40802,213\mathrm{.}155\mathrm{.}18\mathrm{.}48,http$:coco $,$ Apache $,$

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, ngin $x,$ 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 target URL "coco", find the content length of the metadata as well as the size of the

HTML DOM $($number of characters$).$

Tips:

For this query you should filter by url and keep warc type "request.

Remember to restart the Spark cluster before each measurement, to avoid hot caches, or

you can clear the cache with the command spark.catalog.clearCache$()$

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