Foundations and Applications of Data Mining Assignment 1

DSCI 553 Foundations and Applications of Data Mining
Assignment 1
1. Overview of the Assignment
In assignment 1, you will work on three tasks. The goal of these tasks is to get you familiar with Spark
operation types (e.g., transformations and actions) and explore a real-world dataset: Yelp dataset
(https://www.yelp.com/dataset).
If you have questions about the assignment, please ask on Piazza, this helps promote interactions
amongst students and will also serve as an FAQ for other students facing similar problems.
You have to submit your assignments on Vocareum directly.
2. Requirements
2.1 Programming Requirements
a. You must use Python to implement all tasks. You can only use standard python libraries (i.e., external
libraries like numpy or pandas are not allowed) because that is sufficient for this programming
assignment. There will be a 10% bonus for each task if you also submit a Scala implementation and both
your Python and Scala implementations are correct.
b. You are required to only use Spark RDD in order to understand Spark operations. You will not get any
points if you use Spark DataFrame or DataSet.
c. Python standard library set : https://docs.python.org/3/library/
2.2 Programming Environment
Python 3.6, JDK 1.8, Scala 2.12, and Spark 3.1.2
We will use these library versions to compile and test your code. There will be no points granted if we
cannot run your code on Vocareum.
On Vocareum, you can call `spark-submit` located at
`/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit`. (Do not use the one at
/usr/local/bin/spark-submit (2.3.0)). We use `--executor-memory 4G --driver-memory 4G` on Vocareum
for grading.
2.3 Write your own code
Do not share code with other students!!
For this assignment to be an effective learning experience, you must write your own code! We
emphasize this point because you will be able to find Python implementations of some of the required
functions on the web. Please do not look for or at any such code!
TAs will combine all the code that can be found from the web (e.g., Github) as well as other students’
code from this and other (previous) sections for plagiarism detection. We will report all detected
plagiarism and severe penalties will be given for the students whose submissions are plagiarized.
2.4 What you need to turn in
We will grade all submissions on Vocareum. Vocareum produces a submission report after you click the
“Submit” button (It takes a while since Vocareum needs to run your code in order to generate the
report). Vocareum will only grade Python scripts during the submission phase and it will grade both
Python and Scala during the grading phase.
a. [REQUIRED] three Python scripts, named: (all lowercase)
task1.py, task2.py, task3.py
b1. [OPTIONAL, REQUIRED FOR SCALA] three Scala scripts and the output jar file, named: (all lowercase)
hw1.jar, task1.scala, task2.scala, task3.scala
c. You don’t need to include your results or the datasets. We will grade your code with our testing data
(data will be in the same format).
d. Students can submit an unlimited number of times. Only the latest submission will be accepted and
graded.
3. Yelp Data
In this assignment, you will explore the Yelp dataset. You can find the data on Vocareum under
resource/asnlib/publicdata/. The two files business.json and test_review.json are the two files you will
work on for this assignment, and they are subsets of the original Yelp Dataset. The submission report you
get from Vocareum is for the subsets. For grading, we will use the files from the original Yelp dataset
which is SIGNIFICANTLY larger (e.g. review.json can be 5GB). You should make sure your code works well
on large datasets as well.
4. Tasks
4.1 Task1: Data Exploration (3 points)
You will work on test_review.json, which contains the review information from users, and write a
program to automatically answer the following questions:
A. The total number of reviews (0.5 point)
B. The number of reviews in 2018 (0.5 point)
C. The number of distinct users who wrote reviews (0.5 point)
D. The top 10 users who wrote the largest numbers of reviews and the number of reviews they wrote
(0.5 point)
E. The number of distinct businesses that have been reviewed (0.5 point)
F. The top 10 businesses that had the largest numbers of reviews and the number of reviews they had
(0.5 point)
Input format: (we will use the following command to execute your code)
Python:
/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit --executor-memory 4G --driver-memory 4G
task1.py <review_filepath> <output_filepath>
Scala:
spark-submit --class task1 --executor-memory 4G --driver-memory 4G hw1.jar <review_filepath>
<output_filepath>
Output format:
IMPORTANT: Please strictly follow the output format since your code will be graded automatically.
a. The output for Questions A/B/C/E will be a number. The output for Questions D/F will be a list, which
is sorted by the number of reviews in the descending order. If two user_ids/business_ids have the same
number of reviews, please sort the user_ids /business_ids in the lexicographical order.
b. You need to write the results in the JSON format file. You must use exactly the same tags (see the red
boxes in Figure 2) for answering each question.
Figure 1: JSON output structure for task1
4.2 Task2: Partition (2 points)
Since processing large volumes of data requires performance optimizations, properly partitioning the
data for processing is imperative.
In this task, you will show the number of partitions for the RDD used for Task 1 Question F and the
number of items per partition.
Then you need to use a customized partition function to improve the performance of map and reduce
tasks. A time duration (for executing Task 1 Question F) comparison between the default partition and
the customized partition (RDD built using the partition function) should also be shown in your results.
Hint:
Certain operations within Spark trigger an event known as the shuffle. The shuffle is Spark’s mechanism
for redistributing data so that it’s grouped differently across partitions. This typically involves copying
data across executors and machines, making the shuffle a complex and costly operation. So, trying to
design a partition function to avoid the shuffle will improve the performance a lot.
Input format: (we will use the following command to execute your code)
Python:
/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit --executor-memory 4G --driver-memory 4G
task2.py <review_filepath> <output_filepath> <n_partition>
Scala:
spark-submit --class --executor-memory 4G --driver-memory 4G task2 hw1.jar <review_filepath>
<output_filepath> <n_partition>
Output format:
A. The output for the number of partitions and execution time will be a number. The output for the
number of items per partition will be a list of numbers.
B. You need to write the results in a JSON file. You must use exactly the same tags.
C. Do not round off the execution times.
Figure 3: JSON output structure for task2
4.3 Task3: Exploration on Multiple Datasets (2 points)
In task3, you are asked to explore two datasets together containing review information
(test_review.json) and business information (business.json) and write a program to answer the following
questions:
A. What are the average stars for each city? (1 point)
1. (DO NOT use the stars information in the business file).
2. (DO NOT discard records with empty “city” field prior to aggregation - this just means that you
should not worry about performing any error handling, input data cleanup or handling edge case
scenarios).
3. (DO NOT perform any round off for the average stars).
B. You are required to compare the execution time of using two methods to print top 10 cities with
highest average stars. Please note that this task – (Task 3(B)) is not graded. You will get full points only if
you implement the logic to generate the output file required for this task.
1. To evaluate the execution time, start tracking the execution time from the point you load the file.
For M1: execution time = loading time + time to create and collect averages, sort using Python
and print the first 10 cities.
For M2: execution time = loading time + time to create and collect averages, sort using Spark
and print the first 10 cities.
The loading time will stay the same for both methods, the idea is to compare the overall
execution time for both methods and understand which method is more efficient for an
end-to-end solution.
Please note that for Method 1, only sorting is to be done in Python. Creating and collecting
averages needs to be done via RDD.
You should store the execution time in the json file with the tags “m1” and “m2”.
2. Additionally, add a “reason” field and provide a hard-coded explanation for the observed
execution times.
3. Do not round off the execution times.
Input format: (we will use the following command to execute your code)
Python: /opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit --executor-memory 4G --driver-memory
4G task3.py <review_filepath> <business_filepath> <output_filepath_question_a>
<output_filepath_question_b>
Scala: spark-submit --class task3 --executor-memory 4G --driver-memory 4G hw1.jar <review_file>
<business_file> <output_filepath_question_a> <output_filepath_question_b>
Output format:
a. You need to write the results for Question A as a text file. The header (first line) of the file is
“city,stars”. The outputs should be sorted by the average stars in descending order. If two cities have the
same stars, please sort the cities in the lexicographical order. (see Figure 3 left).
b. You also need to write the answer for Question B in a JSON file. You must use exactly the same tags
for the task.
Figure 3: Question A output file structure (left) and JSON output structure (right) for task3
5. Grading Criteria
(% penalty = % penalty of possible points you get)
1. You can use your free 5-day extension separately or together https://forms.gle/edH8jw1mJjrLFRcm8
1. This form will record the number of late days you use for each assignment. We will not
count late days if no request is submitted.
1. There will be a 10% bonus if you use both Scala and Python and get expected results.
2. We will combine all the codes we can find from the web (e.g., Github) as well as other students’
code from this and other (previous) sections for plagiarism detection. If plagiarism is detected, there
will be no point for the entire assignment and we will report all detected plagiarism.
3. All submissions will be graded on the Vocareum. Please strictly follow the format provided,
otherwise you can’t get the point even though the answer is correct. You are encouraged to try out
your code on Vocareum terminal.
4. We will grade both the correctness and efficiency of your implementation. The efficiency is
evaluated by processing time and memory usage. The maximum memory allowed to use is 4G, and
maximum processing time is 1800s for grading. The datasets used for grading are larger than the
ones that you use for doing the assignment. You will get *% penalty if your implementation cannot
generate correctness outputs for large files using 4G memory within the 1800s. Therefore, please
make sure your implementation is efficient to process large files.
5. Regrading policy: We can regrade your assignments within seven days once the scores are released.
Regrading requests will not be accepted after one week.
6. There will be a 20% penalty for late submission within a week and no point after a week. If you use
your late days, there wouldn’t be a 20% penalty.
7. Only when your results from Python are correct, the bonus of using Scala will be calculated. There is
no partial point for Scala. See the example below:
Example situations
Task Score for Python
Score for Scala
(10% of previous column if correct)
Total
Task1 Correct: 3 points Correct: 3 * 10% 3.3
Task1 Wrong: 0 point Correct: 0 * 10% 0.0
Task1 Partially correct: 1.5 points Correct: 1.5 * 10% 1.65
Task1 Partially correct: 1.5 points Wrong: 0 1.5
6. Common problems causing fail submission on Vocareum/FAQ
(If your program runs successfully on your local machine but fail on Vocareum, please check these)
1. Try your program on Vocareum terminal. Remember to set python version as python3.6,
And use the latest Spark
2. Check the input command line formats.
3. Check the output formats, for example, the headers, tags, typos.
4. Check the requirements of sorting the results.
5. Your program scripts should be named as task1.py task2.py etc.
6. Check whether your local environment fits the assignment description, i.e. version, configuration.
7. If you implement the core part in python instead of spark, or implement it with a high time complexity
(e.g. search an element in a list instead of a set), your program may be killed on the Vocareum because it
runs too slow.
8. You are required to only use Spark RDD in order to understand Spark operations more deeply. You will
not get any points if you use Spark DataFrame or DataSet. Don’t import sparksql.
9. Do not use Vocareum for debugging purposes, please debug on your local machine. Vocareum can be
very slow if you use it for debugging.
10. Vocareum is reliable in helping you to check the input and output formats, but its function on
checking the code correctness is limited. It can not guarantee the correctness of the code even with a
full score in the submission report.
11. Some students encounter an error like: the output rate …. has exceeded the allowed value
….bytes/s; attempting to kill the process.
To resolve this, please remove all print statements and set the Spark logging level such that it
limits the logs generated - that can be done using sc.setLogLevel . Preferably, set the log level to either
WARN or ERROR when submitting your code.
7. Running Spark on Vocareum
We're going to use Spark 3.1.2 and Scala 2.12 for the assignments and the competition project. Here are
the things that you need to do on Vocareum and local machine to run the latest Spark and Scala:
On Vocareum:
1. Please select JDK 8 by running the command "export
JAVA_HOME=/usr/lib/jvm/java-1.8.0-openjdk-amd64"
2. Please use the spark-submit command as "/opt/spark/spark-3.1.2-bin-hadoop3.2/bin/spark-submit"
On your local machine:
1. Please download and set up spark-3.1.2-bin-hadoop3.2, the setup steps should be the same as
spark-2.4.4
2. If you use Scala, please update Scala's version to 2.12 on IntelliJ.
8. Tutorials for Spark Installation
Here are some useful links here to help you get started with the Spark installation.
Tutorial for ubuntu: https://phoenixnap.com/kb/install-spark-on-ubuntu
Tutorial for windows:
https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c
Windows Installation without Anaconda (Recommended):
https://phoenixnap.com/kb/install-spark-on-windows-10
Tutorial for mac: https://medium.com/beeranddiapers/installing-apache-spark-on-mac-os-ce416007d79f
Tutorial for Linux systems:
https://www.tutorialspoint.com/apache_spark/apache_spark_installation.htm
Tutorial for using IntelliJ:
https://medium.com/@Sushil_Kumar/setting-up-spark-with-scala-development-environment-using-intel
lij-idea-b22644f73ef1
Tutorial for Jupyter notebook on Windows:
https://bigdata-madesimple.com/guide-to-install-spark-and-use-pyspark-from-jupyter-in-windows/
Spark 3.1.2 installation
https://archive.apache.org/dist/spark/spark-3.1.2/