This assignment tests your ability to implement simple data analytic workload using basic features of MapReduce and Spark framework. In particular, you are encouraged to practice the skills of designing algorithms by organizing data as key value pairs, the concept that is central to both frameworks. The data set you will work on is adapted fromTrending Youtube Video Statisticsdata from Kaggle. There are two workloads you should design and implement against the given data set. You are required to implement one workload with MapReduce and the other workload with Spark.
2 Input Data Set Description
The dataset contains several months’ records of daily top trending YouTube video in the following ten countries: Canada,France, Germany, India,Japan, Mexico, Russia, South Ko- rea, United Kingdom and United States of America. There are up to 200 trending videos listed per day.
In the original data set, each country’s data is stored in a separate CSV file, with each row representing a trending video record. If a video is listed as trending in multiple days, each trending appearance has its own record. The record includes video id, title, trending date, publish time, number of views, and so on. The record also includes a category id field. The categories are slightly different in each country. A JSON file defineing the mapping between category ID and category name is provided for each country.
The following preprocessing have been done to ensure that you can focus on the main workload design.
• Merge the 10 individual CSV files into a single CSV file;
• Add a column category to store the actual category name based on the mapping
• Add a column country to store the trending country, each country is represented by two capital letter code.
• Remove rows with invalid video id values
• Remove textual columns that are not relevant to the workloads and may cause en- coding and parsing issue
The results is a CSV file AllVideos short.csv with mostly numeric and date columns.
3 Analysis Workload Description
3.1 Category and Trending Correlation
Some videos are trending in multiple countries. We are interested to know if there is any correlation between video category and trending popularity among countries. For instance, we all know that “music has an universal appeal”, in the context of Youtube videos, we may expect to see a common set of trending music videos among many countries. On the contrary, we may expect to see each country with a distinctive set of trending political videos.
In this workload, you are asked to find out the average country number for videos in each category. For instance, if in the data set there are five videos belonging to category Sports, their trending data are as follows:
We can see that video 1 appears in 1 country; video 2 appears in 3 countries; video 3, 4 and 5 each appears in 1 country respectively. The average country number for videos in category Sports would be (1+3+1+1+1) = 1.4 The final result of this work load would look like the following:
News & Politics: 1.05
3.2 Controversial Trending Videos Identification
Listing a video as trending would help it attract more views. However, not all trending videos are liked by viewers. It is not unusual for a trending video to have more dislikes than likes; For some video, listing it as trending would increase its dislikes number more than the increase of its likes number. This workload aims to identify such videos.
Below are a few records of a particular video demonstrating the change of various num- bers over time:
The video has multiple trending appearances in US and GB. In both countries, its views, likes and dislikes all increase over time with each trending appearance. As highlighted in the table above, the dislikes number grows much faster than the likes numbers. In both countries, the video ended with higher number of dislikes than likes albeit starting with higher likes number.
In this workload, you are asked to find out the top 10 videos with fastest growth of dislikes number between its first and second trending appearances. Here we mea- sure the growth of dislikes number by the gap of dislikes increase and likes increase between the first two trending appearances in the same country.
For instance, the dislikes growth of video QwZT7T-TXT0 in US is computed as follows:
(1065772− 629120)− (1082422− 835378) = 189608
Where the first component is the increase of dislikes and the second component is the increase of likes between the first and second trending appearances .
The result of this workload should show a few details of the top 10 videos, including the video id, category, dislike growth value and country code. Below is a few sample results:
If a video has changed its category name over time, you can use the category of the first appearance. It is possible to include the same video multiple times in top 10 list if it has large dislikes growth in multiple countries. Video QwZT7T-TXT0 is such an example.
4 Coding and Execution Requirement
Below are requirements on coding and Execution:
• You can implement the workloads in either Java or Python. No other language is allowed.
• You must use MapReduce and Spark framework in your implementation. Imple- mentation using plain language features will not achieve any point. Implementation “pretends” to use the framework will not achieve any point. A typical example of “pretending” to use MapReduce framework is to design the workload as one job, with an identity mapper and a bloated reducer; The identity mapper just pass the input as is to the reducer, which has all the implemented everything in one function.
•Your code must take two parameters: an input path and an output path. The out- put should be written to a file.
• Your code must execute on AWS EMR with emr-5.21.0 software release.
• For Java implementation, a script (e.g. ant build file) must be provided to allow easy creation of the executable jar file. The job submission command must be provided in a read.me file.
• For Python implementation, a shell script must be provided with job submission com- mand.
There are two deliverables: source code and brief report (up to 2 pages). Both are due on Wednesday 10th of April 23:59 (Week 7).
JAVA submission should be organized in the following folder structure and packed as a zip file:
The submitted zip file should be called <labCode>-<uniKey>-<firstNae>-<lastName>.zip.
There will be a demo in week 7 during tutorial time. The tutor will upload all submis-sions on AWS and run your code on EMR cluster during the demo. You are expected to answer design and implementation related questions duing the demo.
Submit a hard copy of your report together with signed cover sheet during the demo.
The report must contain a computation graph for each workload, with very brief de-scriptions. A sample report will be uploaded as reference.