Report ETL Pipeline

About

Report ETL Pipeline is a Dagster pipeline to extract radiological reports (inside SR Modality instances) from a PACS (by using ADIT) and transfer them to RADIS for creating a full-text search index. The pipeline contains two jobs. collect_reports_job collects all reports since the year 2012 (by using Dagster backfills) and also has a schedule to collect reports during the night from the previous day to send them to RADIS. revise_reports_job has a schedule to collect reports from the day 7 days before to collect changed or afterwards added reports and send those to RADIS.

Setup

• Both development and production uses Docker Compose to setup the Dagster server
• dagster_home_dev resp. dagster_home_prod folder in the workspace is mounted as DAGSTER_HOME folder. Every data output by the pipelines is stored in those folders.
• Copy example.env to .env.dev or resp. .env.prod and edit the settings in there.
• Artifacts are stored according to ARTIFACTS_DIR. If ARTIFACTS_DIR is not set then the files are stored in the DAGSTER_HOME folder under storage.
• A relative ARTIFACTS_DIR path is stored relative to DAGSTER_HOME which is dagster_home_dev folder in development and dagster_home_prod folder in production.
• Production uses Nginx for basic auth and SSL encryption.
  • Generate a password file for basic authentication by using htpasswd -c .htpasswd <username> (needs apache2-utils to be installed).
  • Generate SSL certificate with openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout ssl.key -out ssl.crt (nothing has to be filled out)
• Attach the virtual environment with poetry shell and then start the stack with inv compose-up or inv compose-up --env prod.
• Forward port 3500 in development resp. 3600 in production to Dagster UI in VS Code ports tab.
• Alternatively (for testing purposes), run a single job from command line, e.g. python ./scripts/materialize_assets.py -d ./artifacts/ 2023-01-01.