your logs. This should be fairly easy to setup using syslog (see also syslog-ng [http://en.wikipedia.org/wiki/Syslog-ng] and rsyslog [http://www.rsyslog.com/].). Celery uses the logging [http://docs “pictures” of the clusters state at regular intervals. This can then be stored in a database to generate statistics with, or even monitoring over longer time periods. django-celery now comes with a Celery command renamed to show. celeryd-multi start will now actually start and detach worker nodes. To just generate the commands you have to use celeryd-multi show. celeryd: Added –pidfile argument. The worker

Celery is not able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the use that configuration, but if it’s not defined in the list of queues Celery will automatically generate a new queue for you (depending on the CELERY_CREATE_MISSING_QUEUES option). You can also tell the your logs. This should be fairly easy to setup using syslog (see also syslog-ng [http://en.wikipedia.org/wiki/Syslog-ng] and rsyslog [http://www.rsyslog.com/].). Celery uses the logging [https://docs

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name print(body) r = hello.apply_async() print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}

Celery isn’t able to detect what module the function belongs to, it uses the main module name to generate the beginning of the task name. This is only a problem in a limited set of use cases: 1. If the Names Every task must have a unique name. If no explicit name is provided the task decorator will generate one for you, and this name will be based on 1) the module the task is defined in, and 2) the name = hello.apply_async(args=(a, b)) print(r.get(on_message=on_raw_message, propagate=False)) Will generate output like this: {'task_id': '5660d3a3-92b8-40df-8ccc-33a5d1d680d7', 'result': {'progress': 50}