now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. The primary disadvantage to JSON is that it limits you to the following types (in- cluding dates, recursive references, etc.) However, the Python libraries for YAML are a good bit slower than the libraries for JSON. If you need a more expressive set of data types and need to :json => @status end You can easily port this scheme to any language/framework; new examples and libraries are very welcome. 2.7.4 Executing webhook tasks To execute the task you use the URL class: >>>

now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. The primary disadvantage to JSON is that it limits you to the following types (including dates, recursive references, etc.) However, the Python libraries for YAML are a good bit slower than the libraries for JSON. If you need a more expressive set of data types and need to :json => @status end You can easily port this scheme to any language/framework; new examples and libraries are very welcome. Executing webhook tasks To execute the task you use the URL class: >>> from

now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. 30 Chapter 2. User Guide Celery Documentation, Release 2.1.4 The types (in- cluding dates, recursive references, etc.) However, the Python libraries for YAML are a good bit slower than the libraries for JSON. If you need a more expressive set of data types and need to :json => @status end You can easily port this scheme to any language/framework; new examples and libraries are very welcome. 2.7.4 Executing webhook tasks To execute the task you use the URL class: >>>

descriptors, that are simply out of your control. Read more... . • User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up routing options, as it is much better to keep this in a centralized location, and especially for libraries it makes it possible for users to control how they want your tasks to behave, you can also imagine now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. The primary disadvantage to JSON is that it limits you to the following

file descriptors, that are simply out of your control. Read more…. User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up routing options, as it is much better to keep this in a centralized location, and especially for libraries it makes it possible for users to control how they want your tasks to behave, you can also imagine now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. The primary disadvantage to JSON is that it limits you to the following

now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries such as cjson or simplejson. The primary disadvantage to JSON is that it limits you to the following types (including dates, recursive references, etc.) However, the Python libraries for YAML are a good bit slower than the libraries for JSON. If you need a more expressive set of data types and need to :json => @status end You can easily port this scheme to any language/framework; new examples and libraries are very welcome. Executing webhook tasks To execute the task you use the URL class: >>> from

file descriptors, that are simply out of your control. Read more…. User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up discouraged. It is much better to keep these in a centralized location. This is especially true for libraries, as it enables users to control how their tasks behave. A centralized configuration will also allow now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries, such as simplejson [https://pypi.python.org/pypi/simplejson/]. The primary disadvantage to JSON

file descriptors, that are simply out of your control. Read more…. User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up discouraged. It is much better to keep these in a centralized location. This is especially true for libraries, as it enables users to control how their tasks behave. A centralized configuration will also allow now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries, such as simplejson [https://pypi.python.org/pypi/simplejson/]. The primary disadvantage to JSON

descriptors, that are simply out of your control. Read more... . • User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up discouraged. It is much better to keep these in a centralized location. This is especially true for libraries, as it enables users to control how their tasks behave. A centralized configuration will also allow now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries, such as simplejson. The primary disadvantage to JSON is that it limits you to the following data

descriptors, that are simply out of your control. Read more... . • User Components Each worker component can be customized, and additional components can be defined by the user. The worker is built up location. This is especially true 3.2. Getting Started 25 Celery Documentation, Release 4.3.0 for libraries, as it enables users to control how their tasks behave. A centralized configuration will also allow now a standard part of Python (since 2.6), and is fairly fast to decode using the modern Python libraries, such as simplejson. The primary disadvantage to JSON is that it limits you to the following data