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mach | ||
bash-completion.sh | ||
README.rst | ||
setup.py |
The mach Driver =============== The *mach* driver is the command line interface (CLI) to the source tree. The *mach* driver is invoked by running the *mach* script or from instantiating the *Mach* class from the *mach.main* module. Implementing mach Commands -------------------------- The *mach* driver follows the convention of popular tools like Git, Subversion, and Mercurial and provides a common driver for multiple subcommands. Subcommands are implemented by decorating a class and by decorating methods that act as subcommand handlers. Relevant decorators are defined in the *mach.decorators* module. There are the *Command* and *CommandArgument* decorators, which should be used on methods to denote that a specific method represents a handler for a mach subcommand. There is also the *CommandProvider* decorator, which is applied to a class to denote that it contains mach subcommands. 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 as arguments to *argparse.ArgumentParser.add_parser()* and *argparse.ArgumentParser.add_argument()*. 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, force=False): # 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 Mach ----------------------- Mach is just a frontend. Therefore, code in this package should pertain to one of 3 areas: 1. Obtaining user input (parsing arguments, prompting, etc) 2. Calling into some other Python package 3. Formatting output Mach should not contain core logic pertaining to the desired task. If you find yourself needing to invent some new functionality, you should implement it as a generic package outside of mach and then write a mach shim to call into it. There are many advantages to this approach, including reusability outside of mach (others may want to write other frontends) and easier testing (it is easier to test generic libraries than code that interacts with the command line or terminal). Keeping Frontend Modules Small ------------------------------ The frontend modules providing mach commands are currently all loaded when the mach CLI driver starts. Therefore, there is potential for *import bloat*. We want the CLI driver to load quickly. So, please delay load external modules until they are actually required. In other words, don't use a global *import* when you can import from inside a specific command's handler. Structured Logging ================== One of the features of mach is 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}')