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Setup Instructions

Overview

If you plan to contribute to the data wrangling and database management on this project and need to run the Python script, follow the installation and setup instructions below.

Setup

Fork the Repository

  1. Navigate to our GitHub repository.
  2. Create a fork of the repository by clicking the "Fork" button in the top right corner of the page.
  3. Clone your fork of the repository to your local machine using git clone.

Note: Keep your fork up to date with the original repository by following the instructions here.

Precommit hook

We use a precommit hook to help with formatting and linting in our CI/CD pipeline. When setting up the repo, please first make sure you have pre-commit installed using pip or another package manager. Once that's done, run pre-commit install in the root directory to set up the precommit hooks (configured in .pre-commit-config.yaml).

Set Environment Variables

The project requires specific and sensitive information to run, which should be stored in the user's development environment rather than in source control. Here are instructions for setting environment variables locally on your machine or using a .env file.

Using a .env File

  1. Create a file named .env in the /data subdirectory of your project.
  2. Add the following environment variables to the .env file:
POSTGRES_PASSWORD=a-strong-password-here
VACANT_LOTS_DB=postgresql://postgres:${POSTGRES_PASSWORD}@localhost:5433/vacantlotdb

All local environment variables will be passed through to docker compose, so if you have them set up in the .env file, you should not need to hard-code them elsewhere.

Setting Environment Variables Locally

For Mac and Linux, you can permanently store the environment variables in your command line shell's configuration file, e.g., ~/.bashrc, ~/.bash_profile, ~/.zshrc, or ~/.profile. Add a line export VAR_NAME=VALUE in your file and run source <file> to read it in when newly created. Any new shells will automatically have the new environment.

For Windows, you can set environment variables under System -> Advanced or you can download a terminal emulator such as Git Bash and follow the instructions for Mac and Linux above. A terminal emulator is recommended.

export POSTGRES_PASSWORD=a-strong-password-here
export VACANT_LOTS_DB=postgresql://postgres:${POSTGRES_PASSWORD}@localhost:5433/vacantlotdb

All of your local environment variables will be passed through to docker compose, so if you have them locally, you should not have to hard-code them.

Docker Build

Docker is a platform that allows you to containerize and run applications in isolated environments, making it easier to manage dependencies and ensure consistent deployments. Download the latest version of Docker Desktop for your operating system.

We use docker compose to manage the backend Docker services. The data/docker-compose.yaml file defines the services. The only service that runs perpetually in Docker is postgres. The other services are one-time batch jobs to build the data sets.

  1. The first time you set up your backend, or any time either of the two Docker files change, build the Docker services by running:

    docker compose build

    This should correctly build both containers. However, if it does not, you can explicitly build the postgres container with the following:

    docker compose build postgres

  2. When both containers are built, connect to the PG database in the container by running:

    docker compose up -d postgres

For first-time runs, set FORCE_RELOAD=True in config.py and optionally log_level: int = logging.DEBUG to get more verbose output.

All Docker commands should be run from the data/ directory. There is one main Dockerfile for the batch scripts and one called Dockerfile-pg for the PostgreSQL and postgis installation. There is also a file called init_pg.sql that is run one time by Docker when the postgres data volume is empty to create the database and install postgis. You should not have to touch any of the above three files.

Windows

  1. Make sure Docker is running by opening the Docker Desktop app.
  2. Open the command prompt. Navigate to the location of the clean-and-green-philly repository. Run cd data and then docker compose run vacant-lots-proj.
  3. When the script is done running, you’ll get a notification. When you’re done, to shut off the Docker container (which uses memory), run docker compose down.

Linux

  1. In the terminal, navigate to your repository location using cd path/to/clean-and-green-philly. Then run cd data to move into the data directory.
  2. Run docker compose run vacant-lots-proj. Enter your password if requested. If you run into an error message related to "KEY_ID" or something similar, you may have to do the following:
    • Hard-code your VACANT_LOTS_DB variable in docker-compose.yml.

The backend also works on WSL Ubuntu running Docker for Linux on Windows 10.

  1. When you're finished, and you want to shut down the Docker container, run docker compose down.

macOS

In the terminal, use the cd command to navigate to your repository location, and then into the data directory. Run docker compose run vacant-lots-proj. This command starts Docker Compose and sets up your environment as defined in your docker-compose.yml file. When you're finished and want to shut down the Docker containers, run docker compose down.

PostgreSQL

PostgreSQL AKA postgres, pg, psql is an open-source relational database management system. It is used in this project only by the data load script to stage data and by the data diff process to compare new data with backed up data. It is not needed by the front-end to run. We run Postgres with the Postgis extension for geospatial data in a Docker container.

We are running postgres on the non-standard port 5433 instead of the default of 5432. This is so our Docker postgres will not conflict with any native postgres already running on the developer's PC.

To start the postgres Docker container, run:

docker compose up -d postgres

You can access the psql command line in your container to work with the database with this command:

docker exec -it cagp-postgres psql -U postgres -d vacantlotdb

To stop the postgres container run:

docker compose down postgres

Python Development

You can set up your local Python environment so you can develop and run the backend script.py and create and run unit tests outside of Docker. Build your local environment to match what is defined in the Dockerfile. Install the same python version as is in the Dockerfile, using pyenv to manage multiple distributions if needed. Use pipenv to create a virtual environment. Install the pip dependencies that are defined in the Pipfile into your virtual environment. Install the executables with apt-get. Now you can develop in Python in your terminal and IDE and run unit tests with pytest.

Configuration

There are numerous configuration variables in data/src/config/config.py. See the documentation in that file for each variable. You will also have to set up environmental variables for keys and database connection parameters as defined throughout this document.

There are the following secrets that may be securely shared with you by the project leads:

  • The password for the project's Google account to access the cloud platform. For development purposes, you can work in your personal cloud account, see the GCP section below.
  • The Slack API key to post diff reports to the project Slack via the messenger bot. See the 'Backup and difference reporting' section below. You can set up your own Slack bot for your personal workspace and use that API key for local testing. See this link for instructions or do a Google search on how to do it.

Making code changes

Changes to our codebase should always address an issue and need to be requested to be merged by submitting a pull request that will be reviewed by at least the team lead or tech lead.

Formatting

Format all python files by running:

docker compose run formatter

Google Cloud (GCP)

The map data is converted to the pmtiles format and served from Google Cloud. For access to production credentials, contact the project lead.

You can run the tile build locally with docker compose run vacant-lots-proj to create a tile file and upload it to your own GCP bucket. First, create your own GCP account using their free trial. You will need to create the following assets in your GCP account and configure them in the environment variables in the .env file:

  1. Under APIs and Services -> Credentials, create an API key and put that in the CLEAN_GREEN_GOOGLE_KEY variable.
  2. Under APIs and Services -> Credentials, create a service account. After you create the service account you will download the service account private key file named like encoded-keyword-ddd-xxx.json. Copy that to ~/.config/gcloud/application_default_credentials.json. This path is specified by default in the volumes section of the docker compose file.
  3. Go to Cloud storage -> Buckets and create a new bucket. Name it logically, e.g. cleanandgreenphl-{your_initials}. It has to be globally unique. Grant access to at least write to the bucket to your service account. Put your bucket name in the GOOGLE_CLOUD_BUCKET_NAME variable. Make sure the tiles file in your bucket is publicly accessible by following Google's instructions online.

Your /data/.env file should now look like this:

POSTGRES_PASSWORD=a-strong-password-here
VACANT_LOTS_DB=postgresql://postgres:${POSTGRES_PASSWORD}@localhost:5433/vacantlotdb
CLEAN_GREEN_GOOGLE_KEY=your-api-key-here
GOOGLE_CLOUD_BUCKET_NAME=your-bucket-name-here

The python script loads the tiles to Google Cloud as vacant_properties_tiles_staging.pmtiles. You can check this tileset by changing the config setting on the frontend useStagingTiles to true. If the tiles look OK, manually change the name in Google Cloud to remove the _staging and archive the previous copy.

Converting PMTiles to Vector Data

Pmtiles stored in the cloud or locally can be converted back to a vector dataset (e.g., GeoJson) by using tippecanoe in the terminal: tippecanoe-decode -c vacant_properties_tiles.pmtiles > vacant_properties_tiles.geojson. (Note that this can likely be done in the Docker container to avoid installing a local copy of tippecanoe, but we haven't explored this yet.)

Google Streetview

To update streetview images, after running the full data script run:

docker compose run streetview

The script should only load new images that aren't in the bucket already (new properties added to list).

Backup and difference reporting

Whenever the data load script is run in force reload mode, the old data set is backed up and a report of any differences is sent to the team via Slack. Differences in data are calculated using the data-diff package. See issue 520 in Github.

Backups are done in PostgreSQL in the vacantlotsdb database by copying the whole public schema to a backup schema named backup_{timestamp}. Besides the original tables, the backup schema includes a '{table_name}_diff' table with details of the differences from data-diff for each table.

Backup schemas are only kept for one year by default. Backup schemas older than a year are deleted at the end of the load script.

After all runs of the back-end script, the tiles file is backed up to the backup/ directory in the GCP bucket with a timestamp. If the main tiles file ever gets corrupted, it can be rolled back to a backup file.

When a diff is performed, an html file of the contents of the '{table_name}_diff' table is generated for each table and uploaded to the public GCP bucket so it can be viewed in a web browser. The location of the html files is in the format: https://storage.googleapis.com/cleanandgreenphl/diff/{backup_timestamp}/{table_name}.html The link to the detail diff page is included in the Slack report message.

The CAGP_SLACK_API_TOKEN environmental variable must be set with the API key for the Slack app that can write messages to the channel as configured in the config.py report_to_slack_channel variable.

The report will also be emailed to any emails configured in the config.py report_to_email variable.

Production script execution

The job to reload the backend data has been scheduled in the Google Cloud to run on a weekly basis.

A virtual machine running Debian Linux named backend is set up in the compute engine of the CAGP GCP account. The staging branch of the git project has been cloned here into the home directory of the cleanandgreenphl user. All required software such as docker and git has been installed on this vm.

To access the Linux terminal of this vm instance via SSH you can use the 'SSH-in-browser' GCP tool on the web. Go to Compute Engine -> VM instances and select SSH next to the backend instance, then select 'Open in browser window'.

You can also connect to the vm with the terminal ssh client on your pc. This is recommended for more advanced use cases as the web UI is limited. To set this up, follow the steps below:

  • In GCP, go to IAM and Admin -> Service Accounts -> Keys and click on the [email protected] account.
  • Click 'Add key'. You can only download the service account JSON key file when you create a key so you will have to create a new key. Select 'JSON' and save the .json file to your local machine.
  • Download and install the Google Cloud Command Line Interface (CLI) for your OS.
  • In your terminal, navigate to the folder with your saved .json file. Run the command: gcloud auth activate-service-account --key-file=your-key.json
  • Now you can ssh into the vm with: gcloud compute ssh --zone "us-east1-b" "cleanandgreenphl@backend" --project "clean-and-green-philly"
  • You will land in the home directory of the cleanandgreenphl user. The project has been cloned to this directory.

The job to regenerate and upload the tiles file and street images to the GCP bucket has been scheduled in cron to run weekly on Wednesday at 5 AM. You can run crontab -l to see the job. Currently it looks like this:

0 5 * * 3 . /home/cleanandgreenphl/.cagp_env && cd clean-and-green-philly/data && docker compose run vacant-lots-proj && docker compose run streetview

The specific production environmental variables are stored in /home/cleanandgreenphl/.cagp_env. Some variables in the data/src/config/config.py project file have been edited locally for the scheduled run. Be careful when running this job in this environment because the production web site could be affected.

The message with the diff report will be sent to the clean-and-green-philly-back-end Slack channel.

To troubleshoot any errors you can look at the docker logs of the last run container. e.g.: docker logs data-vacant-lots-proj-run-8c5e7639c386 | grep -i error