CMS itself. The two applications are completely independent. They cannot make use of each other’s data or functionality. Let’s create the new Polls/CMS Integration application where we will bring them as _ class PollPluginPublisher(CMSPluginBase): model = PollPluginModel # model where plugin data are saved module = _("Polls") name = _("Poll Plugin") # name of the plugin in the interface _("Create Alias"), reverse("admin:cms_create_alias"), data={'plugin_id': plugin.pk, 'csrfmiddlewaretoken': get_token(request)}, ) ]

CMS itself. The two applications are completely independent. They cannot make use of each other’s data or functionality. Let’s create the new Polls/CMS Integration application where we will bring them plugin class PollPluginPublisher(CMSPluginBase): model = PollPluginModel # model where plugin data are saved module = _("Polls") name = _("Poll Plugin") # name of the plugin in the interface _("Create Alias"), reverse("admin:cms_create_alias"), data={'plugin_id': plugin.pk, 'csrfmiddlewaretoken': get_token(request)}, ) ]

CMS itself. The two applications are completely independent. They cannot make use of each other’s data or functionality. Let’s create the new Polls/CMS Integration application where we will bring them plugin class PollPluginPublisher(CMSPluginBase): model = PollPluginModel # model where plugin data are saved module = _("Polls") name = _("Poll Plugin") # name of the plugin in the interface _("Create Alias"), reverse("admin:cms_create_alias"), data={'plugin_id': plugin.pk, 'csrfmiddlewaretoken': get_token(request)}, ) ]

if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth has a whole section dedicated to the topic of task granularity. Data locality The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst

[http://redis.io/] is also feature-complete, but power failures or abrupt termination may result in data loss. Using SQLAlchemy Using the Django Database Using a database as a message queue is not recommended if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

SQLite for local development. Celery can run on a single machine, on multiple machines, or even across data centers. Get Started If this is the first time you’re trying to use Celery, or you are new to Celery com/download.html Redis Redis [http://redis.io/] is also feature-complete, but is more susceptible to data loss in the event of abrupt termination or power failures. Detailed information about using Redis: for different workers too, e.g: $ celery multi start 10 -A proj -l info -Q:1-3 images,video -Q:4,5 data \ -Q default -L:4,5 debug For more examples see the multi module in the API reference. About

[http://redis.io/] is also feature-complete, but power failures or abrupt termination may result in data loss. Using SQLAlchemy Using the Django Database Using a database as a message queue is not recommended if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth

if they’re not used Avoid launching synchronous subtasks Performance and Strategies Granularity Data locality State Database transactions Example blog/models.py blog/views.py blog/tasks.py This guide processing other waiting tasks. However, executing a task does have overhead. A message needs to be sent, data may not be local, etc. So if the tasks are too fine-grained the additional overhead may not be worth has a whole section dedicated to the topic of task granularity. Data locality The worker processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst

SQLite for local development. Celery can run on a single machine, on multiple machines, or even across data centers. Get Started If this is the first time you’re trying to use Celery, or if you haven’t kept com/download.html Redis Redis [https://redis.io/] is also feature-complete, but is more susceptible to data loss in the event of abrupt termination or power failures. Detailed information about using Redis: different workers too, for example: $ celery multi start 10 -A proj -l info -Q:1-3 images,video -Q:4,5 data \ -Q default -L:4,5 debug For more examples see the multi module in the API reference. About

SQLite for local development. Celery can run on a single machine, on multiple machines, or even across data centers. Get Started If this is the first time you’re trying to use Celery, or if you haven’t kept com/download.html Redis Redis [http://redis.io/] is also feature-complete, but is more susceptible to data loss in the event of abrupt termination or power failures. Detailed information about using Redis: different workers too, for example: $ celery multi start 10 -A proj -l info -Q:1-3 images,video -Q:4,5 data \ -Q default -L:4,5 debug For more examples see the multi module in the API reference. About