Ticketly: A Smart, Scalable Ticket Booking System on AWS

- 5 mins
FAST-API Ticket booking AWS Python

Ticketly

A Movie Theatre Ticket Booking System Build in Python Using FastAPI.

Swagger Page

See It In Action

LoadBalancer

QR Code(Tryit on Your Phone): qr

Ticketly

Object Model

  1. TicketSlot : A logical entity which defines movieslot (could be extended to documentaries, concerts etc - Hence the name TicketSlot and not just MovieSlot). It is defined as following: 
    {
     "slotName": "Skyfall", 
     "slotDescription": "An ex-MI6 agent steals a hard drive with top secret information to carry out a vendetta on Bond's overseer, M. Bond must face his past in a bid to try and save M.", 
     "startTime": "2020-11-01 12:22",
     "endTime": "2020-11-01 15:22", 
     "slotType": "Movie", 
     "genre": "Action"
 }

Same movie can have many diffrent TicketSlots if the movie is scheduled at many different timings.

  1. Ticket : A logical entity which defines a ticket object at a point in time. Tickets are Booked at the time of booking and the state changes to Expired, Canceled, Archived according to Tickets lifecycle which is controled by the TicketController.
    {
      "ticketStatus": "Booked",
      "ticketSlotId": "713396",
      "ticketId": "TKT331086",
      "userId": "U385985"
    }
  1. User : A logical entity which defines a user object. Example:
    {
        "userId": "U385985",
        "userName": "Anuranjan",
        "phoneNumber": "7906543416"
    }

Sample Requests

Click To Expand Sample Requests ### Book User Ticket ```json { "userName": "John", "userPhoneNumber": "9653864514", "movieName": "Avatar", "movieStartTime":"2020-11-01 12:22", "numTickets": "3" } ``` ### Update Ticket Time ```json { "ticketId": "TKT437664", "newMovie": "Avatar", "newStartTime": "2020-11-01 12:22" } ``` ### Get all Booked Tickets for a Movie Slot ```json { "movieName": "Inception", "movieStartTime": "2020-11-01 12:22" } ``` ### Get User Details By ticketId ```json { "ticketId": "TKT437664" } ``` ### Cancel a Ticket By ticketId ```json { "ticketId": "TKT437664" } ``` ### Add Movie Slot ```json { "slotName": "Skyfall", "slotDescription": "An ex-MI6 agent steals a hard drive with top secret information to carry out a vendetta on Bond's overseer, M. Bond must face his past in a bid to try and save M.", "startTime": "2020-11-01 12:22", "endTime": "2020-11-01 15:22", "slotType": "Movie", "genre": "Action" } ``` ### Get All Movie Slots By Genre ```json { "genre": "Action" } ```

Features

Application Architecture

UML Diagram

Since this seems like a system which might need code volution, new features to be added in future, I decided to closely follow the OOPS paradigm. UML also makes it easy to think through and handle corner cases. UML

The architecture of the application involves back-facing and a client-facing parts. I decided to use AWS for the backend of the application. The application is configured to use ec2, however, the instances are throttled and managed according to the scaling policies defined using AWS AutoScaling Groups. This is all configured behind an Application Load Balancer (ALB) which hides all the complexity from the user.

When the ALB receives a request, it is automatically routed to one of the ec2 instances within the target group. Design

Data Layer

For static content, we could leverage AWS Amplify and S3. For tables in particular, I decided to use DynamoDB for two reasons:

  1. NoSQL Databases allow a code-first strategy. This allows easy integration with ORMs and thus, faster development. The schema is loosely coupled with code, and thus can evolve over time.
  2. Faster read through.

Fractional Millisecond Latency on SCAN operations

As shown below, even witha simulated load test, the DynamoDB database can handle sub-millisecond latency for scans. (Note that this does not include GET operations where hash_key is involved, which are O(logn)) ScanLatency

Scaling The Application

I chose to define a two-fold scaling mechanism. New nodes (ec2-instances) are spawned automatically if:

  1. The ALB health-check (runs every 300 sec) marks the node unhealthy (disk failure / disaster etc)
  2. The network load as observed by the nodes is higher than specified threadshold.

This could also be configured using CloudWatch logs. But because of limited time, I didn’t get a chance to do it ScalingPolicies

Node Status on AWS Dashboard

NodeStatus

Alarms Set For DDoS & Database Protection

AWS allows SNS alarms (Simple Notification Service) to be set up if there is an unusually high amount of load on the database

NodeAlarm

The Nitty-Gritty

Test Framework

pytest is used as the testing framework: pytest

Framework Used

Build with

How to Use

    git clone https://github.com/almique/Movie_Ticket_Booking.git
    uvicorn main:app --reload

Tests

Just install pytest and in the terminal type:

    pytest

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