Skip to content

Latest commit

 

History

History
120 lines (100 loc) · 5.61 KB

README.md

File metadata and controls

120 lines (100 loc) · 5.61 KB
Raw

USED TECHNOLOGIES

  • NestJs with Fastify: Nodejs framework for server-side applications.
  • Docker: Containarization Tool.
  • Github Actions: Automated deployment of the code.
  • EC2: AWS virtual machine service for running the app.
  • DynamoDB: For database.

APP URL AND API DOCUMENTATION

USERS

  • Currently, there are 191 users in the system with some progress.
  • You can find their credentials in "user_credentials.txt" file to test the system with a user with some progress. You can also create a new user and test the system from scratch.

HOW TO RUN THE CODE IN LOCAL DEVELOPMENT ENVIRONMENT

Dependencies: Docker.

  1. First, you should create DynamoDB tables on AWS. Table definitions and indexes are given in the following section. You should also insert the first tournament entry by hand before running the application. You can use example values given in the DynamoDB Tables - Tournaments section.
  2. Second, clone the project:
$ git clone <url>
$ cd good-blast-backend
  1. Third, create prod.env and dev.env files in the root folder. You can refer to prod.env.example and dev.env.example for how to fill it. Even if you do not use the production mode, you should still create prod.env file. However, it can be empty.
  2. Forth, you should run the following command:
$ docker-compose up app-dev

HOW TO DEPLOY THE CODE AND RUN IT IN PRODUCTION

  1. First, you should create DynamoDB tables on AWS. Table definitions and indexes are given in the following section. You should also insert the first tournament entry by hand before running the application. You can use example values given in the DynamoDB Tables section.
  2. Second, open an EC2 instance and install git, docker, and docker-compose.
  3. Third, clone the project:
$ git clone <url>
$ cd good-blast-backend
  1. Forth, create prod.env and dev.env files in the root folder. You can refer to prod.env.example and dev.env.example for how to fill it. Even if you do not use the development mode, you should still create dev.env file. However, it can be empty.
  2. Run:
$ docker-compose app-prod

DynamoDB Tables

Users

  • Partition key: username (String)
  • Sort key: none
  • Indexes:
    • CountryCodeGSI:
      • Partition Key: countryCode
      • Sort Key: levelAndUsername
    • LevelGSI:
      • Partition Key: dummyPartitionKey
      • Sort Key: levelAndUsername
  • Attributes:
    • username
    • password
    • coins
    • levelNum
    • countryCode
    • currGroupId
    • claimedReward
    • joinedTournamentAt
    • levelAndUsername
    • dummyPartitionKey

Tournaments

  • Partition key: id
  • Sort key: none
  • Indexes:
    • isOngoingIndex:
      • Partition Key: isOngoing
      • Sort Key: id
  • Attributes
    • id
      • example: String - 376a3106-4507-455d-8a21-2eafdd639bbe
    • startTime
      • example: String - 2024-01-29T00:00:00.362Z
    • duration
      • example: Number - 86400
    • isOngoing
      • example: String - true
    • availableGroupId
      • example: String - 4596b647-e51a-4f0d-b8ca-1547e16d06ad
    • availableGroupItemCount
      • example: Number - 0

TournamentGroups

  • Partition key: groupId
  • Sort key: username
  • Attributes
    • groupId
    • username
    • tournamentId
    • tournamentScore

APPLIED PERFORMANCE OPTIMIZATIONS

  • Created isOngoingIndex on Tournaments table: This index is created in order to optimize the query which fetches the ongoing tournament. There will be many tournaments in the system but only one tournament will be active. Using this index, we can get the ongoing tournament directly.
  • Created LevelGSI index on Users table: This index is used to store users in a sorted manner based on their levels. I created a dummy partition key which has the same value for every item and a sort key called levelAndUsername in order to ensure uniqueness. I add 0’s at the beginning of levelAndUsername string until making it 7 digit in order to satisfy consistency between string comparison and level comparison. For example level 78 is represented as follows: “0000078#”. I tested the system with 2500 users the result is always returned between 1 - 1.5 seconds.
  • Created CountryCodeGSI index on Users table: This index is used to store users in a sorted manner based on their levels for each country. They are partitioned based on their countryCode and stay sorted based on their levelAndUsername attribute. Remaining logic is similar to LevelGSI index. I tested the system with 2500 users and the result is always returned between 1 - 1.5 seconds.
  • Utilized transactions when there are multiple db accesses in the same operation. This both improves performance and ensure consistency in database.
  • Created two different docker targets for development and production for better development experience and enhanced production performance

FUTURE WORK

  1. Integrating Redis: Especially for GET /api/leaderboard/global and GET /api/leaderboard/country/:countryCode endpoints, we can utilize caching to improve speed and reduce database load.
  2. Adding Rate-Limiting: In order to prevent attacks, we can put a throttler for each endpoint.
  3. Adding Unit Tests: I could not implement unit tests in the given time. However, they are crucial for robust systems, therefore they should be definitely added.
  4. Adding Pagination: For GET /api/leaderboard/global and GET /api/leaderboard/country/:countryCode endpoints, there should be pagination.