Lab5: Exploring full-text Wikipedia articles

In this assignment, you will get to know Spark by exploring full-text Wikipedia articles. To start first download the assignment: wikipedia.zip

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 January 2015 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 RedMonk rankings. You'll complete this exercise on just one node (your laptop).

Decompress wikipedia.zip file. The folder contains a src folder, a build.sbt and a project folder containing eclipse plugin. You can run commands sbt clean compile package eclipse inside the project folder. The src folder contains a main folder which contains (1) the wikipedia resources and (2) the scala source code. You should complete wikipedia/WikipediaRanking.scala file where it is indicated by ???.

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. Then create 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. For help, see the Spark API Docs.

Read-in Wikipedia Data

There are several ways to read data into Spark. The simplest (but most unrealistic) 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 pre-processed a small chunk of Wikipedia for you, and have made it available in the articles member of the WikipediaData object. 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

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. Consider using method aggregate on RDD. Should you count the "Java" language when you see "JavaScript"? Take this into account in your code.

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 returns might look like this:

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.)

How much does the code take? What is the list of ranked languages?

Rank languages attempt #2: rankLangsUsingIndex

Compute an inverted index: makeIndex

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.

How much does the code take? What is the list of ranked languages?

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.

Hint: method mapValues on PairRDD could be useful for this part.

How much does the code take? What is the list of ranked languages?

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?

Lab Deliverables

Submit your lab report (including the source code and answers to the questions) in pdf format via moodle before Tuesday December 1, 8:30am.