Note on commit history: I originally had one github repository for all my assignments and project, I spun out the project folder after starting it for a bit so the commit history has assignment and project commits mixed in. I was pretty good with prefixing the commit messages though so all the relevant project ones should start like "PROJECT ...".

The project directory breakdown is like this:

• All the spark jobs are in the main folder.
• All the file to generate graphs for the reports are in the draw/ folder.
• The web crawler for the most-helpful analysis is in the spider/ folder.
• The web application for interacting with the data is in the web/ folder.
• The CASSANDRA.md has information on cassandra as well as the structure of the database
• Some utility/help files are in utils/

A web front end should be running at http://732.cmplx.ca. This version just has dummy data to demonstrate the bare basics. If it throws a server error it's likely Cassandra stopped running, as of writing this I don't have it configured the best way. I'm also trying to get the full dataset version up and running, but as of writing this it's still processing. If that works out it will be at https://kyle732.localtunnel.me.

The dataset is relatively large (~23GB) and can't be included in git. It was attained from http://jmcauley.ucsd.edu/data/amazon/. None of those download links work, you must request access from the author.

Without the dataset I'm not sure how you can run my project (other than the web front end). I can maybe make subsets of the dataset available on S3, but you wont really get useful results on a subset. My project was designed around the report with several steps of analysis. I'll go through each of step of the report and detail what jobs/files were used:

The first step was to convert the dataset to parquet. This actually reduced the filesize compared to the originall compressed version, while making the dataset easy to query against. save_parquet.py handles this. It also defines the schemas. The metadata json was in a little different format (it used single quotes instead of double, which the parquet importer didn't like, and some other stuff), so I use utils/process_metadata.py to process it first.

Generate a histogram of the overall scores of each review, see draw/draw_score_histogram.py for chart generation. parquet_histogram.py to generate the data. Result is small so just prints it to screen.

Calculate the average score per item, so we can generate a histogram. parquet_aggregate_histogram.py handles this task. It outputs a file (or many files, if we needed) with each item and it's average review score. draw/draw_overall_histogram.py can take this file and use matplotlib to draw the histogram. This file will have ~9 million lines, which is fine for modern computers. If this needed to scale we could move the bin-counts into the spark file, greatly reducing the amount of output of the job.

amz.json are the randomly chosen files. Scraper was written with the Scrapy project.Change to the spider directory, and run like this:

scrapy runspider scrapy_most_helpful.py -o amz.json

Result and drawing of the chart are in draw/helpfulness.py.

balance_rating_normal.py will calculate a users score given their rating counts. calculate_alternate_scores.py is a spark job that will take all the reviews, calculate the alternate score for each user, and then recalculate all the products average score with those weights. We're dealing with a few million products and a few million users, so I output the results as one file for symplicity, but the rest of the code could be adapted to deal with a colletion of files.

draw/draw_ascore_histogram.py and draw/draw_ascore_diff_histogram.py correspond to the two graphs evaluating the overall difference when using alternate scores.

To analyze categories and brands use the spark jobs breakdown_category.py and breakdown_brand.py. draw/print_stats.py is used to analyze the results, which are put in draw/draw_category_breakdown.py and draw_brand_breakdown.py to draw the comparison graphs.

The web front end is a flask application that connects to a Cassandra cluster. Code is in web/. Code to interact with Cassandra is in web/model.py. You can run the web app with:

python application.py

See CASSANDRA.md for info on cassandra. It just assumes cassandra is running locally with default settings

Putting all the data into Cassandra is done with a spark job. It uses the two parquet files and the two files that are generated from calculate_alternate_scores.py with alternate score sna histogram info.

${SPARK_HOME}/bin/spark-submit --packages TargetHolding:pyspark-cassandra:0.2.1 cassandra_upload.py REVIEWS_PARQUET_FILE METADATA_PARQUET_FILE USERS_ASCORES PRODUCT_ASCORES