This tool is an example module written in Python for the Akamai CLI Tool although it can be executed individually. A new Cloudlet policy version is created on each change.
Installation is done via akamai install
:
$ akamai install audience-segmentation
Running this will run the system python setup.py
automatically.
To update to the latest version:
$ akamai update audience-segmentation
usage: akamai audience-segmentation update --policy POLICY --rule RULE
--weights WEIGHTS
[--activate ACTIVATE]
[--edgerc EDGERC]
[--section SECTION] [--verbose]
required arguments:
--policy POLICY Policy name
--rule RULE Rule name. Example: 'Test Rule #1'
--weights WEIGHTS New percentages. Example: '1,50'
optional arguments:
--activate ACTIVATE Activate the policy to staging|production
--edgerc EDGERC Config file [default: ~/.edgerc]
--section SECTION Config section in .edgerc
--verbose Enable an interactive verbose mode
The --verbose mode goes through every step for every API call and showing the user the generated JSON requests and responses.
Defaults are:
--edgerc: ~/.edgerc --section: papi --verbose: OFF --activate: OFF
$ akamai audience-segmentation --policy <policy_name> --rule <'rule_name'> --weights <'start_weight_value,end_weight_value'> --activate <staging|production>
$ akamai as --edgerc <~/other_location/.edgerc> --section <other_section> --policy <policy_name> --rule <'rule_name'> --weights <'start_weight_value,end_weight_value'> --activate <staging|production>