In the early days of computing, applications handled tasks sequentially. As the scale grew with millions of users, this approach became impractical. Asynchronous processing allowed handling multiple tasks concurrently, but managing threads/processes on a single machine led to resource constraints and complexity.

This is where distributed parallel processing comes in. By spreading the workload across multiple machines, each dedicated to a portion of the task, it offers a scalable and efficient solution. If you have a function to process a large batch of files, you can divide the workload across multiple machines to process files concurrently instead of handling them sequentially on one machine. Additionally, it improves performance by leveraging combined resources and provides scalability and fault tolerance. As the demands increase, you can add more machines to increase available resources.

Leave a Reply

Your email address will not be published. Required fields are marked *