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日期:2020-07-26 10:53

COMP9313 (20T2) ASSIGNMENT 1

Q1. HDFS (30 Marks)

Let N be the number of DataNodes and R be the total number of blocks in the

DataNodes.

Assume the replication factor is 5, and k out of N DataNodes have failed

simultaneously.

1. Write down the formula of Li(k, N) for i ∈ {1, . . . , 5}, where Li(k, N) is

the number of blocks that have lost i replicas.

2. Let N = 500, R = 20, 000, 000, and k = 200. Compute the number of

blocks that cannot be recovered under this scenario. You need to show

both the steps and the final result to get full credit.

Q2. Spark (35 Marks)

Consider the following PySpark code snippet:

raw_data = [("Joseph", "Maths", 83), ("Joseph", "Physics", 74),

("Joseph", "Chemistry", 91), ("Joseph", "Biology", 82),

("Jimmy", "Maths", 69), ("Jimmy", "Physics", 62),

("Jimmy", "Chemistry", 97), ("Jimmy", "Biology", 80),

("Tina", "Maths", 78), ("Tina", "Physics", 73),

("Tina", "Chemistry", 68), ("Tina", "Biology", 87),

("Thomas", "Maths", 87), ("Thomas", "Physics", 93),

("Thomas", "Chemistry", 91), ("Thomas", "Biology", 74)]

rdd_1 = sc.parallelize(raw_data)

rdd_2 = rdd_1.map(lambda x:(x[0], x[2]))

rdd_3 = rdd_2.reduceByKey(lambda x, y:max(x, y))

rdd_4 = rdd_2.reduceByKey(lambda x, y:min(x, y))

rdd_5 = rdd_3.join(rdd_4)

rdd_6 = rdd_5.map(lambda x: (x[0], x[1][0]+x[1][1]))

rdd_6.collect()

1. Write down the expected output of the above code snippet.

1

2. List all the stages in the above code snippet.

3. What makes the above implmentation inefficient? How would you modify

the code and improve the performance?

Q3: LSH (35 marks)

Consider a database of N = 1, 000, 000 images. Each image in the database is

pre-processed and represented as a vector o ∈ Rd

. When a new image comes as

a query, it is also processed to form a vector q ∈ Rd

. We now want to check if

there is any duplicates or near duplicates of q in the database. Specifically, an

image o is a near duplicate to q if cos(θ(o, q)) ≥ 0.9. We want to find any near

duplicate with probability no less than 99%.

We now design an LSH scheme using SimHash to generate candidate near

duplicates. Assume that for query q, there are 100 images that are near duplicate

to q.

1. Assume k = 5, how many tables does the LSH scheme require (i.e., L) to

ensure that we can find any near duplicate with probability no less than

99%?

2. Consider image o with cos(θ(o, q)) < 0.8, k = 5 and L = 10. What is the

maximum value of the probability of o to become a false positive of query

q?

You need to show the intermediate steps along with the final result to get

full credit.

Submission

Please write down your answers in a file named ass1.pdf. You must write

down your name and student ID on the first page. You should typeset your

answers in LATEX or MS Word. We do not accept handwritten answers.

You can submit your file using the command:

give cs9313 ass1 ass1.pdf

Late Penalty. We DO NOT ACCEPT LATE SUBMISSIONS (0 mark if

you do not submit on time).

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