Restaurant Monitoring System

The Restaurant Monitoring System is a backend API that helps restaurant owners track the online and offline status of their stores during business hours. The system polls each store roughly every hour and records whether the store was active or not in a CSV file. The system also has data on the business hours of all the stores and the timezone for each store.

The system provides two APIs:

1. /trigger_report endpoint that triggers the generation of a report from the data provided (stored in the database). The API has no input and returns a report ID (a random string). The report ID is used to poll the status of report completion.

2. /get_report endpoint that returns the status of the report or the CSV. The API takes a report ID as input and returns the following:

   • If report generation is not complete, return "Running" as the output
   • If report generation is complete, return "Complete" along with the CSV file with the following schema: store_id, uptime_last_hour(in minutes), uptime_last_day(in hours), update_last_week(in hours), downtime_last_hour(in minutes), downtime_last_day(in hours), downtime_last_week(in hours) The uptime and downtime reported in the CSV only include observations within business hours. The system extrapolates uptime and downtime based on the periodic polls we have ingested to the entire time interval.

Data Sources

The system has the following three sources of data:

1. A CSV file with three columns (store_id, timestamp_utc, status) where status is active or inactive. All timestamps are in UTC.

2. A CSV file with data on the business hours of all the stores. The schema of this data is store_id, dayOfWeek(0=Monday, 6=Sunday), start_time_local, end_time_local. These times are in the local time zone. If data is missing for a store, assume it is open 24*7.

3. A CSV file with data on the timezone for each store. The schema is store_id, timezone_str. If data is missing for a store, assume it is America/Chicago. This is used so that data sources 1 and 2 can be compared against each other.

NOTE: Data files cannot be pushed due to lfs issue

     #### Files structure 
     
     ```
        data/
        
          ├── business_hours.csv
          
          ├── stores.csv
          
          └── timezones.csv
        ```

System Requirements

• The data sources are not static, and the system should not precompute the answers. The system should keep updating the data every hour.
• The system should store the CSVs into a relevant database and make API calls to get the data.

Installation

To install the required packages, run the following command in the project directory:

    pip install -r requirements.txt

Usage

To start the server, run the following command in the project directory:

    python run.py

The server will start running on http://localhost:5000

Note: Remember to add url prefix in the api like http://localhost:5000/api

API Documentation

• /trigger_report

  This endpoint triggers the generation of a report from the data provided (stored in the database).

  • Request

  POST /api/trigger_report HTTP/1.1
  
  

  • Response

      HTTP/1.1 200 OK
  Content-Type: application/json
  
  {
      'message': 'Success', 
      'error_code':200,
      "report_id": "random_string"
  }
  
  

• /get_report

  This endpoint returns the status of the report or the CSV.

  • Request

  GET /api/get_report?report_id=random_string HTTP/1.1
  
  

  • Response

      HTTP/1.1 200 OK
      Content-Type: text/csv
  
      store_id, uptime_last_hour(in minutes), uptime_last_day(in hours), update_last_week(in hours), downtime_last_hour(in minutes), downtime_last_day(in hours), downtime_last_week(in hours)
      1, 60, 
  
  
  

Functionalities

Used advance python features like -

• Multithreading
• Caching