4. Distributed Applications – with Celery | Distributed Computing with Python

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Distributed Computing with Python

By : Pierfederici

4.3 (3)

Distributed Computing with Python

4.3 (3)

By: Pierfederici

Overview of this book

CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. An Introduction to Parallel and Distributed Computing

1. An Introduction to Parallel and Distributed Computing

Parallel computing

Distributed computing

Shared memory versus distributed memory

Amdahl's law

The mixed paradigm

Summary

2. Asynchronous Programming

2. Asynchronous Programming

Coroutines

An asynchronous example

Summary

3. Parallelism in Python

3. Parallelism in Python

Multiple threads

Multiple processes

Multiprocess queues

Closing thoughts

Summary

4. Distributed Applications – with Celery

4. Distributed Applications – with Celery

Establishing a multimachine environment

Installing Celery

Testing the installation

A tour of Celery

More complex Celery applications

Celery in production

Celery alternatives – Python-RQ

Celery alternatives – Pyro

Summary

5. Python in the Cloud

5. Python in the Cloud

Cloud computing and AWS

Creating an AWS account

Creating an EC2 instance

Storing data in Amazon S3

Amazon elastic beanstalk

Creating a private cloud

Summary

6. Python on an HPC Cluster

6. Python on an HPC Cluster

Your typical HPC cluster

Job schedulers

Running a Python job using HTCondor

Running a Python job using PBS

Debugging

Summary

7. Testing and Debugging Distributed Applications

7. Testing and Debugging Distributed Applications

The big picture

Common problems – clocks and time

Common problems – software environments

Common problems – permissions and environments

Common problems – the availability of hardware resources

Challenges – the development environment

A useful strategy – logging everything

A useful strategy – simulating components

Summary

8. The Road Ahead

8. The Road Ahead

The first two chapters

The tools

The cloud and the HPC world

Debugging and monitoring

Where to go next

Index

Index

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More complex Celery applications

We will implement two simple but interesting applications using Celery. The first one is a reimplementation of the currency exchange rate example from Chapter 3, Parallelism in Python, and the second one is a distributed sort algorithm.

We are going to use a total of four machines again (HOST1, HOST2, HOST3, and HOST4) for all these examples. As we did before, machine one (HOST1) will run RabbitMQ. The second machine (HOST2) will run Redis, the third one (HOST3) will run Celery workers, and finally, the fourth one (HOST4) will run our main code.

Let's start with a simple example. Create a new Python script (celery/currency.py) and write the following code (if you're not using Redis, remember to change backend to 'amqp://HOST1'):

import celery
import urllib.request


app = celery.Celery('currency',
                    broker='amqp://HOST1',
                    backend='redis://HOST2')


URL = 'http://finance.yahoo.com/d/quotes.csv?s={}=X&f=p'

@app.task...

Visually different images

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