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https://github.com/seattleflu/id3c

Data logistics system enabling real-time pathogen surveillance. Built for the Seattle Flu Study.
https://github.com/seattleflu/id3c

cli data-distribution etl fhir id3c infectious-disease plpgsql postgres postgresql python redcap rest-api scan-study seattle-flu-study sqitch sql

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Data logistics system enabling real-time pathogen surveillance. Built for the Seattle Flu Study.

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README 

        
# ID3C: Infectious Disease Data Distribution Center



Data logistics system enabling real-time genomic epidemiology. Built for the

[Seattle Flu Study](https://seattleflu.org).



## Navigation

* [Database](#database)

* [Web API](#web-api)

* [CLI](#cli)

* [Development Setup](#development-setup)



## Database



Currently [PostgreSQL 15](https://www.postgresql.org/about/news/postgresql-15-released-2526/).



Initially aims to provide:



* Access via SQL and [REST APIs](#web-api), with

  [PostgREST](http://postgrest.org) and/or ES2018 web app to maybe come later



* De-identified metadata for participants (age, sex, address token, etc.) and

  samples (tissue, date, location, etc.)



* Sample diagnostic results (positive/negative for influenza, RSV, and more)



* Sequencing read sets and genome assemblies stored in the cloud and referenced

  via URLs in database



* Rich data types (key/value, JSON, geospatial, etc)



* Strong data integrity and validation controls



* Role-based authentication and restricted data fields using row and

  column-level access control



* Encrypted at rest and TLS-only connections



* Administration via standard command-line tools (and maybe later

  [pgAdmin4](https://www.pgadmin.org/))



### Design



The database is designed as a [distribution center][] which receives data from

external providers, repackages and stores it in a data warehouse, and ships

data back out of the warehouse via views, web APIs, and other means.  Each of

these three conceptual areas are organized into their own PostgreSQL schemas

within a single database.



The "receiving" area contains tables to accept minimally- or un-controlled data

from external providers.  The general expectation is that most tables here are

logs ([in the journaling sense][the log]) and will be processed later in

sequential order.  For example, participant enrollment documents from our

consent and questionnaire app partner, Audere, are stored here when received by

our web API.



The "warehouse" area contains a hybrid relational + document model utilizing

standard relational tables that each have a JSON column for additional details.

Data enters the warehouse primarily through extract-transform-load (ETL)

routines which process received data and copy it into suitable warehouse rows

and documents.  These ETL routines are run via `bin/id3c etl` subcommands, where

they're defined in Python (though lean heavily on Pg itself).



The "shipping" area contains views of the warehouse designed with specific data

consumers and purposes in mind, such as the incidence modeling team.



While the receiving and shipping areas are expected to be fairly fluid and

reactive to new and changing external requirements, the warehouse area is

expected to change at a somewhat slower pace informed by longer-term vision for

it.



[distribution center]: https://en.wikipedia.org/wiki/Distribution_center

[the log]: https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying



### Guidelines



General principles to follow when developing the schema.



* Do the ~simplest thing that meets our immediate needs.  Aim for ease of

  modification in the future rather than trying to guess future needs in

  advance.



  It can be really hard to stick to this principle, but it turns out that the

  best way to make something flexible for future needs is to make it as simple

  as possible now so it can be modified later.



* Columns should be maximally typed and constrained, unless there exists a

  concrete use case for something less.



* Consider if a column should ever be unknown (null).



* Consider if a column should have a default.



* Consider what constraints make sense at both a column- and table-level.

  Would a `CHECK` constraint be useful to express domain logic?



* Write a description (comment) for all schemas, tables, columns, etc.



* Grant only the minimal privileges necessary to the read-only and read-write

  roles.  For example, if the read-write role isn't expected to `UPDATE`

  existing records, then only grant it `INSERT`.



* Consider expected data access patterns and create indexes to match.



### Integration with other data systems



Although we're building our own data system, we want to design and create it

with interoperability in mind.  To this extent, our system should adopt or

parallel practices and terminology from other systems when appropriate.

For example:



* Nouns (tables, columns, etc) in our system should consider adopting the

  equivalent terms used by [FHIR R4](http://www.hl7.org/implement/standards/fhir/)

  resources.



  This will aid with producing FHIR documents in the future and provides a

  consistent terminology on which to discuss concepts more broadly than our

  group.  FHIR is a large specification and there is a lot to digest; it's

  easy to be daunted or confused by it, so please don't hesitate to ask

  questions.



* Value vocabulary (specimen types, organism names, diagnostic tests, etc)

  should consider using and/or referencing the preferred terms from an

  appropriate ontology like

  [SNOMED CT](https://www.snomed.org/snomed-ct/why-snomed-ct),

  [LOINC](https://loinc.org),

  or [GenEpiO](https://genepio.org/).



### Deploying



The database schema is deployed using [Sqitch](https://sqitch.org), a database

change management tool that really shines.  You can install it a large number

of ways, so pick the one that makes most sense to you.



### Development



For development, you'll need a PostgreSQL server and superuser credentials for

it.  The following commands assume the database server is running locally and

your local user account maps directly to a database superuser.



Create a database named `seattleflu` using the standard Pg tools.  (You can use

another name if you want, maybe to have different dev instances, but you'll

need to adjust the [sqitch target][] you deploy to.)



    createdb --encoding=UTF-8 seattleflu



Then use `sqitch` to deploy to it.  (`dev` is a [sqitch target][] configured in

_sqitch.conf_ which points to a local database named `seattleflu`.)



    sqitch deploy dev



Now you can connect to it for interactive use with:



    psql seattleflu



### Testing and production



Our [testing and production databases][databases doc] are configured as

`testing` and `production` sqitch targets.  When running sqitch against these

targets, you'll need to provide a username via `PGUSER` and a password via an

entry in _~/.pgpass_.



[sqitch target]: https://metacpan.org/pod/distribution/App-Sqitch/lib/sqitch-target.pod

[databases doc]: https://github.com/seattleflu/documentation/blob/master/infrastructure.md#databases-postgresql



## Web API



Python 3 + [Flask](http://flask.pocoo.org)



* Consumes and stores enrollment documents from the Audere backend systems



### Config



* Database connection details are set entirely using the [standard libpq

  environment variables](https://www.postgresql.org/docs/current/libpq-envars.html),

  such as `PGHOST` and `PGDATABASE`. You may provide these when starting the

  API server.



  User authentication is performed against the database for each request, so

  you do not (and should not) provide a username and password when starting the

  API server.



* The maximum accepted Content-Length defaults to 20MB.  You can override this

  by setting the environment variable `FLASK_MAX_CONTENT_LENGTH`.



* The `LOG_LEVEL` environment variable controls the level of terminal output.

  Levels are strings: `debug`, `info`, `warning`, `error`.



### Starting the server



The commands `pipenv run python -m id3c.api` or `pipenv run flask run`

will run the application's __development__ server. To provide database

connection details while starting the development server, run the command

`PGDATABASE=DB_NAME pipenv run flask run`, substituting `DB_NAME` with the name

of your database.



For production, a standard `api.wsgi` file is provided which can be used by any

web server with WSGI support.



### Examples



User authentication must be provided when making POST requests to the API. For

example, you can run the following `curl` command to send JSON data named

`enrollments.json` to the `/enrollment` endpoint on a local development server:



```sh

curl http://localhost:5000/enrollment \

  --header "Content-Type: application/json" \

  --data-binary @enrollments.json \

  --user USERNAME

```



Substitute your own local database username for `USERNAME`.  This will prompt

you for a password; you can also specify it directly by using `--user

"USERNAME:PASSWORD"`, though be aware it will be saved to your shell history.



## CLI



Python 3 + [click](https://click.palletsprojects.com)



Interact with the database on the command-line in your shell to:



* Mint identifiers and barcodes



* Run ETL routines, e.g. enrollments, to process received data into the

  warehouse



* Parse, diff, and upload sample manifests.



* Preprocess clinical data and upload it into receiving.



* Send Slack notifications from the [Reportable Conditions Notifications Slack

  App](https://api.slack.com/apps/ALJJAQGKH)



The `id3c` command is the entry point.  It must be run within the project

environment, for example by using `pipenv run id3c`.



The `LOG_LEVEL` environment variable controls the level of terminal output.

Levels are strings: `debug`, `info`, `warning`, `error`.



## Development setup



### Dependencies



Python dependencies are managed using [Pipenv](https://pipenv.readthedocs.io).



Install all the (locked, known-good) dependencies by running:



    pipenv sync



Add new dependencies to `setup.py` and run:



    pipenv lock

    pipenv sync



and then commit the changes to `Pipfile` and `Pipfile.lock`.



### Connection details



Details for connecting to the ID3C database are by convention controlled

entirely by the [standard libpq environment variables](https://www.postgresql.org/docs/current/libpq-envars.html),

[service definitions](https://www.postgresql.org/docs/current/libpq-pgservice.html),

and [password files](https://www.postgresql.org/docs/current/libpq-pgpass.html).



For example, if you want to list the identifier sets available in the Seattle

Flu Study testing database, you could create the following files:



_~/.pg\_service.conf_



    [seattleflu-testing]

    host=testing.db.seattleflu.org

    user=your_username

    dbname=testing



_~/.pgpass_



    testing.db.seattleflu.org:5432:*:your_username:your_password



Make sure the _~/.pgpass_ file is only readable by you since it contains your

password:



    chmod u=rw,og= ~/.pgpass



and then run:



    PGSERVICE=seattleflu-testing pipenv run bin/id3c identifier set ls



These files will also allow you to connect using `psql`:



    psql service=seattleflu-testing



### Tests



Run all tests with:



    pipenv run pytest -v



Run just type-checking tests with:



    ./dev/mypy