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Introduction
Ansible is a simple IT automation engine that is used to automate cloud provisioning, configuration management, application deployment, and many other IT requirements. It has the capability for multi-tier deployment. It models the user's IT infrastructure by describing how all of the systems inter-relate, rather than just managing one system at a time. It is very simple to deploy as it uses no agents and requires no additional custom security infrastructure.
AnsibleModule
Include "from ansible.module_utils.basic import AnsibleModule" in your module in order to use this functionality.
It's a common code generally used for quickly building an ansible module in python, though the user can write modules with anything till it returns a JSON.
add_path_info(kwargs)
In case the results are files, then supplement the information about the file in the return path along with the stats about the file path.
atomic_move(src, dest, unsafce_writes=False)
This atomically moves the src(source) to the dest(destination), copying attributes from the dest, it returns true on success and uses the os.rename to ensure that this is an atomic operation, besides this, the rest of the function is work around limitations, corner cases and ensure selinux context is saved if possible.
backup_local(fn)
This makes a data-marked backup of the file which is specified, and depending on the success or failure, it returns true or false.
boolean(arg)
It converts the argument to a boolean
exit_json(**kwargs)
It returns from the module without any error
is_executable(path)
Whether the given path is executable or not?
Parameters
path:It defines the path of the file which is to be checked.
Limitations
FSACLs are not accounted for by this.
Most of the time, we want to know whether the current user can execute this file or not. It cannot tell us this, it can only tell if any execute bit is set.
is_special_selinux_path(path)
It returns a tuple which contains(True, selinux_context) if in case the given path is on an NFS else it will return(False, None).
Basic
We need to include "import ansible.module_utils.basic" in the module if we wish to use this functionality.
It's an argument spec validation class. It is used to create argument_spec based validator that is used to validate several parameters using the validate() method.
Parameters
argument_spec (dict[str, dict]): it specifies valid parameters as well as their types, it can include nested argument specs too.
mutually_exclusive(list{str} or list[list[str]]): it specifies a list of lists of terms that can’t be provided together.
required_one_of(list[list[str]]): it specifies a list of lists of terms, out of which one in each list is required.
validate(parameters, *args, **kwargs)
It is used to validate parameters over an argument spec.
There may be no_log values in the error messages in the ValidationResult, and therefore it should be sanitized before logging or displaying with the sanitize_keys(). It returns a ValidationResult that contains validated parameters.
Parameters
parameters(dic[str,dict]): it contains parameters to validate over the argument spec.
It is the result of argument spec validation. It is returned by ArgumentSpecValidator.validate(). It is an object which contains validated parameters and errors, if any.
errors
The AnsibleValidationErrorMultiple contains all the AnsibleValidationError objects if any failure occurs during validation.
property unsupported_parameters
It is a set that contains the names of unsupported parameters.
property error_messages
It is a list that contains all the error messages from every exception in errors.
Validation
These are the functions used for validating various parameter types. They are standalone functions.
It is used for checking the required parameters when it's not possible to check via argspec because more information is required from what is given in the argspec.
If in case any required parameters are missing, then it raises TypeError. It either returns an empty list or raises TypeError in case the check fails.
Parameters
parameters: it is a dictionary of all the parameters.
required_parameters: it is a list of all the parameters which are to be looked for in the given parameters.
It is used for checking mutually exclusive terms over argument parameters.
It accepts either a single list or a list of lists that are a group of terms that need to be mutually exclusive. It either returns an empty list or raises TypeError in case the check fails.
Parameters
terms: it is a list of all the mutually exclusive parameters
parameters: it is a dictionary of the parameters
options_context: it is a list of strings of parent key names if in case the terms are in sub-spec.
For every key in the requirements, it checks the corresponding list to see if it exists in the parameters or not. It either accepts a single string or a list of values for each key. It either returns an empty dictionary or raises TypeEror.
Parameters
requirements: it is a dictionary of all the requirements.
parameters: it is a dictionary of the parameters
options_context: it is a list of strings of parent key names if in case the requirements are in sub-spec.
It is used to convert the value to float and return it. If it is unable to convert the value, it will raise a TypeError. It returns a float of the given value.
Parameters
value: float, int, str, or bytes to either verify or to convert and return.
It checks whether the given value is an integer and returns it if it is, or it will convert the value to an integer and then return it. If it is unable to convert the value, then it will raise a TypeError. It returns an int of the given value.
Parameters
value: it accepts a string or int to either convert or verify.
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