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Ghost Cloud | Redis Hackathon Submission

Description

Ghost Cloud is an online platform for sharing files, and its a cloud-first microservices application. Once someone uploads a file it becomes publicly available in the platform and other persons can also view and save it. With the help of redis, tasks like file compression and uploading are completely asynchronous and Event Driven for the user.

The application consists of 5-tier microservices. That are performing various tasks.

This Project is using the following features of Redis

  • Storing Data in database with the help of JSONModels ✅
  • Background Tasks like File compresion are using Pub/Sub features ✅
  • Syncing different web servers by using redis as a message broker ✅

Screenshots

Home Page Save File Page
Home Page Screenshot of Save file screen
Upload File View File
Upload File Dialog View File

Overview video

Here's a short video that explains the project and how it uses Redis:

YouTube Video

Architecture

Ghost Cloud is composed of 4 microservices and a frontend which connects everything together.

Architecture of services

How it works

How the data is stored:

  • There are different ways, in which the data is been saved in the system.

    • In Database Models

      Using redis-om python client to create models.

      File Model

      class File(JsonModel):
    '''
    This model represents a single file
    Operations like searching are performed using this model
    '''

    # name of the file
    name: str = Field(index=True, full_text_search=True)

    # content type of the file
    type: str

    # file size in bytes
    size: int

    # upload id, this id connects this file object
    # with another JSON model where more information
    # of the file is available.
    upload_id: str

    # tells whether file is uploaded, compressing, or compressed
    status: str

      UploadFile Model

      class FileUpload(HashModel):
    '''
    This model represents a customer
    '''

    # name of the file
    name: str
    
    # content type of the file
    type: str
    
    # file size in bytes
    size: int

    # location of the file on the system
    upload_path: str

      To save an instance of a Model the following way is used.

      To save a file sent via FastAPI request.

      @app.put("/files/upload/")
async def upload_file(product: UploadFile):
    ...
    file_upload = FileUpload(
        name=file.filename,
        type=file.content_type,
        size=Path(upload_path).stat().st_size,
        upload_path=upload_path,
    )
    file_upload.save()
    ...

    • Sync the data between File Upload and API server

      Using instance of data models itself, we can construct the payload.

      @app.put("/files/upload/")
async def upload_file(file: UploadFile):
    ...
    # connect with api server via redis pub/sub
    data = {
        "name": file_upload.name,
        "type": file_upload.type,
        "size": file_upload.size,
        "upload_id": file_upload.pk,
        "status": "uploaded"
    }
    redis.publish("file_uploaded", json.dumps(data))
    ...

How the data is accessed:

  • There are different ways, in which the data can be accessed in the system.

    • From Database Models

      This is how to access all the products in a system, and return as FastAPI Response.

      @app.get("/files/")
async def list_files():
    return [serialize_files(pk) for pk in File.all_pks()]

    • Revieving data from other service using Pub/Sub

      def file_uploaded(data: dict):
    """
    Updates the API server once the file is uploaded
    successfully on the file upload server.
    """
    print("saving:", data)
    ...

def upload_event_listener():
    """
    Listens to the events emitted on file_uploaded
    channel and synchronises servers 
    """
    p = redis.pubsub()
    p.subscribe("file_uploaded")

    while True:
        message = p.get_message()

        if message and not message['data'] == 1:
            data = json.loads(message['data'])
            file_uploaded(data)

Performance Benchmarks

As compared to the previous case where GCP was using Memorystore (redis), having a lot of limitations and constraints, this project is using Redis Cloud.

Redis Cloud provides customers real-time performance with linear scaling to hundreds of millions of operations per second while providing local latency in a global Active-Active deployment with 99.999% uptime.

How to run it locally?

Step 1. Start the Redis Stack docker container

$ docker run -d --name redis-stack -p 6379:6379 redis/redis-stack:latest

Step 2. Clone the repository

$ git clone git@github.com:ankit-brijwasi/ghost-cloud.git
$ cd ghost-cloud
$ docker compose up --build

NOTE: Wait for the frontend container to start serving

Now, open http://localhost:3000/ in browser.

Prerequisites

  • Git
  • Docker & docker compose
  • A redis cloud account (optionally)

More Information about Redis Stack

Here some resources to help you quickly get started using Redis Stack. If you still have questions, feel free to ask them in the Redis Discord or on Twitter.

Getting Started

  1. Sign up for a free Redis Cloud account using this link and use the Redis Stack database in the cloud.
  2. Based on the language/framework you want to use, you will find the following client libraries:

The above videos and guides should be enough to get you started in your desired language/framework. From there you can expand and develop your app. Use the resources below to help guide you further:

  1. Developer Hub - The main developer page for Redis, where you can find information on building using Redis with sample projects, guides, and tutorials.
  2. Redis Stack getting started page - Lists all the Redis Stack features. From there you can find relevant docs and tutorials for all the capabilities of Redis Stack.
  3. Redis Rediscover - Provides use-cases for Redis as well as real-world examples and educational material
  4. RedisInsight - Desktop GUI tool - Use this to connect to Redis to visually see the data. It also has a CLI inside it that lets you send Redis CLI commands. It also has a profiler so you can see commands that are run on your Redis instance in real-time
  5. Youtube Videos

Happy Coding

Cheers