LARA

To start, first download the assignment: wikipedia.zip. For this assignment, you also need to download some data (133 MB): http://alaska.epfl.ch/~dockermoocs/bigdata/wikipedia.dat and place it in the folder: src/main/resources/wikipedia in your project directory.

Do not commit the wikipedia.dat file to your repository. This is very important, so I repeat: Do not commit the wikipedia.dat file to your repository. Git is not made for handling large files, and this would likely cause you many problems. We will see how to ignore this large file in git…

First, create the branch for this assignment and switch to that branch. You can verify that you are on the correct branch using git branch.

Then, we will update (or add) a git configuration file to tell git to ignore the large data file you have just downloaded. To tell git to ignore this file, you can update (or create, if it doesn't exist yet) the .gitignore file of your repository, which should be located at the root of your repository. Add the following line to it:

src/main/resources/

Then, every file in the src/main/resources directory will be ignored by git!

You can now safely add the handout to the repository and commit.

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 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), but you can also head over to Databricks Community Edition to experiment with your code on a “micro-cluster” for free.

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 SparkConfig instance. A SparkConfig 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 Docs.

Configure your cluster to run in local mode by implementing val conf and val sc.

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. In this exercise, we will use the textFile method of sc instead.

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.

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.

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.

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:

  List(("Scala",999999),("JavaScript",1278),("LOLCODE",982),("Java",42))

The list should be sorted in descending order. That is, according to thisranking, the pair with the highest second component (the count) should be thefirst element of the list.

Pay attention to roughly how long it takes to run this part! (It should take tens of seconds.)

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.

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 #2? Why?

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?