To start, first download the assignment: stackoverflow.zip. For this assignment, you also need to download the data (170 MB):
and place it in the folder:
src/main/resources/stackoverflow in your
As for the last exercise, be sure not to include the data files in your repository. To do so, add the following lines in the
.gitignore file at the root of your repository:
src/main/resources/ target/ project/
Your repository folder should look like this:
. ├── .gitignore ├── assignment.sbt ├── build.sbt ├── project │ ├── CommonBuild.scala │ ├── GradingFeedback.scala │ ├── ScalaTestRunner.scala │ ├── Settings.scala │ ├── StudentBuild.scala │ ├── StudentBuildLike.scala │ ├── StyleChecker.scala │ ├── build.properties │ ├── buildSettings.sbt │ └── plugins.sbt └── src ├── main │ └── scala │ └── stackoverflow │ └── StackOverflow.scala └── test └── scala └── stackoverflow └── StackOverflowSuite.scala
The overall goal of this assignment is to implement a distributed k-means algorithm which clusters posts on the popular question-answer platform StackOverflow according to their score. Moreover, this clustering should be executed in parallel for different programming languages, and the results should be compared.
The motivation is as follows: StackOverflow is an important source of documentation. However, different user-provided answers may have very different ratings (based on user votes) based on their perceived value. Therefore, we would like to look at the distribution of questions and their answers. For example, how many highly-rated answers do StackOverflow users post, and how high are their scores? Are there big differences between higher-rated answers and lower-rated ones?
Finally, we are interested in comparing these distributions for different programming language communities. Differences in distributions could reflect differences in the availability of documentation. For example, StackOverflow could have better documentation for a certain library than that library's API documentation. However, to avoid invalid conclusions we will focus on the well-defined problem of clustering answers according to their scores.
Note: for this assignment, we assume you recall the K-means algorithm introduced earlier in the course. You may refer back to the third assignment for an overview of the algorithm.
You are given a CSV (comma-separated values) file with information about StackOverflow posts. Each line in the provided text file has the following format:
A short explanation of the comma-separated fields follows.
<postTypeId>: Type of the post. Type 1 = question, type 2 = answer.
<id>: Unique id of the post (regardless of type).
<acceptedAnswer>: Id of the accepted answer post. This information is optional, so maybe be missing indicated by an empty string.
<parentId>: For an answer: id of the corresponding question. For a question:missing, indicated by an empty string.
<score>: The StackOverflow score (based on user votes).
<tag>: The tag indicates the programming language that the post is about, in case it's a question, or missing in case it's an answer.
You will see the following code in the main class:
val lines = sc.textFile("src/main/resources/stackoverflow/stackoverflow.csv") val raw = rawPostings(lines) val grouped = groupedPostings(raw) val scored = scoredPostings(grouped) val vectors = vectorPostings(scored)
It corresponds to the following steps:
linesthe lines from the csv file as strings
raw: the raw Posting entries for each line
grouped: questions and answers grouped together
scored: questions and scores
vectors: pairs of (language, score) for each question
The first two methods are given to you. You will have to implement the rest.
We will now look at how you process the data before applying the kmeans algorithm.
Grouping questions and answers
The first method you will have to implement is
val grouped = groupedPostings(raw)
raw variable we have simple postings, either questions or
answers, but in order to use the data we need to assemble them together. Questions
are identified using a
postTypeId == 1. Answers to a question with
id == QID have (a)
postTypeId == 2 and (b)
parentId == QID.
Ideally, we want to obtain an RDD with the pairs of
(Question, Iterable[Answer]). However, grouping on the question
directly is expensive (can you imagine why?), so a better alternative is to match
on the QID, thus producing an
RDD[(QID, Iterable[(Question, Answer))].
To obtain this, in the
groupedPostings method, first filter the
questions and answers separately and then prepare them for a join operation by
extracting the QID value in the first element of a tuple. Then, use one of the
join operations (which one?) to obtain an
RDD[(QID, (Question, Answer))]. Then, the last step is to obtain an
RDD[(QID, Iterable[(Question, Answer)])]. How can you do that, what
method do you use to group by the key of a pair RDD?
Finally, in the description we made QID, Question and Answer separate types,
but in the implementation QID is an
Int and both questions and answers
are of type
Posting. Therefore, the signature of
def groupedPostings(postings: RDD[/* Question or Answer */ Posting]): RDD[(/*QID*/ Int, Iterable[(/*Question*/ Posting, /*Answer*/ Posting)])]
This should allow you to implement the
Second, implement the
scoredPostings method, which should return an
RDD containing pairs of (a) questions and (b) the score of the answer with the
highest score (note: this does not have to be the answer marked as
acceptedAnswer!). The type of this scored RDD is:
val scored: RDD[(Posting, Int)] = ???
For example, the
scored RDD should contain the following tuples:
((1,6,None,None,140,Some(CSS)),67) ((1,42,None,None,155,Some(PHP)),89) ((1,72,None,None,16,Some(Ruby)),3) ((1,126,None,None,33,Some(Java)),30) ((1,174,None,None,38,Some(C#)),20)
Hint: use the provided
answerHighScore given in
Creating vectors for clustering
Next, we prepare the input for the clustering algorithm. For this, we transform
scored RDD into a
vectors RDD containing the vectors
to be clustered. In our case, the vectors should be pairs with two components
(in the listed order!):
- Index of the language (in the
langslist) multiplied by the
- The highest answer score (computed above).
langSpread factor is provided (set to 50000). Basically, it makes
sure posts about different programming languages have at least distance 50000
using the distance measure provided by the
You will learn later what this distance means and why it is set to this value.
The type of the
vectors RDD is as follows:
val vectors: RDD[(Int, Int)] = ???
For example, the
vectors RDD should contain the following tuples:
(350000,67) (100000,89) (300000,3) (50000,30) (200000,20)
Implement this functionality in method
vectorPostings and by using
the given the
firstLangInTag helper method.
(Idea for test:
scored RDD should have 2121822 entries)
val means = kmeans(sampleVectors(vectors), vectors)
Based on these initial means, and the provided variables
method, implement the K-means algorithm by iteratively:
- pairing each vector with the index of the closest mean (its cluster);
- computing the new means by averaging the values of each cluster.
To implement these iterative steps, use the provided functions
In our tests, convergence is reached after 44 iterations (for langSpread=50000) and in 104 iterations (for langSpread=1), and for the first iterations the distance kept growing. Although it may look like something is wrong, this is the expected behavior. Having many remote points forces the kernels to shift quite a bit and with each shift the effects ripple to other kernels, which also move around, and so on. Be patient, in 44 iterations the distance will drop from over 100000 to 13, satisfying the convergence condition.
If you want to get the results faster, feel free to downsample the data (each iteration is faster, but it still takes around 40 steps to converge):
val scored = scoredPostings(grouped).sample(true, 0.1, 0)
However, keep in mind that we will test your assignment on the full data set. So that means you can downsample for experimentation, but make sure your algorithm works on the full data set when you submit for grading.
langSpread corresponds to how far away are languages from
the clustering algorithm's point of view. For a value of 50000, the languages are
too far away to be clustered together at all, resulting in a clustering that only
takes scores into account for each language (similarly to partitioning the data
across languages and then clustering based on the score). A more interesting (but
less scientific) clustering occurs when
langSpread is set to 1 (we can't
set it to 0, as it loses language information completely), where we cluster according
to the score. See which language dominates the top questions now?
Computing Cluster Details
After the call to kmeans, we have the following code in method
val results = clusterResults(means, vectors) printResults(results)
clusterResults method, which, for each cluster, computes:
- the dominant programming language in the cluster;
- the percent of answers that belong to the dominant language;
- the size of the cluster (the number of questions it contains);
- the median of the highest answer scores.
Once this value is returned, it is printed on the screen by the
- Do you think that partitioning your data would help?
- Have you thought about persisting some of your data? Can you think of why persisting your data in memory may be helpful for this algorithm?
- Of the non-empty clusters, how many clusters have “Java” as their label (based on the majority of questions, see above)? Why?
- Only considering the “Java clusters”, which clusters stand out and why?
- How are the “C# clusters” different compared to the “Java clusters”?