Ansible module architecture¶

This in-depth dive helps you understand Ansible’s program flow to execute modules. It is written for people working on the portions of the Core Ansible Engine that execute a module. Those writing Ansible Modules may also find this in-depth dive to be of interest, but individuals simply using Ansible Modules will not likely find this to be helpful.

Topics

Types of modules ¶

Ansible supports several different types of modules in its code base. Some of these are for backwards compatibility and others are to enable flexibility.

Action plugins ¶

Action Plugins look like modules to end users who are writing playbooks but they’re distinct entities for the purposes of this document. Action Plugins always execute on the controller and are sometimes able to do all work there (for instance, the debug Action Plugin which prints some text for the user to see or the assert Action Plugin which can test whether several values in a playbook satisfy certain criteria.)

More often, Action Plugins set up some values on the controller, then invoke an actual module on the managed node that does something with these values. An easy to understand version of this is the template Action Plugin. The template Action Plugin takes values from the user to construct a file in a temporary location on the controller using variables from the playbook environment. It then transfers the temporary file to a temporary file on the remote system. After that, it invokes the copy module which operates on the remote system to move the file into its final location, sets file permissions, and so on.

New-style modules ¶

All of the modules that ship with Ansible fall into this category.

New-style modules have the arguments to the module embedded inside of them in some manner. Non-new-style modules must copy a separate file over to the managed node, which is less efficient as it requires two over-the-wire connections instead of only one.

Python ¶

New-style Python modules use the Ansiballz framework framework for constructing modules. All official modules (shipped with Ansible) use either this or the powershell module framework.

These modules use imports from ansible.module_utils in order to pull in boilerplate module code, such as argument parsing, formatting of return values as JSON, and various file operations.

Note

In Ansible, up to version 2.0.x, the official Python modules used the Module Replacer framework framework. For module authors, Ansiballz framework is largely a superset of Module Replacer framework functionality, so you usually do not need to know about one versus the other.

Powershell ¶

New-style powershell modules use the Module Replacer framework framework for constructing modules. These modules get a library of powershell code embedded in them before being sent to the managed node.

JSONARGS ¶

Scripts can arrange for an argument string to be placed within them by placing the string <<INCLUDE_ANSIBLE_MODULE_JSON_ARGS>> somewhere inside of the file. The module typically sets a variable to that value like this:

json_arguments = """<<INCLUDE_ANSIBLE_MODULE_JSON_ARGS>>"""

Which is expanded as:

json_arguments = """{"param1": "test's quotes", "param2": "\"To be or not to be\" - Hamlet"}"""

Note

Ansible outputs a JSON string with bare quotes. Double quotes are used to quote string values, double quotes inside of string values are backslash escaped, and single quotes may appear unescaped inside of a string value. To use JSONARGS, your scripting language must have a way to handle this type of string. The example uses Python’s triple quoted strings to do this. Other scripting languages may have a similar quote character that won’t be confused by any quotes in the JSON or it may allow you to define your own start-of-quote and end-of-quote characters. If the language doesn’t give you any of these then you’ll need to write a non-native JSON module or Old-style module instead.

The module typically parses the contents of json_arguments using a JSON library and then use them as native variables throughout the rest of its code.

Non-native want JSON modules ¶

If a module has the string WANT_JSON in it anywhere, Ansible treats it as a non-native module that accepts a filename as its only command line parameter. The filename is for a temporary file containing a JSON string containing the module’s parameters. The module needs to open the file, read and parse the parameters, operate on the data, and print its return data as a JSON encoded dictionary to stdout before exiting.

These types of modules are self-contained entities. As of Ansible 2.1, Ansible only modifies them to change a shebang line if present.

Binary modules ¶

From Ansible 2.2 onwards, modules may also be small binary programs. Ansible doesn’t perform any magic to make these portable to different systems so they may be specific to the system on which they were compiled or require other binary runtime dependencies. Despite these drawbacks, a site may sometimes have no choice but to compile a custom module against a specific binary library if that’s the only way they have to get access to certain resources.

Binary modules take their arguments and will return data to Ansible in the same way as want JSON modules.

Old-style modules ¶

Old-style modules are similar to want JSON modules, except that the file that they take contains key=value pairs for their parameters instead of JSON.

Ansible decides that a module is old-style when it doesn’t have any of the markers that would show that it is one of the other types.

How modules are executed ¶

When a user uses ansible or ansible-playbook, they specify a task to execute. The task is usually the name of a module along with several parameters to be passed to the module. Ansible takes these values and processes them in various ways before they are finally executed on the remote machine.

Executor/task_executor ¶

The TaskExecutor receives the module name and parameters that were parsed from the playbook (or from the command line in the case of /usr/bin/ansible). It uses the name to decide whether it’s looking at a module or an Action Plugin. If it’s a module, it loads the Normal Action Plugin and passes the name, variables, and other information about the task and play to that Action Plugin for further processing.

Normal action plugin ¶

The normal action plugin executes the module on the remote host. It is the primary coordinator of much of the work to actually execute the module on the managed machine.

It takes care of creating a connection to the managed machine by instantiating a Connection class according to the inventory configuration for that host.
It adds any internal Ansible variables to the module’s parameters (for instance, the ones that pass along no_log to the module).
It takes care of creating any temporary files on the remote machine and cleans up afterwards.
It does the actual work of pushing the module and module parameters to the remote host, although the module_common code described in the next section does the work of deciding which format those will take.
It handles any special cases regarding modules (for instance, various complications around Windows modules that must have the same names as Python modules, so that internal calling of modules from other Action Plugins work.)

Much of this functionality comes from the BaseAction class, which lives in plugins/action/__init__.py. It makes use of Connection and Shell objects to do its work.

Note

When tasks are run with the async: parameter, Ansible uses the async Action Plugin instead of the normal Action Plugin to invoke it. That program flow is currently not documented. Read the source for information on how that works.

Executor/module_common.py ¶

Code in executor/module_common.py takes care of assembling the module to be shipped to the managed node. The module is first read in, then examined to determine its type. PowerShell and JSON-args modules are passed through Module Replacer. New-style Python modules are assembled by Ansiballz framework. Non-native-want-JSON, Binary modules, and Old-Style modules aren’t touched by either of these and pass through unchanged. After the assembling step, one final modification is made to all modules that have a shebang line. Ansible checks whether the interpreter in the shebang line has a specific path configured via an ansible_$X_interpreter inventory variable. If it does, Ansible substitutes that path for the interpreter path given in the module. After this, Ansible returns the complete module data and the module type to the Normal Action which continues execution of the module.

Next we’ll go into some details of the two assembler frameworks.

Module Replacer framework ¶

The Module Replacer framework is the original framework implementing new-style modules. It is essentially a preprocessor (like the C Preprocessor for those familiar with that programming language). It does straight substitutions of specific substring patterns in the module file. There are two types of substitutions:

Replacements that only happen in the module file. These are public replacement strings that modules can utilize to get helpful boilerplate or access to arguments.
- from ansible.module_utils.MOD_LIB_NAME import * is replaced with the contents of the ansible/module_utils/MOD_LIB_NAME.py These should only be used with new-style Python modules.
- #<<INCLUDE_ANSIBLE_MODULE_COMMON>> is equivalent to from ansible.module_utils.basic import * and should also only apply to new-style Python modules.
- # POWERSHELL_COMMON substitutes the contents of ansible/module_utils/powershell.ps1. It should only be used with new-style Powershell modules.
Replacements that are used by ansible.module_utils code. These are internal replacement patterns. They may be used internally, in the above public replacements, but shouldn’t be used directly by modules.
- "<<ANSIBLE_VERSION>>" is substituted with the Ansible version. In new-style Python modules under the Ansiballz framework framework the proper way is to instead instantiate an AnsibleModule and then access the version from :attr:AnsibleModule.ansible_version.
- "<<INCLUDE_ANSIBLE_MODULE_COMPLEX_ARGS>>" is substituted with a string which is the Python repr of the JSON encoded module parameters. Using repr on the JSON string makes it safe to embed in a Python file. In new-style Python modules under the Ansiballz framework this is better accessed by instantiating an AnsibleModule and then using AnsibleModule.params.
- <<SELINUX_SPECIAL_FILESYSTEMS>> substitutes a string which is a comma separated list of file systems which have a file system dependent security context in SELinux. In new-style Python modules, if you really need this you should instantiate an AnsibleModule and then use AnsibleModule._selinux_special_fs. The variable has also changed from a comma separated string of file system names to an actual python list of filesystem names.
- <<INCLUDE_ANSIBLE_MODULE_JSON_ARGS>> substitutes the module parameters as a JSON string. Care must be taken to properly quote the string as JSON data may contain quotes. This pattern is not substituted in new-style Python modules as they can get the module parameters another way.
- The string syslog.LOG_USER is replaced wherever it occurs with the syslog_facility which was named in ansible.cfg or any ansible_syslog_facility inventory variable that applies to this host. In new-style Python modules this has changed slightly. If you really need to access it, you should instantiate an AnsibleModule and then use AnsibleModule._syslog_facility to access it. It is no longer the actual syslog facility and is now the name of the syslog facility. See the documentation on internal arguments for details.

Ansiballz framework ¶

Ansible 2.1 switched from the Module Replacer framework framework to the Ansiballz framework for assembling modules. The Ansiballz framework differs from module replacer in that it uses real Python imports of things in ansible/module_utils instead of merely preprocessing the module. It does this by constructing a zipfile – which includes the module file, files in ansible/module_utils that are imported by the module, and some boilerplate to pass in the module’s parameters. The zipfile is then Base64 encoded and wrapped in a small Python script which decodes the Base64 encoding and places the zipfile into a temp directory on the managed node. It then extracts just the ansible module script from the zip file and places that in the temporary directory as well. Then it sets the PYTHONPATH to find python modules inside of the zip file and invokes python on the extracted ansible module.

Note

Ansible wraps the zipfile in the Python script for two reasons:

for compatibility with Python 2.6 which has a less functional version of Python’s -m command line switch.
so that pipelining will function properly. Pipelining needs to pipe the Python module into the Python interpreter on the remote node. Python understands scripts on stdin but does not understand zip files.

In Ansiballz, any imports of Python modules from the ansible.module_utils package trigger inclusion of that Python file into the zipfile. Instances of #<<INCLUDE_ANSIBLE_MODULE_COMMON>> in the module are turned into from ansible.module_utils.basic import * and ansible/module-utils/basic.py is then included in the zipfile. Files that are included from module_utils are themselves scanned for imports of other Python modules from module_utils to be included in the zipfile as well.

Warning

At present, the Ansiballz Framework cannot determine whether an import should be included if it is a relative import. Always use an absolute import that has ansible.module_utils in it to allow Ansiballz to determine that the file should be included.

Passing args ¶

In Module Replacer framework, module arguments are turned into a JSON-ified string and substituted into the combined module file. In Ansiballz framework, the JSON-ified string is passed into the module via stdin. When a ansible.module_utils.basic.AnsibleModule is instantiated, it parses this string and places the args into AnsibleModule.params where it can be accessed by the module’s other code.

Note

Internally, the AnsibleModule uses the helper function, ansible.module_utils.basic._load_params(), to load the parameters from stdin and save them into an internal global variable. Very dynamic custom modules which need to parse the parameters prior to instantiating an AnsibleModule may use _load_params to retrieve the parameters. Be aware that _load_params is an internal function and may change in breaking ways if necessary to support changes in the code. However, we’ll do our best not to break it gratuitously, which is not something that can be said for either the way parameters are passed or the internal global variable.

Internal arguments ¶

Both Module Replacer framework and Ansiballz framework send additional arguments to the module beyond those which the user specified in the playbook. These additional arguments are internal parameters that help implement global Ansible features. Modules often do not need to know about these explicitly as the features are implemented in ansible.module_utils.basic but certain features need support from the module so it’s good to know about them.

_ansible_no_log ¶

This is a boolean. If it’s True then the playbook specified no_log (in a task’s parameters or as a play parameter). This automatically affects calls to AnsibleModule.log(). If a module implements its own logging then it needs to check this value. The best way to look at this is for the module to instantiate an AnsibleModule and then check the value of AnsibleModule.no_log.

Note

no_log specified in a module’s argument_spec are handled by a different mechanism.

_ansible_debug ¶

This is a boolean that turns on more verbose logging. If a module uses AnsibleModule.debug() rather than AnsibleModule.log() then the messages are only logged if this is True. This also turns on logging of external commands that the module executes. This can be changed via the debug setting in ansible.cfg or the environment variable ANSIBLE_DEBUG. If, for some reason, a module must access this, it should do so by instantiating an AnsibleModule and accessing AnsibleModule._debug.

_ansible_diff ¶

This boolean is turned on via the --diff command line option. If a module supports it, it will tell the module to show a unified diff of changes to be made to templated files. The proper way for a module to access this is by instantiating an AnsibleModule and accessing AnsibleModule._diff.

_ansible_verbosity ¶

This value could be used for finer grained control over logging. However, it is currently unused.

_ansible_selinux_special_fs ¶

This is a list of names of filesystems which should have a special selinux context. They are used by the AnsibleModule methods which operate on files (changing attributes, moving, and copying). The list of names is set via a comma separated string of filesystem names from ansible.cfg:

# ansible.cfg
[selinux]
special_context_filesystems=nfs,vboxsf,fuse,ramfs

If a module cannot use the builtin AnsibleModule methods to manipulate files and needs to know about these special context filesystems, it should instantiate an AnsibleModule and then examine the list in AnsibleModule._selinux_special_fs.

This replaces ansible.module_utils.basic.SELINUX_SPECIAL_FS from Module Replacer framework. In module replacer it was a comma separated string of filesystem names. Under Ansiballz it’s an actual list.

New in version 2.1.

_ansible_syslog_facility ¶

This parameter controls which syslog facility ansible module logs to. It may be set by changing the syslog_facility value in ansible.cfg. Most modules should just use AnsibleModule.log() which will then make use of this. If a module has to use this on its own, it should instantiate an AnsibleModule and then retrieve the name of the syslog facility from AnsibleModule._syslog_facility. The code will look slightly different than it did under Module Replacer framework due to how hacky the old way was

# Old way
import syslog
syslog.openlog(NAME, 0, syslog.LOG_USER)

# New way
import syslog
facility_name = module._syslog_facility
facility = getattr(syslog, facility_name, syslog.LOG_USER)
syslog.openlog(NAME, 0, facility)

New in version 2.1.

_ansible_version ¶

This parameter passes the version of ansible that runs the module. To access it, a module should instantiate an AnsibleModule and then retrieve it from AnsibleModule.ansible_version. This replaces ansible.module_utils.basic.ANSIBLE_VERSION from Module Replacer framework.

New in version 2.1.

Special considerations ¶

Pipelining ¶

Ansible can transfer a module to a remote machine in one of two ways:

it can write out the module to a temporary file on the remote host and then use a second connection to the remote host to execute it with the interpreter that the module needs
or it can use what’s known as pipelining to execute the module by piping it into the remote interpreter’s stdin.

Pipelining only works with modules written in Python at this time because Ansible only knows that Python supports this mode of operation. Supporting pipelining means that whatever format the module payload takes before being sent over the wire must be executable by Python via stdin.

Why pass args over stdin?¶

Passing arguments via stdin was chosen for the following reasons:

When combined with ANSIBLE_PIPELINING, this keeps the module’s arguments from temporarily being saved onto disk on the remote machine. This makes it harder (but not impossible) for a malicious user on the remote machine to steal any sensitive information that may be present in the arguments.
Command line arguments would be insecure as most systems allow unprivileged users to read the full commandline of a process.
Environment variables are usually more secure than the commandline but some systems limit the total size of the environment. This could lead to truncation of the parameters if we hit that limit.

AnsibleModule ¶

Argument spec ¶

The argument_spec provided to AnsibleModule defines the supported arguments for a module, as well as their type, defaults and more.

Example argument_spec:

module = AnsibleModule(argument_spec=dict(
    top_level=dict(
        type='dict',
        options=dict(
            second_level=dict(
                default=True,
                type='bool',
            )
        )
    )
))

This section will discuss the behavioral attributes for arguments:

type ¶

type allows you to define the type of the value accepted for the argument. The default value for type is str. Possible values are:

str
list
dict
bool
int
float
path
raw
jsonarg
json
bytes
bits

The raw type, performs no type validation or type casing, and maintains the type of the passed value.

elements ¶

elements works in combination with type when type='list'. elements can then be defined as elements='int' or any other type, indicating that each element of the specified list should be of that type.

default ¶

The default option allows sets a default value for the argument for the scenario when the argument is not provided to the module. When not specified, the default value is None.

fallback ¶

fallback accepts a tuple where the first argument is a callable (function) that will be used to perform the lookup, based on the second argument. The second argument is a list of values to be accepted by the callable.

The most common callable used is env_fallback which will allow an argument to optionally use an environment variable when the argument is not supplied.

Example:

username=dict(fallback=(env_fallback, ['ANSIBLE_NET_USERNAME']))

choices ¶

choices accepts a list of choices that the argument will accept. The types of choices should match the type.

required ¶

required accepts a boolean, either True or False that indicates that the argument is required. This should not be used in combination with default.

no_log ¶

no_log indicates that the value of the argument should not be logged or displayed.

aliases ¶

aliases accepts a list of alternative argument names for the argument, such as the case where the argument is name but the module accepts aliases=['pkg'] to allow pkg to be interchangably with name

options ¶

options implements the ability to create a sub-argument_spec, where the sub options of the top level argument are also validated using the attributes discussed in this section. The example at the top of this section demonstrates use of options. type or elements should be dict is this case.

apply_defaults ¶

apply_defaults works alongside options and allows the default of the sub-options to be applied even when the top-level argument is not supplied.

In the example of the argument_spec at the top of this section, it would allow module.params['top_level']['second_level'] to be defined, even if the user does not provide top_level when calling the module.

removed_in_version ¶

removed_in_version indicates which version of Ansible a deprecated argument will be removed in.