ADSOrcid

Short summary

ORCID metadata enrichment pipeline - grabs claims from the API and enriches ADS storage/index.

How it works:

1. periodically check ADS API (using a special OAuth token that gives access to ORCID updates)
1. fetches the claims and puts them into the RabbitMQ queue
1. a worker grabs the claim and enriches it with information about the author (querying both
   public ORCID API for the author's name and ADS API for variants of the author name)
1. given the info above, it updates MongoDB (collection orcid_claims) - it marks the claim
   either as 'verified' (if it comes from a user with an account in BBB) or 'unverified'
   
   (it is the responsibility of the ADS Import pipeline to pick orcid claims and send them to
   SOLR for indexing)

Queues

• check-orcidid: compares our stored version of the ORCID works against that of ORCID; records claims with appropriate statuses; sends tasks (individual claims) to record-claim queue
• record-claim: receives single claim; checks existence of bibcode, updates claim with more author information; passes claim to match-claim queue
• match-claim: verifies (or rejects) claims from record-claim, records approved claims; passes approved claims to output-results queue
• output-results: sends results to another pipeline to be incorporated into the record
• check-updates: checks ORCID microservice for updated profiles; if it finds any, sends them to check-orcidid

dev setup - vagrant (docker)

1. vim ADSOrcid/local_config.py #edit, edit
2. vagrant up db rabbitmq app
3. vagrant ssh app
4. cd /vagrant

This will start the pipeline inside the app container - if you have configured endpoints and access tokens correctly, it starts fetching data from orcid.

We are using 'docker' provider (ie. instead of virtualbox VM, you run the processes in docker). On some systems, it is necessary to do: export VAGRANT_DEFAULT_PROVIDER=docker or always specify `--provider docker' when you run vagrant.

The directory is synced to /vagrant/ on the guest.

dev setup - local editing

If you (also) hate when stuff is unnecessarily complicated, then you can also run/develop locally (using whatever editor/IDE/debugger you like)

1. virtualenv python
2. source python/bin/activate
3. pip install -r requirements.txt
4. pip install -r dev-requirements.txt
5. vagrant up db rabbitmq

This will setup python virtualenv and the database + rabbitmq. You can run the pipeline and tests locally.

RabbitMQ

vagrant up rabbitmq

The RabbitMQ will be on localhost:6672. The administrative interface on localhost:25672.

Database

vagrant up db

MongoDB is on localhost:37017, PostgreSQL on localhost:6432

production setup

vagrant up prod

It will automatically download/install the latest release from the github (no, not your local changes - only from github).

If you /ADSOrcid/prod_config.py is available, it will copy and use it in place of local_config.py

No ports are exposed, no SSH access is possible. New releases will deployed automatically.

Typical installation:

1. vim ADSOrcid/prod_config.py # edit, edit...
2. vagrant up prod

production setup - docker way

1. cd manifests/production/app
2. docker build --name ADSOrcid -t ADSOrcid .
3. cd ../../..
4. vim prod_config.py # edit, edit...
5. dockerun -d -v .:/vagrant/ --name ADSOrcid ADSOrcid /sbin/my_init

Here are some useful commands:

• restart service

  docker exec ADSOrcid sv restart app

• tail log from one of the workers

  docker exec ADSOrcid tail -f /app/logs/ClaimsImporter.log

Maintainers

Kelly, Roman