I consider myself an Advanced/Expert Django developer but I also think that there is always something new to learn and that’s exactly what you expect from a book coming from Daniel and Audrey (authors will forgive me if I call them by name to help the conversational nature of this post). The book “Two Scoops of Django 1.6“ meets the expectations and in most cases it exceed them. There is a great layout and a great use of convention that makes the read really easy and also fun thanks to the nice Ice Cream related examples.
As someone of you that is already using celery already know extend the celery logger was a bit tricky until the last version, mainly because the logger object is not unique, the same handler is added to different logs object (Main Process logger, PoolWorker logger, TaskLogger).
For this reason the command logging.getLogger(“Celery”) give you back only the Main Process logger.
From the version 2.2.7 of Celery is possible to extend all the logs object by using two new signals after_setup_logger and after_setup_task_logger.
This is another tutorial of the mturk series, in this one I will explain how to fetch the ready results from mturk trough python boto and how to approve or reject payments to the workers.
Before continue I suggest you to read my first tutorial about boto and mturk if you didn’t it already.
Well, before continuing for have a good test case I suggest you to publish some hits on the mturk sandbox and do it trough the workers sandxbox, in this way you will have some results ready to be fetched.Read more
This tutorial will be the first of many about mturk and Boto, a python interface to Amazon Web Services.
When I started to develop python tasks for automate some process by using amazon mturk was a little bit difficult found enough information about the usage of Boto and about mturk, for this reason I want to make those things easy for others developers that, like me some time ago, are starting to deal with Amazon Mturk.
Let’s start from the origins what you need is:
Boto library (2.0b4): you can install it with easy_install or download the package from the github page.
Amazon Web Services keys: create an account or login if you already have one on aws.amazon.com, after that go to you account admin panel and than security settings.
In the page scroll to the section Access credentials and keep note of the Access Key ID and Secret Access Key.
At the end of this post you will find the source code of a python sandbox that I write for have a general sandbox that work for all my celery tasks.
Anyway this is a general python code and you can use it also without Celery but here I will give you also some example to how to use it in easy and fast way with Celery.Read more
If you are reading this article probably you already know what is Celery but for who think that it is something to eat I will give you a definition.
Celery is an asynchronous task queue/job queue based on distributed message passing developed in python.
If your using Celery for your back-office tasks will be very nice to run some tasks after an action performed on the front end of your web site, like an user action.
If your web front end is wrote in PHP there is an easy and useful way for do it trough Rabbit MQ, the message broker used by celery.Read more