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INFS3208 – Cloud Computing
创建日期：2024-06-05 14:38:32
标签： INFS3208
Cloud Computing

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INFS3208 – Cloud Computing

Programming Assignment Task III (10 Marks)
Task Description:
In this assignment, you are asked to write a piece of Spark code to count occurrences of verbs in the
collection of Shakespeare. The returned result should be the top 10 verbs that are most
frequently used in Shakespeare’s collection. This assignment is to test your ability to use
transformation and action operations in Spark RDD programming. You will be given the collection
file (shakespeare.txt), a verb list (all_verbs.txt), a verb dictionary file (verb_dict.txt), and the
programming environment (a docker-compose). You can choose either Scala or Python to program in
the Jupyter Notebook. There are some technical requirements in your code submission as follows:
1. You should use an appropriate method to load the files into RDDs. You are NOT allowed to
make changes to the collection file (shakespeare.txt), verb list file (all_verbs.txt), and
verb dictionary file (verb_dict.txt).
2. To accurately count the verbs in the collection, you should use learned RDD operations to pre-
process the text in the collection file:
a. Remove empty lines.
b. Remove punctuations that could attach to the verbs.
E.g., “work,” and “work” will be counted differently, if you DO NOT remove the
punctuation mark.
c. Change the capitalization or case of text
E.g., “WORK”, “Work” and “work” will be counted as three different verbs, if you DO
NOT make all of them in lower case.
3. You should use learned RDD operations to find out used verbs in the collection
(shakespeare.txt) by matching the verbs in the given verb list (all_verbs.txt).
4. A verb can have different forms: present tense, past tense, and future tense
a. E.g., regular verb: “work” - works”, “worked”, and “working”.
b. E.g., irregular verb: “begin” - “begins”, “began”, and “begun”.
c. E.g., linking verb “be” and its various forms, including “is”, “am”, “are”, “was”,
“were”, “being” and “been”.
You should use the learned RDD operations to calculate the occurrences of all the verbs (listed
in the given verb dictionary file) and merge the verbs that have different tenses by using the
learned RDD operations to look up the verb dictionary file (verb_dict.txt).
d. E.g., (work, 100), (works,50), (working,150) à (work, 300).
5. In the final result, you should return the top 10 verbs (in the base form, e.g., work) that are
most frequently used in the collection of Shakespeare.
Preparation:
In this individual coding assignment, you will apply your knowledge of Spark and Spark RDD
Programming (in Lectures 8 & 9). Firstly, you should read Task Description to understand what the
task is and what the technical requirements include. Secondly, you should review all the transformation
and action operations in Lectures 8 & 9. In the Appendix, there are some transformation and action
operations you could use in this assignment. Also, it would be very helpful to practise the RDD
programming activity in Prac 7 (Week 8). Lastly, you need to write the code (Scala or Python) in the
Jupyter Notebook. All technical requirements need to be fully met to achieve full marks.
You can either practise on the GCP’s VM or your local machine with Oracle Virtualbox if you are
unable to access GCP. Please read the Example of writing Spark code below to have more details.
Assignment Submission:
§ You need to compress the Jupyter Notebook file.
§ The name of the compressed file should be named “FirstName_LastName_StudentNo.zip”.
§ You must make an online submission to Blackboard before 1:00 PM 13/10/2023.
§ Only one extension application could be approved due to medical conditions.
Example of writing Spark code:
Step 1:
Log in your VM and change to your home directory
Step 2:
Download docker-compose.yml and the data you required (shakespeare.txt, all_verbs.txt,
verb_dict.txt).
git clone https://github.com/csenw/cca3.git && cd cca3
sudo chmod -R 777 nbs/
Step 3:
Run all the containers: docker-compose up -d
Step 4:
Open the Jupyter Notebook (EXTERNAL_IP:8888) and write the Spark code in it.
Step 5:
Use the learned method to load external files (shakespeare.txt, all_verbs.txt, verb_dict)
into RDDs.
Output samples:
shakespeare.txt
all_verbs.txt
verb_dict.txt
Step 6:
Use learned RDD operations to pre-process the RDD that stores the text:
1. Remove empty lines
2. Remove punctuations that could attach to the verbs
3. Change the capitalization or case of the text
Output sample:
Step 7:
Use learned RDD operations to keep all the verbs according to the all_verbs.txt.
Output sample:
Step 8:
Use learned RDD operations to count the occurrences of the kept verbs:
Output sample:
Step 9:
Use learned RDD operations to merge the verb pairs that are from the same verb. E.g. (work, 100),
(works,50), (working,150) à (work, 300).
Output sample:
Step 10:
Use learned RDD operations to return the top 10 that are most frequently used in the collection of
Shakespeare.
Output sample:
Note that Steps 5 -10 are just recommended procedures, you can feel free to use your processing
steps. However, your result should reasonably reflect the top 10 verbs that are most frequently
used in the collection of Shakespeare.
Appendix:
Transformations:
Transformation Meaning
map(func) Return a new distributed dataset formed by passing each element of
the source through a function func.
filter(func) Return a new dataset formed by selecting those elements of the
source on which funcreturns true.
flatMap(func) Similar to map, but each input item can be mapped to 0 or more
output items (so funcshould return a Seq rather than a single item).
union(otherDataset) Return a new dataset that contains the union of the elements in the
source dataset and the argument.
intersection(otherDataset) Return a new RDD that contains the intersection of elements in the
source dataset and the argument.
distinct([numPartitions])) Return a new dataset that contains the distinct elements of the
source dataset.
groupByKey([numPartitions]) When called on a dataset of (K, V) pairs, returns a dataset of (K,
Iterable) pairs.
Note: If you are grouping in order to perform an aggregation (such as
a sum or average) over each key,
using reduceByKey or aggregateByKey will yield much better
performance.
Note: By default, the level of parallelism in the output depends on
the number of partitions of the parent RDD. You can pass an
optional numPartitions argument to set a different number of tasks.
reduceByKey(func,
[numPartitions])
When called on a dataset of (K, V) pairs, returns a dataset of (K, V)
pairs where the values for each key are aggregated using the given
reduce function func, which must be of type (V,V) => V. Like
in groupByKey, the number of reduce tasks is configurable through
an optional second argument.
sortByKey([ascending],
[numPartitions])
When called on a dataset of (K, V) pairs where K implements
Ordered, returns a dataset of (K, V) pairs sorted by keys in ascending
or descending order, as specified in the
boolean ascending argument.
join(otherDataset,
[numPartitions])
When called on datasets of type (K, V) and (K, W), returns a dataset
of (K, (V, W)) pairs with all pairs of elements for each key. Outer joins
are supported through leftOuterJoin, rightOuterJoin,
and fullOuterJoin.
Actions:
Action Meaning
reduce(func) Aggregate the elements of the dataset using a function func (which
takes two arguments and returns one). The function should be
commutative and associative so that it can be computed correctly in
parallel.
collect() Return all the elements of the dataset as an array at the driver
program. This is usually useful after a filter or other operation that
returns a sufficiently small subset of the data.
count() Return the number of elements in the dataset.
first() Return the first element of the dataset (similar to take(1)).
take(n) Return an array with the first n elements of the dataset.
countByKey() Only available on RDDs of type (K, V). Returns a hashmap of (K, Int)
pairs with the count of each key.
foreach(func) Run a function func on each element of the dataset. This is usually
done for side effects such as updating an Accumulator or
interacting with external storage systems.
Note: modifying variables other than Accumulators outside of
the foreach() may result in undefined behaviour. See Understanding
closures for more details.
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