Book Image

Parallel Programming with Python

By : Jan Palach, Jan Palach V Cruz da Silva
Book Image

Parallel Programming with Python

By: Jan Palach, Jan Palach V Cruz da Silva

Overview of this book

Table of Contents (16 chapters)
Parallel Programming with Python
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Using Celery to obtain a Fibonacci series term


Let us again go and distribute our multiple inputs in order to calculate the nth Fibonacci term, each of them in a distributed way. The function that calculates Fibonacci will change a little in relation to the previous chapters. The changes are small; now we have the @app.task decorator and a small change in the return message.

In the tasks.py module (created previously), which is in the server machine where also the broker is, we will stop the execution of Celery (Ctrl + C) and add the fibo_task task. This is done by using the following code:

@app.task
def fibo_task(value):
    a, b = 0,1
    for item in range(value):
        a, b = b, a + b
    message = "The Fibonacci calculated with task id %s" \
        " was %d" % (fibo_task.request.id, a)
    Return (value, message)

A point to observe is that we obtain the ID of the task with the <task.request.id> call. The request object is an object in the task class, which provides a context to...