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.

• Bash

  or another Unix Shell equivalent
  • Bash is available on Windows as Windows Subsystem for Linux

• Git

If not using Docker:

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 will use

    Homebrew

  • Linux users already know their package manager (yum, apt, pacman, etc.)

• PostgreSQL

  version 10.x (with a dedicated

  data_store_api

  database)

• Elasticsearch

  version 7.1
• Python 3.7 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

Database Setup

There are three documented options for setting up a local database in order to run the API:

1. Local Empty DB. Use your own local postgres database for the API to use.
2. Containerized Empty DB. Create an empty directory on your localhost where all the database files will persist and use the docker-compose file to bring up a containerized postgres database.
3. Local Populated DB. Download either the whole database or a database subset from the USAspending website.

Option 1: Using a Locally Hosted Postgres Database

Create a Local postgres database called 'datastoreapi' and either create a new username and password for the database or use all the defaults. For help, consult: - Postgres Setup Help

Make sure to grant whatever user you created for the data_store api database superuser permissions or some scripts will not work:

postgres=# ALTER ROLE <> WITH SUPERUSER;

Option 2: Using the Docker Compose Postgres Database

See below for basic setup instructions. For help with Docker Compose: - Docker Compose

Database Setup and Initialization with Docker Compose

• None of these commands will rebuild a Docker image! Use

  --build

  if you make changes to the code or want to rebuild the image before running the

  up

  steps.

• If you run a local database, set

  POSTGRES_HOST

  in

  .env

  to

  host.docker.internal

  .

  POSTGRES_PORT

  should be changed if it isn't 5432.

  • docker-compose up usaspending-db

    will create and run a Postgres database.

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

    will run Django migrations: https://docs.djangoproject.com/en/2.2/topics/migrations/.

  • 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).

  • 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.

Manual Database Setup

• docker-compose.yaml

  contains the shell commands necessary to set up the database manually, if you prefer to have a more custom environment.

Option 3: Downloading the database or a subset of the database and loading it into PostgreSQL

For further instructions on how to download, use, and setup the database using a subset of our data please go to:

USAspending Database Download

Elasticsearch Setup

Some of the API endpoints reach into Elasticsearch for data.

• docker-compose up usaspending-es

  will create and start a single-node Elasticsearch cluster, using the

  ES_CLUSTER_DIR

  specified in the

  .env

  configuration file. We recommend using a folder outside of the usaspending-api project directory so it does not get copied to other containers.

• 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).

Running the API

docker-compose up usaspending-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

Loading Data

Note: it is possible to run ad-hoc commands out of a Docker container once you get the hang of it, see the comments in the Dockerfile.

For details on loading reference data, DATA Act 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.

Running Tests

Test Setup

To run all tests in the docker services run

docker-compose run --rm usaspending-test

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. Required Python Libraries Python package dependencies downloaded and discoverable (See below)
4. Environment Variables Tell python where to connect to the various data stores (See below)

Once these are satisfied, run:

(usaspending-api) $ pytest

Required Python Libraries

Create and activate the virtual environment using

venv

python36u

$ pyenv install 3.7.2
$ pyenv local 3.7.2
$ python -m venv .venv/usaspending-api
$ source .venv/usaspending-api/bin/activate

Your prompt should then look as below to show you are in the virtual environment named

usaspending-api

deactivate

(usaspending-api) $

pip

install

(usaspending-api) $ pip install -r requirements/requirements.txt

Environment Variables

Create a

.envrc

export DATABASE_URL=postgres://usaspending:[email protected]:5432/data_store_api
export ES_HOSTNAME=http://localhost:9200
export DATA_BROKER_DATABASE_URL=postgres://admin:[email protected]:5435/data_broker

If

direnv

$ direnv allow

Alternatively, you could skip using

direnv

Including Broker Integration Tests

Some automated integration tests run against a Broker database. If the 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

pytest -rs

To satisfy these dependencies and include execution of these tests, do the following: 1. Ensure you have

Docker

Broker

../data-act-broker-backend

DATA_BROKER_DATABASE_URL

Broker

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

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

Contributing

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