This project enables users to search the City of San Francisco Mobile Food Facility Permit Open Data API for data such as name of vendor, location, type of food sold and status of permit.
Leveraging the SF Mobile Food Facility permit data in an example Python fastapi application. The current state of the project is a minimal viable product and will evolve progressively.
At the time of this writing, this project performs a number of operations on the related data using using various frameworks and technologies. Below are a list of functional requirements that specify function, purpose and current status
| Status | Action | Details |
|---|---|---|
| Completed | Evaluate data/schema | get familiar with the public api and its data |
| Completed | Model the data | model the data from the api to conform with the app spec |
| Completed | Decide project technologies | chose technologies to implement for this project |
| Completed | Write code | write Python code using fastapi to access the open api and retrieve data |
| Completed | Model service responses | create functions that transform api responses into useable data |
| Completed | Consolidate redundant data | create data models that return one applicant: and restructure locations into an embedded list |
| Completed | Create Sort on Day function | the locations: list must be sorted descending order by day |
| Completed | Create front-end search UI | create a simple search page that takes input and renders the results using Jinja2. |
| Completed | Unit tests | create application unit tests |
| Completed | Dockerfile | create a docker file to build a docker image for this app |
| Completed | Docker Compose | create a docker compose file to build and start a container |
| Completed | Docker Hub | make images publicly available on Docker Hub |
| Completed | Vulnerability Scans | create security scans using Snyk |
| Completed | CI/CD Pipeline | define a ci/cd pipeline to build, test, scan and deploy the docker image to docker hub |
| Completed | Documentation | create documentation for this project |
The following is a list of future enhancements if time permits:
- Refine the data model to eliminate data duplicate and streamline result sets
- Integrate with Google search and find the applicants business on Google and generate reference links to the business google profile
- Refine and polish the UI design to make it more pleasing to the eye
- Decompose the monolith into a micro services architecture where a web ui service consumes a separate food truck data api service and demonstrates distributed systems concepts.
- Create Infrastructure as Code (IaC) that:
- Provisions cloud native resources: VPS, Load Balancers, RBAC/IAMS etc..
- Integrates with AWS, GCP, Azure etc. and other cloud native providers
- Provision Kubernetes Clusters to host the app
- Create Kubernetes Manifests or helm charts
- Create persistence layers to capture and serve data from the application
- Provision and implement observability capabilities at the app level and service levels
- Extend the CI/Cd pipeline to include continuous deployment along with other valuable wokflow segments such as compliance scans, deploy and release execution wit rollback factored
This project has a CI/CD pipeline defined and functional here
https://app.circleci.com/pipelines/github/punkdata/sf-food-truck?branch=main
Execute this application locally with the following commands:
pip install -r requirements.txt
uvicorn main:appUse these commands to build a docker image and start a new container.
docker compose up -d --build# Note: the ariv3ra/ segment of this command should be replaced to reflect your user name
docker buildx build ariv3ra/sf_food_trucks:latest .docker run -d --name sf_food_trucks -p8000:8000 ariv3ra/sf_food_trucks:latest