Generic command line command dispatching framework.
Project description
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.
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:
Obtaining user input (parsing arguments, prompting, etc)
Calling into some other Python package
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}’)
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