Bookdata-tools/README.md

This repository contains the code to import and integrate the book and rating data that we work with. It imports and integrates data from several sources in a single PostgreSQL database; import scripts are primarily in Python, with Rust code for high-throughput processing of raw data files.

If you use these scripts in any published reseaerch, cite our paper:

Michael D. Ekstrand, Mucun Tian, Mohammed R. Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring Author Gender in Book Rating and Recommendation. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys '18). ACM, pp. 242–250. DOI:10.1145/3240323.3240373. arXiv:1808.07586v1 [cs.IR].

Note: the limitations section of the paper contains important information about the limitations of the data these scripts compile. Do not use this data or tools without understanding those limitations. In particular, VIAF's gender information is incomplete and, in a number of cases, incorrect.

We use Data Version Control (dvc) to script the import and wire its various parts together.

Requirements

• PostgreSQL 10 or later with orafce and pg_prewarm (from the PostgreSQL Contrib package) installed.
• Python 3.6 or later with the following packages:
  • psycopg2
  • numpy
  • tqdm
  • pandas
  • numba
  • colorama
  • chromalog
  • humanize
  • dvc
• The Rust compiler (available from Anaconda)
• 2TB disk space for the database
• 100GB disk space for data files

It is best if you do not store the data files on the same disk as your PostgreSQL database.

The environment.yml file defines an Anaconda environment that contains all the required packages except for the PostgreSQL server. It can be set up with:

conda create -f environment.yml

All scripts read database connection info from the standard PostgreSQL client environment variables:

• PGDATABASE
• PGHOST
• PGUSER
• PGPASSWORD

Alternatively, they will read from DB_URL.

Initializing and Configuring the Database

After creating your database, initialize the extensions (as the database superuser):

CREATE EXTENSION orafce;
CREATE EXTENSION pg_prewarm;
CREATE EXTENSION "uuid-ossp";

The default PostgreSQL performance configuration settings will probably not be very effective; we recommend turning on parallelism and increasing work memory, at a minimum.

Downloading Data Files

This imports the following data sets:

• Library of Congress MDSConnect Open MARC Records from https://www.loc.gov/cds/products/MDSConnect-books_all.html.
• LoC MDSConnect Name Authorities from https://www.loc.gov/cds/products/MDSConnect-name_authorities.html.
• Virtual Internet Authority File - get the MARC 21 XML data file from http://viaf.org/viaf/data/.
• OpenLibrary Dump - the editions, works, and authors dumps from https://openlibrary.org/developers/dumps.
• Amazon Ratings - get the 'ratings only' data for Books from http://jmcauley.ucsd.edu/data/amazon/ and save it in data.
• BookCrossing - the BX-Book-Ratings CSV file from http://www2.informatik.uni-freiburg.de/~cziegler/BX/.

Several of these files can be auto-downloaded with the DVC scripts; others will need to be manually downloaded.

Running Everything

You can run the entire import process with:

dvc repro

Individual steps can be run with their corresponding .dvc files.

Layout

The import code consists of Python, Rust, and SQL code, wired together with DVC.

Python Scripts

Python scripts live under scripts, as a Python package. They should not be launched directly, but rather via run.py, which will make sure the environment is set up properly for them.

DVC Usage and Stage Files

In order to allow DVC to be aware of current database state, we use a little bit of an unconventional layout for many of our DVC scripts. Many steps have two .dvc files with associated outputs:

• step.dvc runs import stage step.
• step.transcript is (consistent) output from running step, recording the actions taken. It is registered with DVC as the output of step.dvc.
• step.status.dvc is an always-changed DVC stage that depends on step.transcript and produces step.status, to check the current status in the database of that import stage.
• step.status is an uncached output (so it isn't saved with DVC, and we also ignore it from Git) that is registered as the output of step.status.dvc. It contains a stable status dump from the database, to check whether step is actually in the database or has changed in a meaningful way.

Steps that depend on step then depend on step.status, not step.trasncript.

The reason for this somewhat bizarre layoutis that if we just wrote the output files, and the database was reloaded or corrupted, the DVC status-checking logic would not be ableto keep track of it. This double-file design allows us to make subsequent steps depend on the actual results of the import, not our memory of the import in the Git repository.

The file init.status is an initial check for database initialization, and forces the creation of the meta-structures used for tracking stage status. Everything touching the database should depend on it, directly or indirectly.