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PoKeMoNg

This is a Quarkus / MongoDB app for educational purposes.

Instructions are here for reference.

About

A "Pokemong" is a playful term for a MongoDB pocket monster.

The application is developed using the Quarkus framework and uses MongoDB as its database.

This application is a RESTful service designed to emulate a basic Pokemong management system. It allows users to perform CRUD operations on Pokemongs, Trainers, and Moves.

🗂️DCM

Let's cover the entities and relationships in this Data Concept Model:

Trainer

These are the individuals who capture and train pokemongs. They can engage in battles with other trainers.

  • a trainer has fought between 0 and many trainers
    • we will use referencing, since this is a reflexive relationship
  • a trainer owns between 0 and many pokemongs
    • we will use referencing with denormalizing, since pokemongs have lifecycles of their own

Pokemong

These are the creatures that trainers capture and train. They can be trained or wild.

  • a pokemong is owned by 0 or 1 trainer
    • we will use referencing, since trainers have lifecycles of their own, but no denormalizing, since no queries need that
  • a pokemong has 1 or 2 types
    • we will use embedding, since types don't have lifecycles of their own
  • a pokemong knows between 0 and 4 moves
    • we will use referencing with denormalizing, since moves have lifecycles of their own

Move

These are the abilities or actions that a pokemong can perform. This covers the strategic aspects of battles, as different moves can have different effects and powers depending on the type of the pokemong and the move.

  • a move can be known by between 0 and zillions of pokemongs
    • we will let pokemongs refer to moves, and not the other way around
  • a move has 1 and only 1 type
    • we will use embedding, since types don't have lifecycles of their own

Type

These define the elements or categories that a pokemong or a move can belong to.

  • a type can define between 0 and zillions of pokemongs
  • a type can define between 0 and zillions of moves

Data Concept Model

🧬UML Class diagram

Omitting some details, our entities look like this:

classDiagram

    class Trainer {
        + id: ObjectId
        + name: string
        + dob: date
        + wins: int
        + losses: int
    }

    class Pokemong {
        + id: ObjectId
        + nickname: string?
        + dob: date
        + level: int
        + pokedexId: int
        + evoStage: int
        + evoTrack: PokemongName[]
    }

    class Move {
        + id: ObjectId
        + name: string
        + category: MoveCategoryName
        + power: int
        + accuracy: int
    }

    class Type {
        + id: ObjectId
        + name: TypeName
        + weakAgainst: TypeName[]
        + effectiveAgainst: TypeName[]
    }

    class TypeName {
        <<enumeration>>
        + FIRE
        + WATER
        + ...
    }

    class PokemongName {
        <<enumeration>>
        + BULBASAUR
        + IVYSAUR
        + ...
    }

    class MoveCategoryName {
        <<enumeration>>
        + PHYSICAL
        + SPECIAL
        + STATUS
    }

    Trainer --> "0..*" Trainer: pastOpponents
    Trainer --> "0..*" Pokemong: pokemongs
    Pokemong --> "0..1" Trainer: trainer
    Pokemong --> "0..4" Move: moveSet
    Pokemong --> "1..2" Type: types
    Move --> Type: type

    Type ..> TypeName
    Pokemong ..> PokemongName
    Move ..> MoveCategoryName

🗺NoSQL Schema Versioning Strategy

This application uses MongoDB, a NoSQL database, which provides flexibility in our data model. While this flexibility has its advantages, it poses a unique challenge when we need to update our data model, specifically when we want to introduce breaking changes in the existing schema.

We have adopted a schema versioning strategy to overcome this challenge and manage these changes efficiently.

Schema Versioning Pattern

Schema versioning is a pattern that involves tagging each document in a collection with a version number. This version number corresponds to the schema of the document and is used to handle schema changes in the code that reads these documents.

Each entity in our model extends a GenericVersionedEntity class, which includes a schemaVersion field. This field is an integer that starts at 1 and is to be incremented by one with each schema change. Every change to the schema needs to involve the schema version number being incremented.

Incremental Document Migration

When a document is read from the database, the version number in the document is checked. If the version number is less than the current version, the document is updated to the current version, and the updated document is written back to the database. This process effectively migrates the document to the current version.

In the example of the Move class, the codec's decodeV1 method handles documents with a schemaVersion of less than 2. When it reads a document with this version, it updates the schemaVersion to 2, and writes the updated document back to the database.

Move decodeV1(Document document){
        // ...
        // Increment the schemaVersion to the current version
        move.setSchemaVersion(2);

        // Save the updated Move object back to the database
        moveRepository.persistOrUpdate(move);
        // ...
        }

This strategy allows for graceful schema evolution in a NoSQL environment. Instead of requiring all documents to be migrated at once, which can be a time-consuming operation for large collections, it enables incremental document migration. This approach also helps to avoid downtime during schema migration, as the application continues to function correctly regardless of the document version. As documents are read, they are updated to the current schema version, allowing the schema migration to happen gradually over time.

However, note that this strategy increases write operations to the database, which could affect application performance.

📇Indexes

Various indexes were created for fields that would often be queried in a dashboard situation. If there is an additional reason, it will be specified below.

Unless otherwise specified, please consider indexes to be full, and ascending.

moves collection

In the front-end app, these are queried both in the detail screen and in the list screen.

  • name
  • power: Descending, because users are more likely to sort them in that order.
  • type

pokemongs collection

  • nickname: This field already has a dedicated endpoint for a nickname search filter.
  • dob: Descending, because users are more likely to sort them in that order.
  • evoStage: "Species" is calculated as evoTrack[evoStage], and would often be queried.
  • evoTrack: See evoStage. Yes, it's an array, but it's a one-to-few relationship.
  • trainer: Partial index, to avoid indexing wild pokemongs there.
  • types: It's an array, but it's a one-to-few relationship.

trainers collection

It was tempting to index pastOpponents and pokemongs in the trainers collection, but these arrays could grow indefinitely, and the indexes may grow so large that they wouldn't fit in a server's RAM anymore.

  • name
  • wins: Descending, because users are more likely to sort them in that order for rankings.
  • losses: Descending, because users are more likely to sort them in that order for rankings.

🐕‍🦺Services

Each entity (Pokemong, Trainer, Move) in the application has a corresponding service class. These service classes are responsible for handling the business logic related to their respective entities. They interact with the database through their associated repositories, performing CRUD operations.

All service classes inherit from a GenericService class, which provides the following methods:

  • addOne(T entity): Adds a new entity to the database, after validating it.
  • getOneById(String id): Retrieves a single entity from the database by its ID.
  • getAll(): Retrieves all entities of a certain type from the database.
  • deleteOneById(String id): Deletes an entity from the database by its ID.
  • updateOne(T entity): Updates an existing entity in the database. This method is meant to be overridden in child service classes to provide the specific update logic for each type of entity.
  • updateAll(List<T> entities): Updates all entities in a given list. Each entity is validated before updating.

These methods allow the application to perform all the basic CRUD operations on any type of entity. The specific logic for each type of entity (like how to validate a pokemong, how to update a move, etc.) is provided in the child service classes that inherit from GenericService.

Many business rules were applied, which can be browsed here.

This diagram attempts to show the relationship between services in this API

classDiagram
    class GenericService~T~ {
        -GenericRepository~T~ repository
        +setRepository(GenericRepository~T~ repository)
        +addOne(T entity): T
        +validateOne(T entity)
        +getOneById(String id): T
        +getAll(): List~T~
        +deleteOneById(String id)
        +updateOne(T entity): T
        +updateAll(List~T~ entities)
    }

    class MoveService {
        -MoveRepository moveRepository
        -PokemongService pokemongService
        +init()
        +validateOne(Move move)
        +getOneById(String id): Move
        +getAll(): List~Move~
        +deleteOneById(String id)
        +updateOne(Move move): Move
        +existsById(String moveId): boolean
        -batchUpdatePokemongTrainers(Move move)
        -migrateToV2(Move move): Move
    }

    class TrainerService {
        -TrainerRepository trainerRepository
        -PokemongService pokemongService
        +init()
        +addOne(Trainer trainer): Trainer
        +validateOne(Trainer trainer)
        +deleteOneById(String id)
        +updateOne(Trainer trainer): Trainer
        -transferNewlyArrivedTrainerPokemongs(...)
    }

    class PokemongService {
        -PokemongRepository pokemongRepository
        -MoveService moveService
        -TrainerService trainerService
        +init()
        +addOne(Pokemong pokemong): Pokemong
        +validateOne(Pokemong pokemong)
        +deleteOneById(String id)
        +updateOne(Pokemong pokemong): Pokemong
        +existsById(String pokemongId): boolean
        -updateTrainerPokemong(...)
        +findByMove(String id): List~Pokemong~
        +isEvoValid(String id, PokemongName species): boolean
        +batchUpdatePokemongTrainers(...)
    }

    GenericService <|-- "T <- Move" MoveService
    GenericService <|-- "T <- Trainer" TrainerService
    GenericService <|-- "T <- Pokemong" PokemongService

🌺Special requests

This API goes a little bit beyond basic CRUD operations.

Pokemong by nickname

Using a MongoDB filter with a regex, pokemongs are searchable by nickname with the URL /pokemong/nickname/{nickname} where {nickname} is a partial, case-insensitive search term.

Pokemong in date interval

Users can also use the route pokemong/dob/{start-date}/{end-date} to search for pokemongs who where born within that interval (bounds included).

🦚Aggregation pipeline

Finally, the endpoint pokemong/count-by-evo-stage is provided, to get a mapping of evolution stages with the number of pokemongswho achieved that evolution stage.

As an example of a potential output:

[
  {
    "count": 15,
    "evoStage": 0
  },
  {
    "count": 4,
    "evoStage": 1
  },
  {
    "count": 5,
    "evoStage": 2
  }
]

👔Some business rules

Move CRUD cascade

  • When you delete a move, it also gets deleted from any pokemong's moveSet.
  • Since pokemongMove is denormalized on the name field, that field also gets updated when a move's name is updated.

Pokemong CRUD cascade

  • When a pokemong is created, the new pokemong's information is also added to the pokemongs array of any associated trainer documents.
  • When a pokemong is deleted, the pokemongs array in the associated trainer documents also has that specific pokemong removed.
  • Since trainerPokemong is denormalized on the nickname and species fields, those fields also get updated when a pokemong's nickname is updated, or when a pokemong evolves.

Trainer CRUD cascade

  • When a trainer is created, the new trainer's information is also updated in the trainer field of any associated pokemong documents. Since a pokemong can only belong to one trainer at a time, that may mean removing it from one to give it to the other.
  • When a trainer is deleted, the trainer field in the associated pokemong documents is also removed.

Prep steps

♨️Java version

This project is set up to use Java 17.

Your build will fail if the version of Java that your build tools are using does not match that.

💻 Run from command line

You should have JDK 17 installed locally, and accessible to Gradle.

That may involve updating your JAVA_HOME and Path environment variables.

🛠️ Run from an IDE

If you're planning to run this app directly from an IDE like IntelliJ, make sure to update any Gradle JVM (or similar) settings to use JDK 17 for Gradle tasks

🔐Database connection

Note that the DB connection properties are not included -- your src/main/resources/application.properties should look like this :

quarkus.mongodb.connection-string=mongodb+srv://<username>:<password>@<cluster>.<node>.mongodb.net
quarkus.mongodb.database=<database>
🏫 If you are the corrector

To be able to use this app, please place the provided application-dev.properties inside src > main > resources.

👥 If you are another user or developer

To be able to use this app, first create a MongoDB database, either locally or on their Atlas Cloud, then update application.properties with your database secrets.

You may want to look up the nice MongoDB official documentation if you get stuck.

Running the application in dev mode

You can run the application in dev mode using:

./gradlew quarkusDev

API testing

🧪Sample dataset

🏫 If you are the corrector

The database should already be populated with the sample dataset.

However, if you want to reload that dataset, please navigate to the root of this project in a terminal and run the provided load_data.sh script.

Or you may follow the alternate procedure below.

👥 If you are another user or developer

You can find a sample dataset at data/sample-dataset/. Each JSON file contains a collection.

For example, to load the moves collection into an existing MongoDB cluster, you may use MongoDB Shell ("mongosh") to run

mongoimport --uri=mongodb+srv://<username>:<password>@<cluster>.<node>.mongodb.net/<databasename> --collection=moves --file=./data/sample-dataset/moves.json

You can then do the same, but changing moves for pokemongs, and then trainers

🩺API testing tools

You can use an API testing tool such as Postman or Insomnia to test this app.

If you use Postman, you can even import data/postman_collection.json, designed to work with the 🧪 Sample dataset.

📱Front end

A corresponding front-end app comes into play for trying out this API.

⚠️ That only includes the Move entity, so Postman seems like your best option at the moment.

🏴‍☠️SwaggerUI

Thanks to this project's OpenAPI specs, you can explore the API in a lot of ways. A popular choice is SwaggerUI -- after you run the app, just go to http://localhost:8080/q/swagger-ui and have fun.

⚠️ Swagger or Quarkus or SmallRye adds the field id to all request examples, but in fact you should NOT include id when you POST or UPDATE a new document. The app takes care of it for you. Same thing for the field species with Pokemong documents.

Known limitations

🔀Types are left at the user's mercy

This API doesn't ensure that a Move can't be both effective against a type and weak against that type. It probably should.

But then again, this API doesn't deal with types very much at all anyway. Users are free to create all sorts of weird types within pokemongs and moves, such as a Pikachu with GRASS type effective against ROCK, who has an Ember move with GRASS type weak against ROCK and effective against FLYING...

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A Quarkus MongoDB "pokemong" management API for educational purposes

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