Metadata-Version: 1.1
Name: jobtastic
Version: 1.0.0a2
Summary: Make your user-facing Celery jobs totally awesomer
Home-page: http://policystat.github.com/jobtastic
Author: Wes Winham
Author-email: winhamwr@gmail.com
License: BSD
Description-Content-Type: UNKNOWN
Description: # jobtastic- Celery tasks plus more awesome
        
        [![Build Status](https://travis-ci.org/PolicyStat/jobtastic.png?branch=master)](https://travis-ci.org/PolicyStat/jobtastic)
        
        Jobtastic makes your user-responsive long-running
        [Celery](http://celeryproject.org) jobs totally awesomer.
        Celery is the ubiquitous python job queueing tool
        and jobtastic is a python library
        that adds useful features to your Celery tasks.
        Specifically, these are features you probably want
        if the results of your jobs are expensive
        or if your users need to wait while they compute their results.
        
        Jobtastic gives you goodies like:
        * Easy progress estimation/reporting
        * Job status feedback
        * Helper methods for gracefully handling a dead task broker
          (`delay_or_eager` and `delay_or_fail`)
        * Super-easy result caching
        * [Thundering herd](http://en.wikipedia.org/wiki/Thundering_herd_problem) avoidance
        * Integration with a
          [celery jQuery plugin](https://github.com/PolicyStat/jquery-celery)
          for easy client-side progress display
        * Memory leak detection in a task run
        
        Make your Celery jobs more awesome with Jobtastic.
        
        ## Why Jobtastic?
        
        If you have user-facing tasks for which a user must wait,
        you should try Jobtastic.
        It's great for:
        * Complex reports
        * Graph generation
        * CSV exports
        * Any long-running, user-facing job
        
        You could write all of the stuff yourself, but why?
        
        ## Installation
        
        1. Install gcc and the python C headers
           so that you can build [psutil](https://github.com/giampaolo/psutil/blob/master/INSTALL.rst).
        
          On Ubuntu, that means running:
        
          `$ sudo apt-get install build-essential python-dev python2.7-dev python3.5-dev rabbitmq-server`
        
          On OS X, you'll need to run the "XcodeTools" installer.
        
        2. Get the project source and install it
        
            `$ pip install jobtastic`
        
        ## Creating Your First Task
        
        Let's take a look at an example task using Jobtastic:
        
        ``` python
        from time import sleep
        
        from jobtastic import JobtasticTask
        
        class LotsOfDivisionTask(JobtasticTask):
        	"""
        	Division is hard. Make Celery do it a bunch.
        	"""
        	# These are the Task kwargs that matter for caching purposes
        	significant_kwargs = [
        		('numerators', str),
        		('denominators', str),
        	]
        	# How long should we give a task before assuming it has failed?
        	herd_avoidance_timeout = 60  # Shouldn't take more than 60 seconds
        	# How long we want to cache results with identical ``significant_kwargs``
        	cache_duration = 0  # Cache these results forever. Math is pretty stable.
        	# Note: 0 means different things in different cache backends. RTFM for yours.
        
        	def calculate_result(self, numerators, denominators, **kwargs):
        		"""
        		MATH!!!
        		"""
        		results = []
        		divisions_to_do = len(numerators)
        		# Only actually update the progress in the backend every 10 operations
        		update_frequency = 10
        		for count, divisors in enumerate(zip(numerators, denominators)):
        			numerator, denominator = divisors
        			results.append(numerator / denominator)
        			# Let's let everyone know how we're doing
        			self.update_progress(
                        count,
                        divisions_to_do,
                        update_frequency=update_frequency,
                    )
        			# Let's pretend that we're using the computers that landed us on the moon
        			sleep(0.1)
        
        		return results
        ```
        
        This task is very trivial,
        but imagine doing something time-consuming instead of division
        (or just a ton of division)
        while a user waited.
        We wouldn't want a double-clicker to cause this to happen twice concurrently,
        we wouldn't want to ever redo this work on the same numbers
        and we would want the user to have at least some idea
        of how long they'll need to wait.
        Just by setting those 3 member variables,
        we've done all of these things.
        
        Basically, creating a Celery task using Jobtastic is a matter of:
        
        1. Subclassing `jobtastic.JobtasticTask`
        2. Defining some required member variables
        3. Writing your `calculate_result` method
          (instead of the normal Celery `run()` method)
        4. Sprinkling `update_progress()` calls in your `calculate_result()` method
          to communicate progress
        
        Now, to use this task in your Django view, you'll do something like:
        
        ``` python
        from django.shortcuts import render_to_response
        
        from my_app.tasks import LotsOfDivisionTask
        
        def lets_divide(request):
        	"""
        	Do a set number of divisions and keep the user up to date on progress.
        	"""
        	iterations = request.GET.get('iterations', 1000)  # That's a lot. Right?
        	step = 10
        
        	# If we can't connect to the backend, let's not just 500. k?
        	result = LotsOfDivisionTask.delay_or_fail(
        		numerators=range(0, step * iterations * 2, step * 2),
        		denominators=range(1, step * iterations, step),
        	)
        
        	return render_to_response(
        		'my_app/lets_divide.html',
        		{'task_id': result.task_id},
        	)
        ```
        
        The `my_app/lets_divide.html` template will then use the `task_id`
        to query the task result all asynchronous-like
        and keep the user up to date with what is happening.
        
        For [Flask](http://flask.pocoo.org/), you might do something like:
        
        ``` python
        from flask import Flask, render_template
        
        from my_app.tasks import LotsOfDivisionTask
        
        app = Flask(__name__)
        
        @app.route("/", methods=['GET'])
        def lets_divide():
        	iterations = request.args.get('iterations', 1000)
        	step = 10
        
        	result = LotsOfDivisionTask.delay_or_fail(
        		numerators=range(0, step * iterations * 2, step * 2),
        		denominators=range(1, step * iterations, step),
        	)
        
        	return render_template('my_app/lets_divide.html', task_id=result.task_id)
        ```
        
        ### Required Member Variables
        
        "But wait, Wes. What the heck do those member variables actually do?" You ask.
        
        Firstly. How the heck did you know my name?
        
        And B, why don't I tell you!?
        
        #### significant_kwargs
        
        This is key to your caching magic.
        It's a list of 2-tuples containing the name of a kwarg
        plus a function to turn that kwarg in to a string.
        Jobtastic uses these to determine if your task
        should have an identical result to another task run.
        In our division example,
        any task with the same numerators and denominators can be considered identical,
        so Jobtastic can do smart things.
        
        ``` python
        significant_kwargs = [
        	('numerators', str),
        	('denominators', str),
        ]
        ```
        
        If we were living in bizzaro world,
        and only the numerators mattered for division results,
        we could do something like:
        
        ``` python
        significant_kwargs = [
        	('numerators', str),
        ]
        ```
        
        Now tasks called with an identical list of numerators will share a result.
        
        #### herd_avoidance_timeout
        
        This is the max number of seconds for which Jobtastic will wait
        for identical task results to be determined.
        You want this number to be on the very high end
        of the amount of time you expect to wait
        (after a task starts)
        for the result.
        If this number is hit,
        it's assumed that something bad happened to the other task run
        (a worker failed)
        and we'll start calculating from the start.
        
        ### Optional Member Variables
        
        These let you tweak the default behavior.
        Most often, you'll just be setting the `cache_duration`
        to enable result caching.
        
        #### cache_duration
        
        If you want your results cached,
        set this to a non-negative number of seconds.
        This is the number of seconds for which identical jobs
        should try to just re-use the cached result.
        The default is -1,
        meaning don't do any caching.
        Remember,
        `JobtasticTask` uses your `significant_kwargs` to determine what is identical.
        
        #### cache_prefix
        
        This is an optional string used to represent tasks
        that should share cache results and thundering herd avoidance.
        You should almost never set this yourself,
        and instead should let Jobtastic use the `module.class` name.
        If you have two different tasks that should share caching,
        or you have some very-odd cache key conflict,
        then you can change this yourself.
        You probably don't need to.
        
        #### memleak_threshold
        
        Set this value to monitor your tasks
        for any runs that increase the memory usage
        by more than this number of Megabytes
        (the SI definition).
        Individual task runs that increase resident memory
        by more than this threshold
        get some extra logging
        in order to help you debug the problem.
        By default, it logs the following via standard Celery logging:
         * The memory increase
         * The memory starting value
         * The memory ending value
         * The task's kwargs
        
        You then grep for `Jobtastic:memleak memleak_detected` in your logs
        to identify offending tasks.
        
        If you'd like to customize this behavior,
        you can override the `warn_of_memory_leak` method in your own `Task`.
        
        ### Method to Override
        
        Other than tweaking the member variables,
        you'll probably want to actually, you know,
        *do something* in your task.
        
        #### calculate_result
        
        This is where your magic happens.
        Do work here and return the result.
        
        You'll almost definitely want to
        call `update_progress` periodically in this method
        so that your users get an idea of for how long they'll be waiting.
        
        ### Progress feedback helper
        
        This is the guy you'll want to call
        to provide nice progress feedback and estimation.
        
        #### update_progress
        
        In your `calculate_result`,
        you'll want to periodically make calls like:
        
        ``` python
        self.update_progress(work_done, total_work_to_do)
        ```
        
        Jobtastic takes care of handling timers to give estimates,
        and assumes that progress will be roughly uniform across each work item.
        
        Most of the time,
        you really don't need ultra-granular progress updates
        and can afford to only give an update every `N` items completed.
        Since every update would potentially hit your
        [CELERY_RESULT_BACKEND](http://celery.github.com/celery/configuration.html#celery-result-backend),
        and that might cause a network trip,
        it's probably a good idea to use the optional `update_frequency` argument
        so that Jobtastic doesn't swamp your backend
        with updated estimates no user will ever see.
        
        In our division example,
        we're only actually updating the progress every 10 division operations:
        
        ``` python
        # Only actually update the progress in the backend every 10 operations
        update_frequency = 10
        for count, divisors in enumerate(zip(numerators, denominators)):
        	numerator, denominator = divisors
        	results.append(numerator / denominator)
        	# Let's let everyone know how we're doing
        	self.update_progress(count, divisions_to_do, update_frequency=10)
        ```
        
        ## Using your JobtasticTask
        
        Sometimes,
        your [Task Broker](http://celery.github.com/celery/configuration.html#broker-url)
        just up and dies
        (I'm looking at you, old versions of RabbitMQ).
        In production,
        calling straight up `delay()` with a dead backend
        will throw an error that varies based on what backend you're actually using.
        You probably don't want to just give your user a generic 500 page
        if your broker is down,
        and it's not fun to handle that exception every single place
        you might use Celery.
        Jobtastic has your back.
        
        Included are `delay_or_eager` and `delay_or_fail` methods
        that handle a dead backend
        and do something a little more production-friendly.
        
        Note: One very important caveat with `JobtasticTask` is that
        all of your arguments must be keyword arguments.
        
        Note: This is a limitation of the current `significant_kwargs` implementation,
        and totally fixable if someone wants to submit a pull request.
        
        ### delay_or_eager
        
        If your broker is behaving itself,
        this guy acts just like `delay()`.
        In the case that your broker is down,
        though,
        it just goes ahead and runs the task in the current process
        and skips sending the task to a worker.
        You get back a nice shiny `EagerResult` object,
        which behaves just like the `AsyncResult` you were expecting.
        If you have a task that realistically only takes a few seconds to run,
        this might be better than giving yours users an error message.
        
        This method uses `async_or_eager()` under the hood.
        
        ### delay_or_fail
        
        Like `delay_or_eager`,
        this helps you handle a dead broker.
        Instead of running your task in the current process,
        this actually generates a task result representing the failure.
        This means that your client-side code can handle it
        like any other failed task
        and do something nice for the user.
        Maybe send them a fruit basket?
        
        For tasks that might take a while
        or consume a lot of RAM,
        you're probably better off using this than `delay_or_eager`
        because you don't want to make a resource problem worse.
        
        This method uses `async_or_fail()` under the hood.
        
        ### async_or_eager
        
        This is a version of `delay_or_eager()` that exposes the calling signature
        of `apply_async()`.
        
        ### async_or_fail
        
        This is a version of `delay_or_fail()` that exposes the calling signature
        of `apply_async()`.
        
        ## Client Side Handling
        
        That's all well and good on the server side,
        but the biggest benefit of Jobtastic is useful user-facing feedback.
        That means handling status checks using AJAX in the browser.
        
        The easiest way to get rolling is to use our sister project,
        [jquery-celery](https://github.com/PolicyStat/jquery-celery).
        It contains jQuery plugins that help you:
        * Poll for task status and handle the result
        * Display a progress bar using the info from the `PROGRESS` state.
        * Display tabular data using [DataTables](http://www.datatables.net/).
        
        If you want to roll your own,
        the general pattern is to poll a URL
        (such as the django-celery
        [task_status view](https://github.com/celery/django-celery/blob/master/djcelery/urls.py#L25) )
        with your taskid to get JSON status information
        and then handle the possible states to keep the user informed.
        
        The [jquery-celery](https://github.com/PolicyStat/jquery-celery/blob/master/src/celery.js)
        jQuery plugin might still be useful as reference,
        even if you're rolling your own.
        In general, you'll want to handle the following cases:
        
        ### PENDING
        
        Your task is still waiting for a worker process.
        It's generally useful to display something like "Waiting for your task to begin".
        
        ### PROGRESS
        
        Your task has started and you've got a JSON object like:
        
        ``` javascript
        {
        	"progress_percent": 0,
        	"time_remaining": 300
        }
        ```
        
        `progress_percent` is a number between 0 and 100.
        It's a good idea to give a different message if the percent is 0,
        because the time remaining estimate might not yet be well-calibrated.
        
        `time_remaining` is the number of seconds estimated to be left.
        If there's no good estimate available, this value will be `-1`.
        
        ### SUCCESS
        
        You've got your data. It's time to display the result.
        
        ### FAILURE
        
        Something went wrong and the worker reported a failure.
        This is a good time to either display a useful error message
        (if the user can be expected to correct the problem),
        or to ask the user to retry their task.
        
        ### Non-200 Request
        
        There are occasions where requesting the task status itself might error out.
        This isn't a reflection on the worker itself,
        as it could be caused by any number of application errors.
        In general, you probably want to try again if this happens,
        but if it persists, you'll want to give your user feedback.
        
        ## Running The Test Suite
        
        We use [tox](https://tox.readthedocs.org/en/latest/)
        to run our tests against various combinations
        of python/Django/Celery.
        We only officially support
        the combinations listed in our `.travis.yml` file,
        but we're working on
        ([Issue 33](https://github.com/PolicyStat/jobtastic/issues/33))
        supporting everything defined in `tox.ini`.
        Until then,
        you can run tests against supported combos with:
        
            $ pip install tox
            $ tox -e py27-django1.8.X-djangocelery3.1.X-celery3.1.X
        
        Our test suite currently only tests usage with Django,
        which is definitely a [bug](https://github.com/PolicyStat/jobtastic/issues/15).
        Especially if you use Jobtastic with Flask,
        we would love a pull request.
        
        ## Dynamic Time Estimates via JobtasticMixins
        
        Have tasks whose duration is difficult to estimate
        or that doesn't have smooth progress?
        [JobtasticMixins](https://github.com/abbasovalex/JobtasticMixins)
        to the rescue!
        
        JobtasticMixins provides an `AVGTimeRedis` mixin
        that stores duration date in a Redis backend.
        It then automatically uses this stored historical data
        to calculate an estimate.
        For more details,
        check out [JobtasticMixins](https://github.com/abbasovalex/JobtasticMixins)
        on github.
        
        ## Is it Awesome?
        
        Yes. Increasingly so.
        
        ## Project Status
        
        Jobtastic is currently known to work
        with Django 1.6+ and Celery 3.1.X
        The goal is to support those versions and newer.
        Please file issues if there are problems
        with newer versions of Django/Celery.
        
        ### A note on usage with Flask
        
        Previously,
        if you were using Flask instead of Django,
        then the only currently-supported way to work with Jobtastic
        was with Memcached as your `CELERY_RESULT_BACKEND`.
        
        Thanks to @rhunwicks this is no longer the case!
        
        A cache is now selected with the following priority:
        
        * If the Celery appconfig has a `JOBTASTIC_CACHE` setting and it is a valid cache, use it
        * If Django is installed, then:
            - If the setting is a valid Django cache entry, then use that.
            - If the setting is empty use the default cache
        * If Werkzeug is installed, then:
            - If the setting is a valid Celery Memcache or Redis Backend, then use that.
            - If the setting is empty and the default Celery Result Backend is Memcache or Redis, then use that
        
        ## Non-affiliation
        
        This project isn't affiliated with the awesome folks at the
        [Celery Project](http://www.celeryproject.org)
        (unless having a huge crush counts as affiliation).
        It's a library that the folks at [PolicyStat](http://www.policystat.com)
        have been using internally
        and decided to open source in the hopes it is useful to others.
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: System :: Distributed Computing
Classifier: Topic :: Software Development :: Object Brokering
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Operating System :: OS Independent
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Framework :: Django
