Event Recommendation & Ticket Search System

• Designed and maintained an interactive web page for clients to search events and purchase tickets

Table of contents

• 1. Project Description
• 2. Infrastructure Design
• 3. Java Servlet Design
• 4. Database Implementation
• 5. Design pattern

1. Project Description

• Link to the web application
• Frontend: an interactive web page with AJAX technology implemented with HTML, CSS and JavaScript. This web application supports 3 major implementations:
  • Auto-Search events around users based on geolocation
  • Favorite events when the user likes while Unfavorite events when user unlikes the events
  • Show recommendation of events around based on their favorite history
• Backend: create Java servlet with RESTful APIs in Apache Tomcat to handle HTTP requests and responses
  • Utilized relational and NoSQL databases (MySQL/MongoDB) to store real business data, such as name, description, location and price, from TicketMaster API
  • Design content-based recommendation algorithm for event recommendation
  • Deployed server to Amazon EC2 to handle 160 QPS tested by Apache JMeter

2. Infrastructure Design

• 3-tier architecture
  • Presentation tier: HTML, CSS, JavaScript
  • Data tier: MySQL, MongoDB
  • Logic tier: Java
• Local and remote development environment

Local development environment

Remote development environment

3. Java Servlet Design

• Logic tier(Java Servlet to RPC)
  • Search
    • searchItems
    • Ticketmaster API
    • parse and clean data, saveItems
    • return response
  • History
    • get, set, delete favorite items
    • query database
    • return response
  • Recommendation
    • recommendItems
    • get favorite history
    • search similar events, sorting
    • return response
  • Login
    • GET: check if the session is logged in
    • POST: verify the user name and password, set session time and marked as logged in
    • query database to verify
    • return response
  • Logout
    • GET: invalid the session if exists and redirect to index.html
    • POST: the same as GET
    • return response
  • Register
    • Set a new user into users table/collection in database
    • return response

APIs design in logic tier

• TicketMasterAPI Official Doc - Discovery API
• Recommendation Algorithms design
  • Content-based Recommendation: find categories from item profile from a user’s favorite, and recommend the similar items with same categories.
  • Present recommended items with ranking based on distance (geolocation of users)

Process of recommend request

4. Database Implementation

• MySQL
  • users - store user information.
  • items - store item information.
  • category - store item-category relationship
  • history - store user favorite history

MySQL database design

• MongoDB
  • users - store user information and favorite history. = (users + history)
  • items - store item information and item-category relationship. = (items + category)
  • logs – store log information

5. Design pattern

• Builder pattern: Item.java
  • When convert events from TicketMasterAPI to java Items, use builder pattern to freely add fields.
• Factory pattern: ExternalAPIFactory.java, DBConnectionFactory.java
  • ExternalAPIFactory.java: support multiple function like recommendation of event, restaurant, news, jobs… just link to different public API like TicketMasterAPI. Improve extension ability.
  • DBConnectionFactory.java: support multiple database like MySQL and MongoDB. Improve extension ability.
• Singleton pattern: MySQLConnection.java, MongoDBConnection.java
  • Only create specific number of instance of database, and the class can control the instance itself, and give the global access to outerclass

Acknowledgement

• Above figures credit to Hannah Wang