# Wikipedia
Use the following commands to make a fresh clone of your repository:
```
git clone -b wikipedia git@gitlab.epfl.ch:lamp/student-repositories-s21/cs206-GASPAR.git cs206-wikipedia
```
## Useful links
* [The API documentation of Spark](http://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html)
* [The API documentation of the Scala standard library](https://www.scala-lang.org/files/archive/api/2.13.4)
* [The API documentation of the Java standard library](https://docs.oracle.com/en/java/javase/15/docs/api/index.html)
**If you have issues with the IDE, try [reimporting the
build](https://gitlab.epfl.ch/lamp/cs206/-/blob/master/labs/example-lab.md#ide-features-like-type-on-hover-or-go-to-definition-do-not-work),
if you still have problems, use `compile` in sbt instead.**
## Introduction
For this assignment, you will need to download the wikipedia dataset (68 MB):
[http://alaska.epfl.ch/~dockermoocs/bigdata/wikipedia-grading.dat](http://alaska.epfl.ch/~dockermoocs/bigdata/wikipedia-grading.dat)
and place it in the folder: `src/main/resources/wikipedia` in your
project directory.
In this assignment, you will get to know Spark by exploring full-text Wikipedia
articles.
Gauging how popular a programming language is important for companies judging
whether or not they should adopt an emerging programming language. For that reason,
industry analyst firm RedMonk has bi-annually computed a ranking of programming
language popularity using a variety of data sources, typically from websites like
GitHub and StackOverflow. See their
[top-20 ranking for June 2016](http://redmonk.com/sogrady/2016/07/20/language-rankings-6-16/)
as an example.
In this assignment, we'll use our full-text data from Wikipedia to produce a
rudimentary metric of how popular a programming language is, in an effort to see
if our Wikipedia-based rankings bear any relation to the popular Red Monk rankings.
You'll complete this exercise on just one node (your laptop).
## Set up Spark
For the sake of simplified logistics, we'll be running Spark in "local" mode. This
means that your full Spark application will be run on one node, locally, on your
laptop.
To start, we need a `SparkContext`. A `SparkContext` is the
"handle" to your cluster. Once you have a `SparkContext`, you can use it
to create and populate RDDs with data.
To create a `SparkContext`, you need to first create a
`SparkConf` instance. A `SparkConf` represents the
configuration of your Spark application. It's here that you must specify that you
intend to run your application in "local" mode. You must also name your Spark
application at this point. For help, see the
[Spark API documentation](http://spark.apache.org/docs/latest/api/scala/org/apache/spark/index.html).
Configure your cluster to run in local mode by implementing `val conf`
and `val sc`.
## Read-in Wikipedia Data
There are several ways to read data into Spark. The simplest way to read in data
is to convert an existing collection in memory to an RDD using the
`parallelize` method of the Spark context.
We have already implemented a method `parse` in the object
`WikipediaData` object that parses a line of the dataset and turns it into a `WikipediaArticle`.
Create an `RDD` (by implementing `val wikiRdd`) which contains
the `WikipediaArticle` objects of `articles`.
## Compute a ranking of programming languages
We will use a simple metric for determining the popularity of a programming
language: the number of Wikipedia articles that mention the language at least once.
### Rank languages attempt #1: rankLangs
**Computing** `occurrencesOfLang`
Start by implementing a helper method `occurrencesOfLang` which computes
the number of articles in an `RDD` of type `RDD[WikipediaArticles]`
that mention the given language at least once. For the sake of simplicity we check
that it least one word (delimited by spaces) of the article text is equal to the given
language.
**Computing the ranking,** `rankLangs`
Using `occurrencesOfLang`, implement a method `rankLangs` which
computes a list of pairs where the second component of the pair is the number of
articles that mention the language (the first component of the pair is the name of
the language).
An example of what `rankLangs` might return might look like this, for
example:
```scala
List(("Scala", 999999), ("JavaScript", 1278), ("LOLCODE", 982), ("Java", 42))
```
The list should be sorted in descending order. That is, according to this ranking,
the pair with the highest second component (the count) should be the first element
of the list.
Pay attention to roughly how long it takes to run this part! (It should take
tens of seconds.)
### Rank languages attempt #2: rankLangsUsingIndex
**Compute an inverted index**
An inverted index is an index data structure storing a mapping from content,
such as words or numbers, to a set of documents. In particular, the purpose of
an inverted index is to allow fast full text searches. In our use-case, an
inverted index would be useful for mapping from the names of programming
languages to the collection of Wikipedia articles that mention the name at
least once.
To make working with the dataset more efficient and more convenient, implement
a method that computes an "inverted index" which maps programming language names
to the Wikipedia articles on which they occur at least once.
Implement method `makeIndex` which returns an RDD of the following type:
`RDD[(String, Iterable[WikipediaArticle])]`. This RDD contains pairs,
such that for each language in the given `langs` list there is at most
one pair. Furthermore, the second component of each pair (the `Iterable`)
contains the `WikipediaArticles` that mention the language at least once.
_Hint: You might want to use methods **`flatMap`** and
**`groupByKey`** on **`RDD`** for this part._
**Computing the ranking, `rankLangsUsingIndex`**
Use the `makeIndex` method implemented in the previous part to
implement a faster method for computing the language ranking.
Like in part 1, `rankLangsUsingIndex` should compute a list of pairs
where the second component of the pair is the number of articles that mention
the language (the first component of the pair is the name of the language).
Again, the list should be sorted in descending order. That is, according to
this ranking, the pair with the highest second component (the count) should
be the first element of the list.
_Hint: method **`mapValues`** on **`PairRDD`** could be useful
for this part._
_Can you notice a performance improvement over attempt #1? Why?_
### Rank languages attempt #3: rankLangsReduceByKey
In the case where the inverted index from above is _only_ used for computing
the ranking and for no other task (full-text search, say), it is more efficient
to use the `reduceByKey` method to compute the ranking directly,
without first computing an inverted index. Note that the `reduceByKey`
method is only defined for RDDs containing pairs (each pair is interpreted as
a key-value pair).
Implement the `rankLangsReduceByKey` method, this time computing the
ranking without the inverted index, using `reduceByKey`.
Like in part 1 and 2, `rankLangsReduceByKey` should compute a list
of pairs where the second component of the pair is the number of articles that
mention the language (the first component of the pair is the name of the language).
Again, the list should be sorted in descending order. That is, according to
this ranking, the pair with the highest second component (the count) should
be the first element of the list.
_Can you notice an improvement in performance compared to measuring both the
computation of the index and the computation of the ranking as we did in
attempt #2? If so, can you think of a reason?_