Introduction to the Dispatcher API

The Dispatcher API is a vital tool for handling task distribution and execution in modern applications. It provides a set of methods to efficiently manage background tasks, optimize load balancing, and enhance application performance. Below, you’ll find an extensive explanation of the Dispatcher API, with useful code snippets to help you get started.

Key API Methods

1. Dispatch

Use the dispatch method to send a task to be executed by the dispatcher.

  dispatcher.dispatch(task, args=(), kwargs={})

2. Delay

The delay method schedules a task to be executed after a certain delay.

  dispatcher.delay(task, delay_seconds, args=(), kwargs={})

3. Schedule

schedule allows you to run tasks at specific intervals.

  dispatcher.schedule(task, interval_seconds, args=(), kwargs={})

4. Cancel

To cancel a scheduled task, use the cancel method.

  dispatcher.cancel(task_id)

5. List Tasks

The list_tasks method returns all scheduled tasks.

  tasks = dispatcher.list_tasks()

6. Get Task Status

Check the status of any task using the get_status method.

  status = dispatcher.get_status(task_id)

Application Example

To illustrate the Dispatcher API in action, here’s a simple app that performs background data processing tasks.

  import time
  from dispatcher import Dispatcher

  dispatcher = Dispatcher()

  def process_data(data):
      # Simulate data processing
      time.sleep(2)
      print(f"Processed data: {data}")

  if __name__ == "__main__":
      # Dispatch data processing tasks
      dispatcher.dispatch(process_data, args=("Data1",))
      dispatcher.dispatch(process_data, args=("Data2",))
      dispatcher.dispatch(process_data, args=("Data3",))

      # Delay a task
      dispatcher.delay(process_data, 5, args=("Delayed Data",))

      # Schedule a recurring task
      dispatcher.schedule(process_data, 10, args=("Scheduled Data",))

In this example, the application uses dispatcher.dispatch to run process_data on three different sets of data. It also demonstrates delaying and scheduling tasks for data processing.

Using the Dispatcher API helps in efficient task distribution and ensures that functions are executed promptly, without overwhelming the system.

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