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COSC 2637辅导、辅导C++/Java编程

Big Data Processing
COSC 2637/2633
Assignment 4 – HDFS Monitoring via Spark Streaming

Assessment
Type

Individual assignment.
Submit online via Canvas → Assignment 4.
Marks awarded for meeting requirements as closely as possible.
Clarifications/updates may be made via announcements or relevant discussion forums.
Due Date At 23:59, 2 Nov, 2022
Marks 25

Overview
Write Spark programs which gives your chance to apply the essential components you learned in lectures and
labs to understand the complexity of Spark programming.

Learning Outcomes
The key course learning outcomes are:
CLO 1 - Model and implement efficient big data solutions for various application areas using
appropriately selected algorithms and data structures.
CLO 2 - Analyse methods and algorithms, to compare and evaluate them with respect to time and space
requirements and make appropriate design choices when solving real-world problems.
CLO 4 - Explain the Big Data Fundamentals, including the evolution of Big Data, the characteristics of
Big Data and the challenges introduced.
CLO 5 - Apply non-relational databases, the techniques for storing and processing large volumes of
structured and unstructured data, as well as streaming data.
CLO 6 - Apply the novel architectures and platforms introduced for Big data, i.e., Hadoop, MapReduce
and Spark.

Task – Spark Streaming
Develop a spark streaming program with Scala to monitor a folder on HDFS in real-time such that any new
file in the folder will be processed (the batch interval is 5 seconds). The following three tasks are implemented
in the same Scala object:
A. For each RDD of Dstream, count the word frequency and save the output on HDFS. Use regular
expression to make sure that each word consists of characters only (tip: findAllIn()). (5 marks)
B. For each RDD of Dstream, filter out the short words (i.e., < 5characters) and then count the co-occurrence
frequency of words (the words are considered co-occurred if they are in the same line); save the output on
HDFS. (10 marks)
C. For the Dstream, filter out the short words (i.e., < 5 characters) and then count the co-occurrence
frequency of words (the words are considered co-occurred if they are in the same line); save the output on
HDFS. Note you are required to use updateStateByKey operation to continuously update the co-
occurrence frequency of words with new information. (10 marks)

RMIT Classification: Trusted
Format Requirements:
Failure to follow the requirements incurs up to 8 marks penalty
(a) The source codes of three tasks are entailed in submission.
(b) Submit the developed Scala project in a single .zip file with a jar file.
(c) The zip file should be named as sxxxxx_BDP_A4.zip (replace sxxxxx by student ID).
(d) You need include a “README” file in the zip file.
(e) In README, you must specify exactly how to run the jar in AWS EMR platform.
(f) Paths of input and output should not be hard-coded.

Functional Requirements:
Failure to follow the requirements incurs up to 5 marks penalty
(a) For each task, the output on HDFS should be named with a unique sequence number. For example,
taskA-001, taskA-002, taskB-001, taskB-002, taskC-001, taskC-002.
(b) You need create a single Scala project including all three tasks so that they work on the same stream
data.

Submission
Your assignment should follow the requirement below and submit via Canvas > Assignment 4.

Assessment declaration: when you submit work electronically, you agree to the assessment declaration:
https://www.rmit.edu.au/students/student-essentials/assessment-and-exams/assessment/assessment-declaration

Academic integrity and plagiarism (standard warning)
Academic integrity is about honest presentation of your academic work. It means acknowledging the work of
others while developing your own insights, knowledge and ideas. You should take extreme care that you have:
Acknowledged words, data, diagrams, models, frameworks and/or ideas of others you have quoted
(i.e., directly copied), summarized, paraphrased, discussed or mentioned in your assessment through
the appropriate referencing methods,
Provided a reference list of the publication details so your reader can locate the source if necessary.
This includes material taken from Internet sites.
If you do not acknowledge the sources of your material, you may be accused of plagiarism because you have
passed off the work and ideas of another person without appropriate referencing, as if they were your own.

RMIT University treats plagiarism as a very serious offence constituting misconduct. Plagiarism covers a
variety of inappropriate behaviours, including:
Failure to properly document a source
Copyright material from the internet or databases
Collusion between students
For further information on our policies and procedures, please refer to
https://www.rmit.edu.au/students/student-essentials/rights-and-responsibilities/academic-integrity

RMIT Classification: Trusted
Marking Guide

Late submission of the assignment results in penalty of 2 marks for (up to) each 24 hours being late. Submissions more than 5*24 hours late results in
zero marks.
If unexpected circumstances affect your ability to complete the assignment, you can apply for special consideration.
Requests for special consideration of within 7*24 hours please can be via emailing the course coordinator directly with supporting evidence.

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