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Creative Commons Catalog

Mapping the commons towards an open ledger and cc search.

Description

This repository contains the methods used to identify over 1.4 billion Creative Commons licensed works. The challenge is that these works are dispersed throughout the web and identifying them requires a combination of techniques. Two approaches are currently explored:

  1. Web crawl data
  2. Application Programming Interfaces (API Data)

Web Crawl Data

The Common Crawl Foundation provides an open repository of petabyte-scale web crawl data. A new dataset is published at the end of each month comprising over 200 TiB of uncompressed data.

The data is available in three file formats:

  • WARC (Web ARChive): the entire raw data, including HTTP response metadata, WARC metadata, etc.
  • WET: extracted plaintext from each webpage.
  • WAT: extracted html metadata, e.g. HTTP headers and hyperlinks, etc.

For more information about these formats, please see the Common Crawl documentation.

CC Catalog uses AWS Data Pipeline service to automatically create an Amazon EMR cluster of 100 c4.8xlarge instances that will parse the WAT archives to identify all domains that link to creativecommons.org. Due to the volume of data, Apache Spark is used to streamline the processing. The output of this methodology is a series of parquet files that contain:

  • the domains and its respective content path and query string (i.e. the exact webpage that links to creativecommons.org)
  • the CC referenced hyperlink (which may indicate a license),
  • HTML meta data in JSON format which indicates the number of images on each webpage and other domains that they reference,
  • the location of the webpage in the WARC file so that the page contents can be found.

The steps above are performed in ExtractCCLinks.py.

API Data

Apache Airflow is used to manage the workflow for various API ETL jobs which pull and process data from a number of open APIs on the internet.

Common API Workflows

The Airflow DAGs defined in common_api_workflows.py manage daily ETL jobs for the following platforms, by running the linked scripts:

Other Daily API Workflows

Airflow DAGs, defined in their own files, also run the following scripts daily:

In the future, we'll migrate to the latter style of Airflow DAGs and accompanying Provider API Scripts.

Monthly Workflow

The Airflow DAG defined in monthlyWorkflow.py handles the monthly jobs that are scheduled to run on the 15th day of each month at 16:00 UTC. This workflow is reserved for long-running jobs or APIs that do not have date filtering capabilities so the data is reprocessed monthly to keep the catalog updated. The following tasks are performed:

DB_Loader

The Airflow DAG defined in loader_workflow.py runs every minute, and loads the oldest file which has not been modified in the last 15 minutes into the upstream database. It includes some data preprocessing steps.

Other API Jobs (not in the workflow)

Development setup for Airflow and API puller scripts

There are a number of scripts in the directory src/cc_catalog_airflow/dags/provider_api_scripts eventually loaded into a database to be indexed for searching on CC Search. These run in a different environment than the PySpark portion of the project, and so have their own dependency requirements.

Development setup

You'll need docker and docker-compose installed on your machine, with versions new enough to use version 3 of Docker Compose .yml files.

To set up environment variables, navigate to the src/cc_catalog_airflow directory, and run

cp env.template .env

If needed, fill in API keys or other secrets and variables in .env. This is not needed if you only want to run the tests. There is a docker-compose.yml provided in the src/cc_catalog_airflow directory, so from that directory, run

docker-compose up -d

This results, among other things, in the following running containers:

  • cc_catalog_airflow_webserver_1
  • cc_catalog_airflow_postgres_1

and some networking setup so that they can communicate. Note:

  • cc_catalog_airflow_webserver_1 is running the Apache Airflow daemon, and also has a few development tools (e.g., pytest) installed.
  • cc_catalog_airflow_postgres_1 is running PostgreSQL, and is setup with some databases and tables to emulate the production environment. It also provides a database for Airflow to store its running state.
  • The directory containing the DAG files, as well as dependencies will be mounted to the usr/local/airflow/dags directory in the container cc_catalog_airflow_webserver_1.

At this stage, you can run the tests via:

docker exec cc_catalog_airflow_webserver_1 /usr/local/airflow/.local/bin/pytest

Edits to the source files or tests can be made on your local machine, then tests can be run in the container via the above command to see the effects.

If you'd like, it's possible to login to the webserver container via

docker exec -it cc_catalog_airflow_webserver_1 /bin/bash

It's also possible to attach to the running command process of the webserver container via

docker attach --sig-proxy=false cc_catalog_airflow_webserver_1

Attaching in this manner lets you see the output from both the Airflow webserver and scheduler, which can be useful for debugging purposes. To leave the container, (but keep it running), press Ctrl-C on *nix platforms

To see the Airflow web UI, point your browser to localhost:9090.

If you'd like to bring down the containers, run

docker-compose down

from the src/cc_catalog_airflow directory.

To reset the test DB (wiping out all databases, schemata, and tables), run

docker-compose down
rm -r /tmp/docker_postgres_data/

PySpark development setup

Prerequisites

JDK 9.0.1
Python 3.6
Pytest 4.3.1
Spark 2.2.1
Airflow 1.10.4

pip install -r requirements.txt

Running the tests

python -m pytest tests/test_ExtractCCLinks.py

Authors

See the list of contributors who participated in this project.

License