WebScreenShotApp

Bulk Screen Shotter, captures screen of given website links.

Installation

If you don't want to pollute your system, install docker, locate to the folder which contains docker-compose.yml and hit the command below:

docker-compose up --build 

If you want to try on your system; you need to download&install:

• .NET Core 3.1
• Mongo DB

Usage

Basically, there is a console application to communicate with service workers. There are 4 different choices to capture web screens with saving into database and to download captured images. You have to give two arguments as input: operation and input option.

Capture screens from url and Save:

 1 "http://www.google.com;http://www.microsoft.com" 

Capture screens from file which contains comma separated links and also named with Input*.txt pattern:

 2 "C:/ScreenShot/Input1.txt" 

Retrieve captures by url and Download:

 3 "http://www.google.com;http://www.microsoft.com" 

Retrieve captures by file which contains comma separated links and also named with Output*.txt pattern:

 4 "C:/ScreenShot/Output1.txt" 

• Retrieving can be made by giving two different inputs with comma separated values: date or site url.

  http://www.google.com;http://www.microsoft.com

  2005-05-05 22:12-20015-05-05 23:12;2020-01-05 22:12-2020-02-05 23:12

Running from Docker

After mapping local folders to virtual images, you can execute commands. In the app.config file of console application and in the appsettings.json file of Service application; we need to map C:/ScreenShot folder to be able to see InputFilesFolder and OutputFilesFolder parameters.

 docker run -i -v c:\screenshot:c:\screenshot screenshotconsole 2 "C:/ScreenShot/Input1.txt"
 #simply we bind c:\screenshot folder of our host system to image's folder

Scalability

Since ScreenShotService is multithreaded service application, you can use appsettings.json file to set thread count for application-wide utilizations. For storage-wide scaling a supersonic document oriented NoSQL database helps us : mongoDB. With sharding(horizontal partitioning) data into different servers, mongoDB also comes with high performance additional to scalability.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.