blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), max_length=64) processing the task should 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 incre- ment its count by. clickmuncher/models.py: from django.db import models from django.utils.translation import ugettext_lazy as _ class ClickManager(models.Manager): def increment_clicks(self, for_url

blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), processing the task should 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 increment its count by. clickmuncher/models.py: from django.db import models from django.utils.translation import ugettext_lazy as _ class ClickManager(models.Manager): def increment_clicks(self, for_url

blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), max_length=64) processing the task should 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 of tasks a worker can execute before it’s replaced by a new process. This is useful if you have memory leaks you have no control over for example from closed source C extensions. The option can be set

- celery.messaging Contrib: Abortable tasks - celery.contrib.abortable Events - celery.events In-memory Representation of Cluster State - celery.events.state Celery Worker Daemon - celery.bin.celeryd Celery blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), processing the task should 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

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 9.22 In-memory Representation of Cluster State - celery.events.state . . . . . . . . . . . . . . . . . . . . 156 processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), max_length=64)

processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), max_length=64) sending a broadcast message to all the workers, the workers then keep a list of revoked tasks in memory. If you want tasks to remain revoked after worker restart you need to specify a file for these to

processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), max_length=64) sending a broadcast message to all the workers, the workers then keep a list of revoked tasks in memory. If you want tasks to remain revoked after worker restart you need to specify a file for these to

- celery.messaging Contrib: Abortable tasks - celery.contrib.abortable Events - celery.events In-memory Representation of Cluster State - celery.events.state App: Worker Node - celery.apps.worker App: processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"),

processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ class Comment(models.Model): name = models.CharField(_("name"), sending a broadcast message to all the workers, the workers then keep a list of revoked tasks in memory. If you want tasks to remain revoked after worker restart you need to specify a file for these to

processing the task should be as close to the data as possible. The best would be to have a copy in memory, the worst would be a full transfer from another continent. If the data is far away, you could try blog/models.py The comment model looks like this: from django.db import models from django.utils.translation import ugettext_lazy as _ 2.2. Tasks 35 Celery Documentation, Release 2.5.5 class Comment(models sending a broadcast message to all the workers, the workers then keep a list of revoked tasks in memory. If you want tasks to remain revoked after worker restart you need to specify a file for these to