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About this project

The Federal Election Commission (FEC) releases information to the public about money that's raised and spent in federal elections — that's elections for US President, Senate, and House of Representatives.

Are you interested in seeing how much money a candidate raised? Or spent? How much debt they took on? Who contributed to their campaign? The FEC is the authoritative source for that information.

The new FEC.gov aims to make campaign finance information more accessible (and understandable) to all users.

This repository, openFEC, is home to the FEC’s API

All FEC repositories:

Get involved

We welcome you to explore, make suggestions, and contribute to our code.

Set up

We are always trying to improve our documentation. If you have suggestions or run into problems please file an issue!

Project prerequisites

  1. Ensure you have the following requirements installed:

    • Python (the latest 3.10 release, which includes pip and a built-in version of virtualenv called venv).

    • The latest long term support (LTS) or stable release of Node.js (which includes npm)

    • PostgreSQL (the latest 13 release).

    • Elastic Search 7.x (instructions here)

    • Flyway 11.0.0 (homebrew instructions)

      • After downloading, create a .toml file in the following location: flyway-11.0.0/conf/flyway.toml and set the flyway environment variables environment, url, user, password andlocations as

        [environments.local]
url = "jdbc:postgresql://localhost:5432/cfdm_test"
user = "<your local database username>"
password = "<your local database password>"

[flyway]
environment = "local"
locations = ["filesystem:/Users/<user>/<project>/api/openFEC/data/migrations"]

        to enable connection to a local database (e.g., cfdm_test from Create a test database, below) and specify the location of the database migration files (SQL)

    • See Database migrations for more information on installing and configuring flyway

  2. Set up your Node environment— learn how to do this with our Javascript Ecosystem Guide.

  3. Set up your Python environment— learn how to do this with our Python Ecosystem Guide.

  4. Clone this repository.

Project dependencies

Install project requirements

Use pip to install the Python dependencies:

pip install -r requirements.txt
pip install -r requirements-dev.txt

Use npm to install JavaScript dependencies:

nvm use --lts
npm install
npm run build
Git hooks

This repo includes optional post-merge and post-checkout hooks to ensure that dependencies are up to date. If enabled, these hooks will update Python dependencies on checking out or merging changes to requirements.txt. To enable the hooks, run:

invoke add_hooks

To disable, run:

invoke remove_hooks

Create a test database

In order to run tests locally, you'll need a test database.

To create the test database, run:

createdb cfdm_unit_test

Create a development database

Before you can run this project locally, you'll need a development database.

To create the development database, run:

createdb cfdm_test

Set the environment variable SQLA_SAMPLE_DB_CONN to point to this database, using:

export SQLA_SAMPLE_DB_CONN="postgresql://<username>:<password>@localhost:<port - default is 5432>/cfdm_test"

Load our sample data into the development database (cfdm_test) by running:

invoke create_sample_db

This will run flyway migrations on the empty database to create the schema, and then load sample data into this database from data/sample_db.sql.

Connecting to a RDS DB instance instead of local DB

Note: FEC members can set the SQL connection to one of the RDS boxes with:

export SQLA_CONN=<psql:address-to-box>

Warning: never perform 'update all' when pointing to an RDS box via the SQLA_CONN env var

Note: An additional setting for connecting to and utilizing mirrors/replica boxes can also be set with:

export SQLA_FOLLOWERS=<psql:address-to-replica-box-1>[,<psql:address-to-replica-box-2>,...]

Note: This is a comma separated (with no spaces) string that contains one or more connection strings to any replicas/mirrors that are setup.

Reach out to a team member to get the actual addresses for all of these connections.

Set other environment variables

  1. Run:

    export FEC_WEB_DEBUG=true

    This shows error details and more verbose request logging.

  2. Run:

    export FEC_API_URL=http://localhost:5000
export FEC_CMS_URL=http://localhost:8000

    These are the default URLs to the other local FEC applications. For complete set-up instructions, explore our documentation for fec-cms.

    Note: If you modify your local environment to run these applications at a different address, be sure to update these environment variables to match.

  3. If you do not login to CloudFoundry with SSO (single sign-on), run:

    export FEC_WEB_USERNAME=<username_of_your_choosing>
export FEC_WEB_PASSWORD=<password_of_your_choosing>

    Create these account credentials to gain full access to the application. You can set them to any username and password of your choosing.

    Note: FEC team members will also have additional environment variables to set up. Please reach out to a team member for detailed information.

  4. If you are using database replicas/mirrors you can also restrict connections to them to be asynchronous tasks only by running:

    export SQLA_RESTRICT_FOLLOWER_TRAFFIC_TO_TASKS=enabled

Run locally

Follow these steps every time you want to work on this project locally.

  1. Set the FLASK_APP environment variable to specify how to load the application. Flask is used when running the project locally, so you will want to turn the debugger on as well. If you'd like to run in development mode (reloader will trigger whenever your code or imported modules change and debugger is on by default) you can set FLASK_ENV=development. Pyenv-dotenv should set these for you automatically by reading .flaskenv.

    export FLASK_APP=webservices.rest
export FLASK_DEBUG=1

  2. If you are using the legal search portion of the site, you will need Elastic Search running. Navigate to the installation folder (eg., elasticsearch-7.4) and run:

    cd bin
./elasticsearch

  3. Start the web server:

    flask run

    or

    python cli.py run

  4. View your local version of the site at http://localhost:5000.

Task queue

We use Celery to schedule periodic tasks— for example, refreshing materialized views and updating incremental aggregates. We use Redis as the Celery message broker.

To work with Celery and Redis locally, install Redis and start a Redis server. By default, we connect to Redis at redis://localhost:6379; if Redis is running at a different URL, set the FEC_REDIS_URL environment variable.

Note: Both the API and Celery worker must have access to the relevant environment variables and services (PostgreSQL, S3).

Running Redis and Celery locally:

redis-server
celery --app webservices.tasks worker
celery --app webservices.tasks beat

Editing Swagger

We are using a customized 2.x version of swagger-ui to display our API developer documentation.

Template and Swagger spec file

The base template for swagger-ui is located at webservices/templates/swagger-ui.html. The {{ specs_url }} template variable points to http://localhost:5000/swagger that is the swagger spec file for FEC specific model definitions and schema. Compiled and vendor assets are served from static/swagger-ui/.

Custom Swagger setup and build

The swagger-ui package is within the swagger-ui directory. The hbs folder contains handlebars templates with customizations, as do the files contained in the js and less folders. However, the js/swagger-client.js is the base v2.1.32 swager-ui file.

All these files are then built and compiled via the npm run build command that runs Gulp tasks. Any modification should be done in the files in swagger-ui that will then be compiled and served in the static/swagger-ui/ folder.

Testing

This repo uses pytest.

If the test database server is not the default local Postgres instance, indicate it using:

export SQLA_TEST_CONN=<psql:address-to-box>

The connection URL has to strictly adhere to the structure postgresql://<username>:<password>@<hostname>:<port>/<database_name>. Note that the database_name should be specified explicitly, unlike URLs for SQLAlchemy connections.

Start the elasticsearch server locally:

./elasticsearch

Running the tests:

pytest

Running only the pep8 linting:

pytest --linting

Running the tests without pep8 linting:

pytest --no-linting

The test data subset

If you add new tables to the data, you'll need to generate a new subset for testing.

We have used this nifty subsetting tool: rdbms-subsetter. Though it may be easier to add some sample data manually.

To build a new test subset, first delete and recreate the test database:

dropdb cfdm_test
createdb cfdm_test

Now use the build_test invoke task to populate the new database with the new subset:

invoke build_test <source> postgresql://:@/cfdm_test

where source is a valid PostgreSQL connection string. To update the version-controlled test subset after rebuilding, run:

invoke dump postgresql://:@/cfdm_test data/subset.dump

Deployment (FEC team only)

Deployment prerequisites

If you haven't used Cloud Foundry in other projects, you'll need to install the Cloud Foundry CLI.

Deploy

Before deploying, install version 7 of the Cloud Foundry CLI:

  1. Read Cloud Foundry documentation to install version 7 of the Cloud Foundry CLI.

  2. Set environment variables used by the deploy script:

    export FEC_CF_USERNAME=<your_cf_username>
export FEC_CF_PASSWORD=<your_cf_password>

    If these variables aren't set, you'll be prompted for your Cloud Foundry credentials when you deploy the app.

Deployment steps

To deploy to Cloud Foundry, run:

invoke deploy

Learn more about Invoke.

The deploy task will detect the appropriate Cloud Foundry space based the current branch. You can override this with the optional --space flag. For example:

invoke deploy --space dev

This command will explicitly target the dev space.

To run migrations with a manual deploy, run:

invoke deploy --space dev --migrate-database

Setting up a service

On Cloud Foundry, we use the aws-elasticache-redis service. For more information about plan names, see the cloud.gov aws-elasticache-redis documentation

The Redis service can be created as follows:

cf create-service aws-elasticache-redis <plan_name> fec-elasticache-redis

Setting up credentials

cf target -s dev
cf cups fec-creds-dev -p '{"SQLA_CONN": "..."}'

To stand up a user-provided credential service that supports both the API and the webapp, ensure that the following keys are set:

Deploys of a single app can be performed manually by targeting the env/space, and specifying the corresponding manifest, as well as the app you want, like so:

cf target -o [dev|stage|prod] && cf push -f manifest_<[dev|stage|prod]>.yml [api|web]

Note: Performing a deploy in this manner will result in a brief period of downtime.

Running commands remotely

There may be a time when you need to run a command remotely, e.g., a management command to update database schemas. Cloud Foundry currently doesn't support a way of connecting to an app that is running directly, so you need to deploy a one-off app specifically for running commands instead.

To accomplish this, follow these steps:

  1. Make sure you're pointing to the correct target space:

    cf target -o fec -s <dev | stage | prod>

  2. Create a new app manifest for this one-off app (choose whatever file name you wish, ending with the .yml extension):

    cf create-app-manifest api -p <one-off-app-manifest-filename.yml>

  3. Open the newly created one-off app manifest file and add/modify the following YAML properties to it (be sure to maintain all of the other existing properties):

    name: one-off-app-name
command: "<your command here, e.g., python cli.py refresh_materialized> && sleep infinity"
no-route: true

    Note: the && sleep infinity part is needed as the end of the command you specify so that Cloud Foundry doesn't attempt to redeploy the app once the command finishes.

  4. Using the same app name you just specified in the custom manifest file, push your application to Cloud Foundry:

    cf push <one-off-app-name> -f <one-off-app-manifest-filename.yml>

Once the app is pushed, you can also tail the logs to make see when the command finishes:

cf logs <one-off-app-name>

When the command you want to run finishes, be sure to stop and delete the app to free resources in Cloud Foundry:

cf stop <one-off-app-name>
cf delete <one-off-app-name>

One other thing you may want to consider doing is adding explicit log statements or an email notification to whatever command you are running so that you know for sure when the command finishes (or errors out). However, please do not check in these custom modifications.

Note: We hope to have a better way of accomplishing this in the future.

SSH

Likely only useful for FEC team members

You can SSH directly into the running app container to help troubleshoot or inspect things with the instance(s). Run the following command:

cf ssh <app name>

Where <app name> is the name of the application instance you want to connect to. Once you are logged into the remote secure shell, you'll also want to run this command to setup the shell environment correctly:

. /home/vcap/app/bin/cf_env_setup.sh

More information about using SSH with cloud.gov can be found in the cloud.gov SSH documentation.

Create a changelog

If you're preparing a release to production, you should also create a changelog. The preferred way to do this is using the changelog generator.

Once installed, run:

github_changelog_generator --since-tag <last public-relase> --t <your-gh-token>

When this finishes, commit the log to the release.

Git-flow and continuous deployment

We use git-flow for naming and versioning conventions. Both the API and web app are continuously deployed through Circle CI accordingly.

Creating a new feature

Creating a hotfix

Creating a release

Additional developer notes

This section covers a few topics we think might help developers after setup.

API umbrella

The staging and production environments use the API Umbrella for rate limiting, authentication, caching, and HTTPS termination and redirection. Both environments use the FEC_API_USE_PROXY flag to reject requests that are not routed through the API Umbrella.

Caching

All API responses are set to expire after one hour (Cache-Control: public, max-age=3600). In production, the API Umbrella will check this response header and cache responses for the specified interval, such that repeated requests to a given endpoint will only reach the Flask application once. This means that responses may be stale for up to an hour following the nightly refresh of the materialized views.

Data for development and staging environments

The production and staging environments use relational database service (RDS) instances that receive streaming updates from the FEC database. The development environment uses a separate RDS instance created from a snapshot of the production instance.

Nightly updates

Incrementally-updated aggregates and materialized views are updated nightly; see webservices/tasks/refresh.py for details. When the nightly update finishes, logs and error reports are slacked to the team.

Refreshing materialized views (excluding nightly mv refresh job)

The materialized views are manually refreshed when something needs to be removed or updated on the website (data section) on-demand.

Managing Elasticsearch

Reference Wiki Elasticsearch 7.x.0 management instruction

There are some management commands to manage (display, create, delete, restore...) repository, index and snapshot on Elasticsearch. More information is available by invoking each of these commands with a --help option. These commands can be run as cf tasks on cloud.gov, e.g.,

Display, Configure and Delete a repository

cf run-task api --command "python cli.py display_repositories" -m 2G --name display_repositories

cf run-task api --command "python cli.py configure_snapshot_repository <repo_name>" -m 4G --name configure_snapshot_repository

cf run-task api --command "python cli.py delete_repository <repo_name>" -m 4G --name delete_repository

Display, Create and Delete an index

We have three indexes for FEC legal documents, that are defined in INDEX_DICT 1) CASE_INDEX includes DOCUMENT_TYPE=('statutes','murs','adrs','admin_fines') current mur only. 2) AO_INDEX includes DOCUMENT_TYPE=('advisory_opinions') 3) ARCH_MUR_INDEX includes DOCUMENT_TYPE=('murs'), archived mur only

cf run-task api --command "python cli.py display_index_alias" -m 2G --name display_index_alias

cf run-task api --command "python cli.py create_index <index_name>" -m 2G --name create_index

cf run-task api --command "python cli.py delete_index <index_name>" -m 2G --name delete_index

cf run-task api --command "python cli.py display_mapping <index_name>" -m 2G --name display_mapping

When mapping change, update mapping and reload legal data with short downtime(<5 mins) by an index

cf run-task api --command "python cli.py update_mapping_and_reload_legal_data <index_name>" -m 4G --name update_mapping_and_reload_legal_data

This command is typically used when there is a schema change. A swapping index (XXXX_SWAP_INDEX) is built and populated in the background. When ready, the swapping index is moved to the current index (XXXX_INDEX) with short downtime)<5 mins>.

Initialize the legal documents with downtime (15mins ~ 2+ hours)by an index

cf run-task api --command "python cli.py initialize_legal_data <index_name>" -m 4G --name initialize_legal_data

This command is used to initialize the legal data with downtime (15mins ~ 2+ hours) by an index.

Reload all legal data by specify 'index_name'(15mins ~ 2+ hours) without downtime.

cf run-task api --command "python cli.py reload_all_data_by_index ao_index" -m 4G --name reload_all_data_by_index_ao

Upload individual legal documents

The progress of these tasks can be monitored using, e.g.,

cf logs api | grep <some key word>

Loading advisory opinions [beginning with FROM_AO_NO through newest AO]

cf run-task api --command "python cli.py load_advisory_opinions [FROM_AO_NO]" -m 4G --name load_advisory_opinions

Loading current MURs (type=murs and mur_type=current) [only one MUR_NO]

cf run-task api --command "python cli.py load_current_murs [MUR_NO]" -m 4G --name load_current_murs

Loading ADRs [only one ADR_NO]

cf run-task api --command "python cli.py load_adrs [ADR_NO]"  -m 4G --name load_adrs

Loading Admin Fines [only one AF_NO]

cf run-task api --command "python cli.py load_admin_fines [AF_NO]" --name load_admin_fines

Loading statutes

cf run-task api --command "python cli.py load_statutes" --name load_statutes

Loading archived murs(type=murs and mur_type=archived)

(load one arch_mur):

cf run-task api --command "python cli.py load_archived_murs [MUR_NO]" --name upload_one_arch_mur

(load all arch_mur):

cf run-task api --command "python cli.py load_archived_murs" --name upload_arch_mur

Production stack

The OpenFEC API is a Flask application deployed using the gunicorn WSGI server behind an nginx reverse proxy. Static files are compressed and served directly through nginx; dynamic content is routed to the Flask application via proxy_pass. The entire application is served through the API Umbrella, which handles API keys, caching, and rate limiting.

Sorting fields

Sorting fields include a compound index on on the filed to sort and a unique field. Because in cases where there were large amounts of data that had the same value that was being evaluated for sort, the was not a stable sort view for results and the results users received were inconsistent, some records given more than once, others given multiple times.

Database mirrors/replicas

Database mirrors/replicas are supported by the API if the SQLA_FOLLOWERS is set to one or more valid connection strings. By default, setting this environment variable will shift all read operations to any mirrors/replicas that are available (and randomly choose one to target per request if there are more than one).

You can optionally choose to restrict traffic that goes to the mirrors/replicas to be the asynchronous tasks only by setting the SQLA_RESTRICT_FOLLOWER_TRAFFIC_TO_TASKS environment variable to something that will evaluate to True in Python (simply using True as the value is fine). If you do this, you can also restrict which tasks are supported on the mirrors/replicas. Supported tasks are configured by adding their fully qualified names to the app.config['SQLALCHEMY_FOLLOWER_TASKS'] list in order to allow them. By default, only the download task is enabled.

Database migration

flyway is the tool used for database migration.

Installing flyway

It is recommended that you install flyway using homebrew: brew install flyway

How flyway works

All database schema modification code is checked into version control in the directory data/migrations in the form of SQL files that follow a strict naming convention - V<version_number>__<descriptive_name>.sql. flyway also maintains a table in the target database called flyway_schema_history which tracks the migration versions that have already been applied.

flyway supports the following commands:

For more information, see Flyway documentation.

Running tests

Tests that require the database will automatically run flyway migrations as long as flyway is in PATH.

Deployment from CircleCI

flyway is installed in CircleCI. During the deployment step, CircleCI invokes flyway to migrate the target database (depending on the target space). For this to work correctly, connection URLs for the target databases have to be stored as environment variables in CircleCI under the names FEC_MIGRATOR_SQLA_CONN_DEV, FEC_MIGRATOR_SQLA_CONN_STAGE and FEC_MIGRATOR_SQLA_CONN_PROD. The connection URL has to strictly adhere to the structure postgresql://<username>:<password>@<hostname>:<port>/<database_name>. Note that the database_name should be specified explicitly, unlike URLs for SQLAlchemy connections.

Running flyway manually

You may need to run flyway manually in order to test migrations locally, or to troubleshoot migrations in production. There are 2 required parameters: