Events manager tech test

Usage Instructions

• clone the repo and install Node.js modules

$ git clone https://github.com/elenamorton/events_manager_js.git
$ cd events_manager_js
$ npm install

• run tests

$ npm test

• start up the CLI Node application

$ npm start

Used Technologies

• Vanilla JavaScript ES6
• NodeJS v8.2.1
• Npm v5.3.0
• Test suite based on Mocha(v3.5.0), Chai(v4.1.0)
• Seeded randomizer using Chance(v1.0.10)

Requirements

Write a program which accepts a user location as a pair of coordinates, and returns a list of the five closest events, along with the cheapest ticket price for each event.

Headline specifications

• The program should randomly generate seed data
• Program should operate in a world that ranges from -10 to +10 (Y axis), and -10 to +10 (X axis).
• Each co-ordinate can hold a maximum of one event
• Each event has a unique numeric identifier (e.g. 1, 2, 3)
• Each event has zero or more tickets
• Each ticket has a non-zero price, expressed in US Dollars
• The distance between two points should be computed as the Manhattan distance

Program design

Assumptions

• The user is not introducing a reference position out of the [-10..10] range on any of X and Y axis;
• The 'proximity' algoritm that determins the closest points to the reference location is set as 'less than 15', as a Manhattan distance to the other points;
• There cannot be duplicated events in the venues hash, or duplicated locations;

Implementation

• Each point contains a X-coordinate, and a Y-coordinate;
• The pointsDatabase is an array of points. A pointID is the index in this array;
• Only 30 points are randomly generated in order to cover the required 3, 5, and 12 distances;
• Each ticket contains the number of tickets available, a price (default is 1), and a currency (default is USD);
• The ticketsDatabase is an array of tickets. A ticketID is the index in this array;
• Each event contains an identifier (between 0 and 999), and a ticketID;
• The eventsDatabase is an array of events. A eventID is the index in this array;
• Each venue is made up of a hash with the eventID, as key, and a pointID, as value;
• The venuesDatabase is an object containing the number of venues (as the object length), and hash with available venues;
• Current implementation contains:
  • 30 locations (points),
  • 10 tickets types,
  • 10 events, and
  • 6 venues

Data structures diagram

Results

$ npm start

> event_manager@0.1.0 start /home/ubuntu/workspace
> node app/app.js

Please enter a position (x,y) or <ctrl>D to exit:
> 4,2
Closest events to (4,2):
Event 003 - $30.29, Distance 3
Event 001 - $35.20, Distance 5
Event 006 - $01.40, Distance 12
> end

Task list for the data collections

• Memory implementation of point, ticket, event, venue collections
• Adding nedb lightweight JavaScript database

Questions

How might you change your program if you needed to support multiple events at the same location?

• Under current implementation, a simple way is to expand the Venue hash value from a pointID, to an array of pointIDs, to allow the multiple locations to be added for an event. The length of the array will give the number of events hold at that location (point);
• A better implementation, would be to change the arrays/hashes implementation to a lightweight database like nedb, and add camo as a data mapper to get the models mapped to database collections. With this aproach, we only need to add a 'belongs to'-like colomn in the Event model, to contain the pointID from the Points collection.

How would you change your program if you were working with a much larger world size?

I would think of several changes that are needed in order to support larger data storage, fast queries, optimisations to avoid often database access.

• Change all data storage from current arrays/hash to a database like MongoDB, to accommodate a larger data collections to be stored in a persistent environment;
• Adding an algorithm to optimize the search times. I would make a list of the 'Five hotest events', and keep it into the memory. This will avoid accessing the database for every query, making the application faster.
• Replace the simplistic 'proximity' algorithm to a more comprehensive one to take into account serveral criteria.
  • If two locations have the same Manhattan distance to the reference point, and not both will fit into the 'Five closest', I would consider 'time of travel' for instance, and choose the quickest one into the list.
  • Expand the input to add 'number of tickets'. If not all of the 'Five closest' events hold the required number of tickets, I would discad them from the list, and search for others further distance, that have the number of tickets required;
  • Maybe, take into account desired facilities at the venue. Venues not meeting the requests will be discarded from the 'Five closest' list.