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 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

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 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

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: >>>

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

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 (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

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 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

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 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

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 (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

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 (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

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 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