COMP9313 2021T3 Project 3 (20 marks)
Set Similarity Join Using Spark on Google Dataproc
Problem Definition:
Given two collections of records R and S, a similarity function sim(., .), and a
threshold τ, the set similarity join between R and S, is to find all record pairs r (from
R) and s (from S), such that sim(r, s) >= τ.
In this project, you are required to use the Jaccard similarity function to compute
sim(r, s). Given the following example, and set τ=0.5,

the result pairs are (r1, s1) (similarity 0.75), (r2, s2) (similarity 0.5), (r3, s1) (similarity
0.5), (r3, s2) (similarity 0.5).
Input files:
You are required to do the “self-join”, that is, a single input file is given, in which
each line is in format of:
“RecordId list”,
and this file serves as both R and S.
An example input file is as below (integers are separated by space):
0 1 4 5 6
1 2 3 6
2 4 5 6
3 1 4 6
4 2 5 6
5 3 5

This sample file “tiny-data.txt” can be downloaded at:
https://webcms3.cse.unsw.edu.au/COMP9313/21T3/resources/69118
Another sample input file “flickr_small.txt” can be downloaded at:
https://webcms3.cse.unsw.edu.au/COMP9313/21T3/resources/69119
Output:
The output file contains the similar pairs together with their similarities. Each line is
in format of “(RecordId1,RecordId2)\tSimilarity” (RecordId1are no duplicate pairs in the result). The similarities are of double precision. The
pairs are sorted in ascending order (by the first record and then the second).
Given the example input data, the output file is like:
(0,2)\t0.75
(0,3)\t0.75
(1,4)\t0.5
(2,3)\t0.5
(2,4)\t0.5
Code format:
Name your java file as “SetSimJoin.scala” and put it in the package
“comp9313.proj3”. Your program should take three parameters: the input file, the
output folder, and the similarity threshold τ (double precision).
Cluster configuration:
Create a bucket with name “comp9313-” in Dataproc.
Create a folder “project3” in this bucket for holding the input files.
This project aims to let you see the power of distributed computation. Your code
should scale well with the number of nodes used in a cluster. You are required to
create three clusters in Dataproc to run the same job:
• Cluster1 - 1 master node and 2 worker nodes;
• Cluster2 - 1 master node and 4 worker nodes;
• Cluster3 - 1 master node and 6 worker nodes.
For both master and worker nodes, select n1-standard-2 (2 vCPU, 7.5GB memory).
Unzip and upload the following data set to your bucket, and set τ to 0.85 to run your
program: https://webcms3.cse.unsw.edu.au/COMP9313/21T3/resources/69125.
Record the runtime on each cluster and draw a figure where the x-axis is the number
of nodes you used and the y-axis is the time of getting the result, and store this figure
in a file “Runtime.jpg”. Please also take a screenshot of running your program on
Dataproc in each cluster as a proof of the runtime. Compress the three screenshots
into a zip file “Screenshots.zip”. Briefly describe your optimization techniques in a
file “Optimization.pdf”.
Create a project locally in the IDE, test everything in your local computer, and
finally do it in Google Dataproc.
Documentation and code readability
Your source code will be inspected and marked based on readability and ease of
understanding. The efficiency and scalability of this project is very important and
will be evaluated as well. Below is an indicative marking scheme:
Result correctness: 12
Efficiency and Scalability: 6
Code structure, Readability, and
Documentation: 2
Submission:
Deadline: Sun 28th Nov 09:59:59 PM
You can submit through Moodle. You need to submit three files: SetSimJoin.scala,
Screenshots.zip, and Optimization.pdf.
If you submit your assignment more than once, the last submission will replace the
previous one. To prove successful submission, please take a screenshot as
assignment submission instructions show and keep it by yourself. If you have any
problems in submissions, please email to [email protected].
Late submission penalty
10% reduction of your marks for the 1st day, 30% reduction/day for the following
days.
Plagiarism:
The work you submit must be your own work. Submission of work partially or
completely derived from any other person or jointly written with any other person is
not permitted. The penalties for such an offence may include negative marks,
automatic failure of the course and possibly other academic discipline. Assignment
submissions will be examined manually.

Relevant scholarship authorities will be informed if students holding scholarships are
involved in an incident of plagiarism or other misconduct.

Do not provide or show your assignment work to any other person - apart from the
teaching staff of this subject. If you knowingly provide or show your assignment
work to another person for any reason, and work derived from it is submitted you
may be penalized, even if the work was submitted without your knowledge or
consent.

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