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 ====

 mach

 ====

 Mach (German for *do*) is a generic command dispatcher for the command

 line.

 To use mach, you install the mach core (a Python package), create an

 executable *driver* script (named whatever you want), and write mach

 commands. When the *driver* is executed, mach dispatches to the

 requested command handler automatically.

 Features

 ========

 On a high level, mach is similar to using argparse with subparsers (for

 command handling). When you dig deeper, mach offers a number of

 additional features:

 Distributed command definitions

   With optparse/argparse, you have to define your commands on a central

   parser instance. With mach, you annotate your command methods with

   decorators and mach finds and dispatches to them automatically.

 Command categories

   Mach commands can be grouped into categories when displayed in help.

   This is currently not possible with argparse.

 Logging management

   Mach provides a facility for logging (both classical text and

   structured) that is available to any command handler.

 Settings files

   Mach provides a facility for reading settings from an ini-like file

   format.

 Components

 ==========

 Mach is conceptually composed of the following components:

 core

   The mach core is the core code powering mach. This is a Python package

   that contains all the business logic that makes mach work. The mach

   core is common to all mach deployments.

 commands

   These are what mach dispatches to. Commands are simply Python methods

   registered as command names. The set of commands is unique to the

   environment mach is deployed in.

 driver

   The *driver* is the entry-point to mach. It is simply an executable

   script that loads the mach core, tells it where commands can be found,

   then asks the mach core to handle the current request. The driver is

   unique to the deployed environment. But, it's usually based on an

   example from this source tree.

 Project State

 =============

 mach was originally written as a command dispatching framework to aid

 Firefox development. While the code is mostly generic, there are still

 some pieces that closely tie it to Mozilla/Firefox. The goal is for

 these to eventually be removed and replaced with generic features so

 mach is suitable for anybody to use. Until then, mach may not be the

 best fit for you.

 Implementing Commands

 ---------------------

 Mach commands are defined via Python decorators.

 All the relevant decorators are defined in the *mach.decorators* module.

 The important decorators are as follows:

 CommandProvider

   A class decorator that denotes that a class contains mach

   commands. The decorator takes no arguments.

 Command

   A method decorator that denotes that the method should be called when

   the specified command is requested. The decorator takes a command name

   as its first argument and a number of additional arguments to

   configure the behavior of the command.

 CommandArgument

   A method decorator that defines an argument to the command. Its

   arguments are essentially proxied to ArgumentParser.add_argument()

 Classes with the *@CommandProvider* decorator *must* have an *__init__*

 method that accepts 1 or 2 arguments. If it accepts 2 arguments, the

 2nd argument will be a *MachCommandContext* instance. This is just a named

 tuple containing references to objects provided by the mach driver.

 Here is a complete example::

     from mach.decorators import (

         CommandArgument,

         CommandProvider,

         Command,

     )

     @CommandProvider

     class MyClass(object):

         @Command('doit', help='Do ALL OF THE THINGS.')

         @CommandArgument('--force', '-f', action='store_true',

             help='Force doing it.')

         def doit(self, force=False):

             # Do stuff here.

 When the module is loaded, the decorators tell mach about all handlers.

 When mach runs, it takes the assembled metadata from these handlers and

 hooks it up to the command line driver. Under the hood, arguments passed

 to the decorators are being used to help mach parse command arguments,

 formulate arguments to the methods, etc. See the documentation in the

 *mach.base* module for more.

 The Python modules defining mach commands do not need to live inside the

 main mach source tree.

 Conditionally Filtering Commands

 --------------------------------

 Sometimes it might only make sense to run a command given a certain

 context. For example, running tests only makes sense if the product

 they are testing has been built, and said build is available. To make

 sure a command is only runnable from within a correct context, you can

 define a series of conditions on the *Command* decorator.

 A condition is simply a function that takes an instance of the

 *CommandProvider* class as an argument, and returns True or False. If

 any of the conditions defined on a command return False, the command

 will not be runnable. The doc string of a condition function is used in

 error messages, to explain why the command cannot currently be run.

 Here is an example:

     from mach.decorators import (

         CommandProvider,

         Command,

     )

     def build_available(cls):

         """The build needs to be available."""

         return cls.build_path is not None

     @CommandProvider

     class MyClass(MachCommandBase):

         def __init__(self, build_path=None):

             self.build_path = build_path

         @Command('run_tests', conditions=[build_available])

         def run_tests(self):

             # Do stuff here.

 It is important to make sure that any state needed by the condition is

 available to instances of the command provider.

 By default all commands without any conditions applied will be runnable,

 but it is possible to change this behaviour by setting *require_conditions*

 to True:

     m = mach.main.Mach()

     m.require_conditions = True

 Minimizing Code in Commands

 ---------------------------

 Mach command modules, classes, and methods work best when they are

 minimal dispatchers. The reason is import bloat. Currently, the mach

 core needs to import every Python file potentially containing mach

 commands for every command invocation. If you have dozens of commands or

 commands in modules that import a lot of Python code, these imports

 could slow mach down and waste memory.

 It is thus recommended that mach modules, classes, and methods do as

 little work as possible. Ideally the module should only import from

 the *mach* package. If you need external modules, you should import them

 from within the command method.

 To keep code size small, the body of a command method should be limited

 to:

 1. Obtaining user input (parsing arguments, prompting, etc)

 2. Calling into some other Python package

 3. Formatting output

 Of course, these recommendations can be ignored if you want to risk

 slower performance.

 In the future, the mach driver may cache the dispatching information or

 have it intelligently loaded to facilitate lazy loading.

 Logging

 =======

 Mach configures a built-in logging facility so commands can easily log

 data.

 What sets the logging facility apart from most loggers you've seen is

 that it encourages structured logging. Instead of conventional logging

 where simple strings are logged, the internal logging mechanism logs all

 events with the following pieces of information:

 * A string *action*

 * A dict of log message fields

 * A formatting string

 Essentially, instead of assembling a human-readable string at

 logging-time, you create an object holding all the pieces of data that

 will constitute your logged event. For each unique type of logged event,

 you assign an *action* name.

 Depending on how logging is configured, your logged event could get

 written a couple of different ways.

 JSON Logging

 ------------

 Where machines are the intended target of the logging data, a JSON

 logger is configured. The JSON logger assembles an array consisting of

 the following elements:

 * Decimal wall clock time in seconds since UNIX epoch

 * String *action* of message

 * Object with structured message data

 The JSON-serialized array is written to a configured file handle.

 Consumers of this logging stream can just perform a readline() then feed

 that into a JSON deserializer to reconstruct the original logged

 message. They can key off the *action* element to determine how to

 process individual events. There is no need to invent a parser.

 Convenient, isn't it?

 Logging for Humans

 ------------------

 Where humans are the intended consumer of a log message, the structured

 log message are converted to more human-friendly form. This is done by

 utilizing the *formatting* string provided at log time. The logger

 simply calls the *format* method of the formatting string, passing the

 dict containing the message's fields.

 When *mach* is used in a terminal that supports it, the logging facility

 also supports terminal features such as colorization. This is done

 automatically in the logging layer - there is no need to control this at

 logging time.

 In addition, messages intended for humans typically prepends every line

 with the time passed since the application started.

 Logging HOWTO

 -------------

 Structured logging piggybacks on top of Python's built-in logging

 infrastructure provided by the *logging* package. We accomplish this by

 taking advantage of *logging.Logger.log()*'s *extra* argument. To this

 argument, we pass a dict with the fields *action* and *params*. These

 are the string *action* and dict of message fields, respectively. The

 formatting string is passed as the *msg* argument, like normal.

 If you were logging to a logger directly, you would do something like:

     logger.log(logging.INFO, 'My name is {name}',

         extra={'action': 'my_name', 'params': {'name': 'Gregory'}})

 The JSON logging would produce something like:

     [1339985554.306338, "my_name", {"name": "Gregory"}]

 Human logging would produce something like:

      0.52 My name is Gregory

 Since there is a lot of complexity using logger.log directly, it is

 recommended to go through a wrapping layer that hides part of the

 complexity for you. The easiest way to do this is by utilizing the

 LoggingMixin:

     import logging

     from mach.mixin.logging import LoggingMixin

     class MyClass(LoggingMixin):

         def foo(self):

              self.log(logging.INFO, 'foo_start', {'bar': True},

                  'Foo performed. Bar: {bar}')

 Entry Points

 ============

 It is possible to use setuptools' entry points to load commands

 directly from python packages. A mach entry point is a function which

 returns a list of files or directories containing mach command

 providers. e.g.::

     def list_providers():

         providers = []

         here = os.path.abspath(os.path.dirname(__file__))

         for p in os.listdir(here):

             if p.endswith('.py'):

                 providers.append(os.path.join(here, p))

         return providers

 See http://pythonhosted.org/setuptools/setuptools.html#dynamic-discovery-of-services-and-plugins

 for more information on creating an entry point. To search for entry

 point plugins, you can call *load_commands_from_entry_point*. This

 takes a single parameter called *group*. This is the name of the entry

 point group to load and defaults to ``mach.providers``. e.g.::

     mach.load_commands_from_entry_point("mach.external.providers")

 Adding Global Arguments

 =======================

 Arguments to mach commands are usually command-specific. However,

 mach ships with a handful of global arguments that apply to all

 commands.

 It is possible to extend the list of global arguments. In your

 *mach driver*, simply call ``add_global_argument()`` on your

 ``mach.main.Mach`` instance. e.g.::

    mach = mach.main.Mach(os.getcwd())

    # Will allow --example to be specified on every mach command.

    mach.add_global_argument('--example', action='store_true',

        help='Demonstrate an example global argument.')