Using asynchronous background tasks for resource-intensive computations

There are implementations of many approximation algorithms and P-complete problems that can create memory-related issues, thread problems, or even memory leaks. To avoid imminent problems when handling solutions for NP-hard problems with indefinite data sets, implement the solutions using asynchronous background tasks.

But first, install the celery client using the pip command:

pip install celery

Also, install the Redis database server for its broker. Place celery_config.py, which contains celery_init_app(), in the project directory and call the method in the main.py module.

After the setup and installations, create a service package in the Blueprint module folder. ch06-project has the following Celery task in the hpi_formula.py service module found in the internal Blueprint module:

@shared_task(ignore_result=False)
def compute_hpi_laspeyre(df_json):
    async def compute_hpi_task...