======= Tasks ======= .. module:: celery.task.base A task is a class that encapsulates a function and its execution options. With a function ``create_user``, that takes two arguments: ``username`` and ``password``, you can create a task like this: .. code-block:: python from celery.task import Task class CreateUserTask(Task): def run(self, username, password): create_user(username, password) For convenience there is a shortcut decorator that turns any function into a task, ``celery.decorators.task``: .. code-block:: python from celery.decorators import task from django.contrib.auth import User @task def create_user(username, password): User.objects.create(username=username, password=password) The task decorator takes the same execution options the ``Task`` class does: .. code-block:: python @task(serializer="json") def create_user(username, password): User.objects.create(username=username, password=password) An alternative way to use the decorator is to give the function as an argument instead, but if you do this be sure to set the resulting tasks ``__name__`` attribute, so pickle is able to find it in reverse: .. code-block:: python create_user_task = task()(create_user) create_user_task.__name__ = "create_user_task" Default keyword arguments ========================= Celery supports a set of default arguments that can be forwarded to any task. task can choose not to take these, or only list the ones it want (the worker will do the right thing). The current default keyword arguments are: * logfile The currently used log file, can be passed on to ``self.get_logger`` to gain access to the workers log file. See `Logging`_. * loglevel The current loglevel used. * task_id The unique id of the executing task. * task_name Name of the executing task. * task_retries How many times the current task has been retried. (an integer starting a ``0``). Logging ======= You can use the workers logger to add some diagnostic output to the worker log: .. code-block:: python class AddTask(Task): def run(self, x, y, **kwargs): logger = self.get_logger(**kwargs) logger.info("Adding %s + %s" % (x, y)) return x + y or using the decorator syntax: .. code-block:: python @task() def add(x, y, **kwargs): logger = add.get_logger(**kwargs) logger.info("Adding %s + %s" % (x, y)) return x + y There are several logging levels available, and the workers ``loglevel`` setting decides whether they will be sent to the log file or not. Retrying a task if something fails ================================== Simply use :meth:`Task.retry` to re-sent the task, it will do the right thing, and respect the :attr:`Task.max_retries` attribute: .. code-block:: python @task() def send_twitter_status(oauth, tweet, **kwargs): try: twitter = Twitter(oauth) twitter.update_status(tweet) except (Twitter.FailWhaleError, Twitter.LoginError), exc: send_twitter_status.retry(args=[oauth, tweet], kwargs, exc=exc) Here we used the ``exc`` argument to pass the current exception to :meth:`Task.retry`. At each step of the retry this exception is available as the tombstone (result) of the task, when :attr:`Task.max_retries` has been exceeded this is the exception raised. However, if an ``exc`` argument is not provided the :exc:`RetryTaskError` exception is raised instead. Using a custom retry delay -------------------------- The default countdown is in the tasks :attr:`Task.default_retry_delay` attribute, which by default is set to 3 minutes. You can also provide the ``countdown`` argument to :meth:`Task.retry` to override this default. .. code-block:: python class MyTask(Task): default_retry_delay = 30 * 60 # retry in 30 minutes def run(self, x, y, **kwargs): try: ... except Exception, exc: self.retry([x, y], kwargs, exc=exc, countdown=60) # override the default and # - retry in 1 minute Task options ============ * name This is the name the task is registered as. You can set this name manually, or just use the default which is atomatically generated using the module and class name. * abstract Abstract classes are not registered, so they're only used for making new task types by subclassing. * max_retries The maximum number of attempted retries before giving up. If this is exceeded the :exc`celery.execptions.MaxRetriesExceeded` exception will be raised. Note that you have to retry manually, it's not something that happens automatically. * default_retry_delay Default time in seconds before a retry of the task should be executed. Default is a 1 minute delay. * rate_limit Set the rate limit for this task type, if this is ``None`` no rate limit is in effect. The rate limits can be specified in seconds, minutes or hours by appending ``"/s"``, ``"/m"`` or "``/h"``". If this is an integer it is interpreted as seconds. Example: ``"100/m" (hundred tasks a minute). Default is the ``CELERY_DEFAULT_RATE_LIMIT`` setting (which is off if not specified). * ignore_result Don't store the status and return value. This means you can't use the :class:`celery.result.AsyncResult` to check if the task is done, or get its return value. Only use if you need the performance and is able live without these features. Any exceptions raised will store the return value/status as usual. * disable_error_emails Disable all error e-mails for this task. * serializer A string identifying the default serialization method to use. Defaults to the ``CELERY_TASK_SERIALIZER`` setting. Can be ``pickle`` ``json``, ``yaml``, or any custom serialization methods that have been registered with :mod:`carrot.serialization.registry`. Please see :doc:`executing` for more information. Message and routing options --------------------------- * routing_key Override the global default ``routing_key`` for this task. * exchange Override the global default ``exchange`` for this task. * mandatory If set, the task message has mandatory routing. By default the task is silently dropped by the broker if it can't be routed to a queue. However - If the task is mandatory, an exception will be raised instead. * immediate Request immediate delivery. If the task cannot be routed to a task worker immediately, an exception will be raised. This is instead of the default behaviour, where the broker will accept and queue the task, but with no guarantee that the task will ever be executed. * priority The message priority. A number from ``0`` to ``9``, where ``0`` is the highest. Note that RabbitMQ doesn't support priorities yet. Please see :doc:`executing` for descriptions of these options. How it works ============ Here comes the technical details, this part isn't something you need to know, but you may be interested, so here goes. All defined tasks are listed in a registry. The registry contains a list of task names and their task classes. You can investigate this registry by yourself: .. code-block:: python >>> from celery.task import registry >>> from celery import task >>> registry.tasks {'celery.delete_expired_task_meta': , 'celery.execute_remote': , 'celery.task.rest.RESTProxyTask': , 'celery.task.rest.Task': , 'celery.map_async': , 'celery.ping': } This is the list of tasks built-in to celery. Note that we had to import ``celery.task`` first for these to show up. This is because the tasks will only be registered when the module it is defined in is imported. When using the default loader the loader imports any modules listed in the ``CELERY_IMPORTS`` setting. If using Django it loads all ``tasks.py`` modules for the applications listed in ``INSTALLED_APPS``. If you want to do something special you can create your own loader to do what you want. The entity responsible for registering your task in the registry is a metaclass, :class:`TaskType`, this is the default metaclass for ``Task``. If you want to register your task manually you can set the ``abstract`` attribute: .. code-block:: python class MyTask(Task): abstract = True This way the task won't be registered, but any task subclassing it will. So when we send a task, we don't send the function code, we just send the name of the task, so when the worker receives the message it can just look it up in the task registry to find the execution code. This means that your workers must optimally be updated with the same software as the client, this is a drawback, but the alternative is a technical challenge that has yet to be solved. Performance and Strategies ========================== Granularity ----------- The tasks granularity is the degree of parallelization your task have. It's better to have a lot of small tasks, than just a few long running ones. With smaller tasks, you can process more tasks in parallel and the tasks won't run long enough to block the worker from processing other waiting tasks. But there's a limit, sending messages takes processing power and bandwidth. If your tasks are so short the overhead of passing them around is worse than just executing them inline, you should reconsider your strategy. There is no universal answer here. Data locality ------------- The worker processing the task should optimally be as close to the data as possible. The best would be to have a copy in memory, the worst being a full transfer from another continent. If the data is far away, you could try to run another worker at location, or if that's not possible, cache often used data, or preload data you know is going to be used. The easiest way to share data between workers is to use a distributed caching system, like `memcached`_. For more information about data-locality, please read http://research.microsoft.com/pubs/70001/tr-2003-24.pdf .. _`memcached`: http://memcached.org/ State ----- Since celery is a distributed system, you can't know in which process, or even on what machine the task will run, also you can't even know if the task will run in a timely manner, so please be wary of the state you pass on to tasks. One gotcha is Django model objects, they shouldn't be passed on as arguments to task classes, it's almost always better to refetch the object from the database instead, as there are possible race conditions involved. Imagine the following scenario where you have an article, and a task that automatically expands some abbreviations in it. .. code-block:: python class Article(models.Model): title = models.CharField() body = models.TextField() @task def expand_abbreviations(article): article.body.replace("MyCorp", "My Corporation") article.save() First, an author creates an article and saves it, then the author clicks on a button that initiates the abbreviation task. >>> article = Article.objects.get(id=102) >>> expand_abbreviations.delay(model_object) Now, the queue is very busy, so the task won't be run for another 2 minutes, in the meantime another author makes some changes to the article, when the task is finally run, the body of the article is reverted to the old version, because the task had the old body in its argument. Fixing the race condition is easy, just use the article id instead, and refetch the article in the task body: .. code-block:: python @task def expand_abbreviations(article_id) article = Article.objects.get(id=article_id) article.body.replace("MyCorp", "My Corporation") article.save() >>> expand_abbreviations(article_id) There might even be performance benefits to this approach, as sending large messages may be expensive.