This API is utilized by USAspending.gov to obtain all federal spending data which is open source and provided to the public as part of the DATA Act.

Creating a Development Environment

Ensure the following dependencies are installed and working prior to continuing:

Requirements

• docker which will handle the other application dependencies.
• docker-compose
• bash or another Unix Shell equivalent
  • Bash is available on Windows as Windows Subsystem for Linux
• git
• make for running build/test/run targets in the Makefile. (Run $ make for a list of targets.)

If not using Docker, you’ll need to install app components on your machine:

Using Docker is recommended since it provides a clean environment. Setting up your own local environment requires some technical abilities and experience with modern software tools.

• Command line package manager
  • Windows’ WSL bash uses apt-get
  • MacOS users can use Homebrew
  • Linux users already know their package manager (yum, apt, pacman, etc.)
• PostgreSQL version 13.x (with a dedicated data_store_api database)
• Elasticsearch version 7.1
• Python version 3.8 environment
  • Highly recommended to use a virtual environment. There are various tools and associated instructions depending on preferences
  • See Required Python Libraries for an example using pyenv

Cloning the Repository

Now, navigate to the base file directory where you will store the USAspending repositories

$ mkdir -p usaspending && cd usaspending
$ git clone https://github.com/fedspendingtransparency/usaspending-api.git
$ cd usaspending-api

Environment Variables

Create Your .env File

Copy the template .env file with local runtime environment variables defined. Change as needed for your environment. This file is git-ignored and will not be committed by git if changed.

$ cp .env.template .env

A .env file is a common way to manage environment variables in a declarative file. Certain tools, like docker-compose, will read and honor these variables.

.envrc File

direnv is a shell extension that automatically runs shell commands in a .envrc file (commonly env var export commands) when entering or exiting a folder with that file

Create a .envrc file in the repo root, which will be ignored by git. Change credentials and ports as-needed for your local dev environment.

export DATABASE_URL=postgres://usaspending:usaspender@localhost:5432/data_store_api
export ES_HOSTNAME=http://localhost:9200
export DATA_BROKER_DATABASE_URL=postgres://admin:root@localhost:5435/data_broker

If direnv does not pick this up after saving the file, type

$ direnv allow

Alternatively, you could skip using direnv and just export these variables in your shell environment.

Just make sure your env vars declared in the shell and in .env match for a consistent experience inside and outside of Docker

Build usaspending-backend Docker Image

This image is used as the basis for running application components and running containerized setup services.

$ docker-compose --profile usaspending build

‼️ Re-run this command if any python package dependencies change (in requirements/requirements-app.txt), since they are baked into the docker image at build-time.

Database Setup

A postgres database is required to run the app. You can run it in a postgres docker container (preferred), or run a PostgreSQL server on your local machine. In either case, it will be empty until data is loaded.

• ⚠️ If running your own PostgreSQL server be sure to:
  1. Have a DB named data_store_api
  2. A superuser role (user), e.g. ALTER ROLE <<role/user you created>> WITH SUPERUSER;
  3. Cross-check your .env or .envrc files if used to be sure it references your DBs user, password, host, and port where needed

Start the Postgres DB Container

If not using your own local install…

$ docker-compose --profile usaspending up usaspending-db

… will create and run a Postgres database.

Bring DB Schema Up-to-Date

• docker-compose run --rm usaspending-manage python3 -u manage.py migrate will run Django migrations.

• docker-compose run --rm usaspending-manage python3 -u manage.py matview_runner --dependencies will provision the materialized views which are required by certain API endpoints.

Seeding and Loading Database Data

To just get essential reference data, you can run:

• docker-compose run --rm usaspending-manage python3 -u manage.py load_reference_data will load essential reference data (agencies, program activity codes, CFDA program data, country codes, and others).

Alternatively, to download a fully populuated production snapshot of the database (full or a subset) and restore it into PostgreSQL, use the pg_restore tool as described here: USAspending Database Download

• Recreate matviews with the command documented in the previous section if this is done

Executing individual data-loaders to load in data is also possible, but requires more familiarity with those ad-hoc scripts and commands, and also requires an external data source (Data Broker DB, or external file, etc.) from which to load the data.

• For details on loading reference data, Data Accountability Broker Submissions, and current USAspending data into the API, see loading_data.md.
• For details on how our data loaders modify incoming data, see data_reformatting.md.

Elasticsearch Setup

Some of the API endpoints reach into Elasticsearch for data.

$ docker-compose --profile usaspending up usaspending-es

… will create and start a single-node Elasticsearch cluster as a docker container with data persisted to a docker volume.

• The cluster should be reachable via at http://localhost:9200 (“You Know, for Search”).

• Optionally, to see log output, use docker-compose logs usaspending-es (these logs are stored by docker even if you don’t use this).

Generate Elasticsearch Indexes

The following will generate two base indexes, one for transactions and one for awards:

$ docker-compose run --rm usaspending-manage python3 -u manage.py elasticsearch_indexer --create-new-index --index-name 01-26-2022-transactions --load-type transaction
$ docker-compose run --rm usaspending-manage python3 -u manage.py elasticsearch_indexer --create-new-index --index-name 01-26-2022-awards --load-type award

Running the API

docker-compose --profile usaspending up usaspending-api

… will bring up the Django app for the RESTful API

• You can update environment variables in settings.py (buckets, elasticsearch, local paths) and they will be mounted and used when you run this.

The application will now be available at http://localhost:8000.

Note: if the code was run outside of Docker then compiled Python files will potentially trip up the docker environment. A useful command to run for clearing out the files on your host is:

find . | grep -E "(__pycache__|\.pyc|\.pyo$)" | xargs rm -rf

Using the API

In your local development environment, available API endpoints may be found at http://localhost:8000/docs/endpoints

Deployed production API endpoints and docs are found by following links here: https://api.usaspending.gov

Running Tests

Test Setup

1. Build the base usaspending-backend Docker image (the test container is based on this Docker image). In the parent usaspending-api directory run:

   docker build -t usaspending-backend .
   

2. Start the Spark containers for the Spark related tests

   docker-compose --profile spark up -d
   

3. To run all USAspending tests in the docker services run

   docker-compose run --rm -e DATA_BROKER_DATABASE_URL='' usaspending-test
   

NOTE: If an env var named DATA_BROKER_DATABASE_URL is set, Broker Integration tests will attempt to be run as well. If doing so, Broker dependencies must be met (see below) or ALL tests will fail hard. Running the above command with -e DATA_BROKER_DATABASE_URL='' is a precaution to keep them excluded, unless you really want them (see below if so).

To run tests locally and not in the docker services, you need:

1. Postgres A running PostgreSQL database server (See Database Setup above)
2. Elasticsearch A running Elasticsearch cluster (See Elasticsearch Setup above)
3. Environment Variables Tell python where to connect to the various data stores (See Environmnet Variables)
4. Required Python Libraries Python package dependencies downloaded and discoverable (See below)

NOTE: Running test locally might require you to run export OBJC_DISABLE_INITIALIZE_FORK_SAFETY=YES. As discussed here, there is an issue than can cause some of the Spark tests to fail without this environment variable set.

Once these are satisfied, run:

(usaspending-api) $ make tests

or, alternatively to skip using make

(usaspending-api) $ pytest

Required Python Libraries

Setup python, virtual environment, and pip dependencies, then check version info

$ make local-dev-setup
$ source $(make activate) 

Your prompt should then look as below to show you are in the virtual environment named usaspending-api (to exit that virtual environment, simply type deactivate at the prompt).

(usaspending-api) $

Including Broker Integration Tests

Some automated integration tests run against a Broker database. If certain dependencies to run such integration tests are not satisfied, those tests will bail out and be marked as Skipped.
(You can see messages about those skipped tests by adding the -rs flag to pytest, like: pytest -rs)

To satisfy these dependencies and include execution of these tests, do the following:

1. Ensure the Broker source code is checked out alongside this repo at ../data-act-broker-backend

2. Ensure you have Docker installed and running on your machine

3. Ensure you have built the Broker backend Docker image by running:

   (usaspending-api) $ docker build -t dataact-broker-backend ../data-act-broker-backend
   

4. Ensure you have the DATA_BROKER_DATABASE_URL environment variable set, and it points to what will be a live PostgreSQL server (no database required) at the time tests are run.

   1. WARNING: If this is set at all, then ALL above dependencies must be met or ALL tests will fail (Django will try this connection on ALL tests’ run)
   2. This DB could be one you always have running in a local Postgres instance, or one you spin up in a Docker container just before tests are run

5. If invoking pytest within a docker container (e.g. using the usaspending-test container), you must mount the host’s docker socket. This is declared already in the docker-compose.yml file services, but would be done manually with: -v /var/run/docker.sock:/var/run/docker.sock

NOTE: Broker source code should be re-fetched and image rebuilt to ensure latest integration is tested

Re-running the test suite using pytest -rs with these dependencies satisfied should yield no more skips of the broker integration tests.

Example Test Invocations of Just a Few Broker Integration Tests: (i.e. using -k)

From within a container

(NOTE: DATA_BROKER_DATABASE_URL is set in the docker-compose.yml file (and could pick up .env values, if set)

(usaspending-api) $ docker-compose run --rm usaspending-test pytest --capture=no --verbose --tb=auto --no-cov --log-cli-level=INFO -k test_broker_integration

From Developer Desktop

(NOTE: DATA_BROKER_DATABASE_URL is set in the .envrc file and available in the shell)

(usaspending-api) $ pytest --capture=no --verbose --tb=auto --no-cov --log-cli-level=INFO -k test_broker_integration

Contributing

To submit fixes or enhancements, or to suggest changes, see CONTRIBUTING.md