jobtastic 0.1.0b1

# jobtastic- Celery tasks plus more awesome

Jobtastic is a [Celery](http://celeryproject.org) library
that makes your user-responsive long-running 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_run` and `delay_or_fail`)
* A [celery jQuery plugin](https://github.com/PolicyStat/jquery-celery)
for easy client-side progress display
* Result caching
* [Thundering herd](http://en.wikipedia.org/wiki/Thundering_herd_problem) avoidance

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. 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),
('denominator', str),
]
# How long should we give a task before assuming it has failed?
herf_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.

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=10)
# 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 4
member variables, we've done all of these things.

Basically, creating a Celery task using Jobtastic is a matter of:

1. Subclassing `jobtastic.task.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=request.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),
('denominator', 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 behavior a bit, but you can usually ignore them
(assuming you want to cache identical task results forever).

#### cache_duration

If you want your results cached, set this to a positive 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 `signficant_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 always not set this,
and 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 shouldn't.

### Method to Override

Other than those two member variables, you'll probably want to actually 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_run` 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 `signficant_kwargs`
implementation, and totally fixable if someone wants to submit a pull request.

### delay_or_run

If your broker is behaving, 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. If you have a task that
realistically only takes a few seconds to run, this might be better than giving
an error message.

### delay_or_fail

Like `delay_or_run`, 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
flowers?

For tasks that might take a while or consume a lot of RAM, you're probably
better off using this than `delay_or_run` because you don't want to make a
resource problem worse.

## 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.

## Is it Awesome?

Yes. Increasingly so.

## A note on usage with Flask

If you're using Flask instead of Django, then the only currently-supported way
to work with Jobtastic is with Memcached as your `CELERY_RESULT_BACKEND`. A
more generally-pythonic way of choosing/plugging cache backends is definitely a
goal, though, and pull requests
(see [Issue 8](https://github.com/PolicyStat/jobtastic/issues/8)
or suggestions are very welcome.

## Project Status

Jobtastic is being in production on a large Django project with RabbitMQ as a
broker and Memcached as a result backend. If that's your configuration, then
you're in good shape. For other configurations, there are probably bugs that
will need to be ironed out.

Jobtastic is currently known to work with Django 1.3.x and Celery 2.5.x. The
goal is to support those versions and newer. Please file issues if there are
problems with newer versions of Django/Celery.

## 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.