Mini Shell
import inspect
import six
import sys
import textwrap
from fabric import state
from fabric.utils import abort, warn, error
from fabric.network import to_dict, disconnect_all
from fabric.context_managers import settings
from fabric.job_queue import JobQueue
from fabric.task_utils import crawl, merge, parse_kwargs
from fabric.exceptions import NetworkError
if sys.version_info[:2] == (2, 5):
# Python 2.5 inspect.getargspec returns a tuple
# instead of ArgSpec namedtuple.
class ArgSpec(object):
def __init__(self, args, varargs, keywords, defaults):
self.args = args
self.varargs = varargs
self.keywords = keywords
self.defaults = defaults
self._tuple = (args, varargs, keywords, defaults)
def __getitem__(self, idx):
return self._tuple[idx]
def patched_get_argspec(func):
return ArgSpec(*inspect._getargspec(func))
inspect._getargspec = inspect.getargspec
inspect.getargspec = patched_get_argspec
def get_task_details(task):
details = [
textwrap.dedent(task.__doc__)
if task.__doc__
else 'No docstring provided']
argspec = inspect.getargspec(task)
default_args = [] if not argspec.defaults else argspec.defaults
num_default_args = len(default_args)
args_without_defaults = argspec.args[:len(argspec.args) - num_default_args]
args_with_defaults = argspec.args[-1 * num_default_args:]
details.append('Arguments: %s' % (
', '.join(
args_without_defaults + [
'%s=%r' % (arg, default)
for arg, default in zip(args_with_defaults, default_args)
])
))
return '\n'.join(details)
def _get_list(env):
def inner(key):
return env.get(key, [])
return inner
class Task(object):
"""
Abstract base class for objects wishing to be picked up as Fabric tasks.
Instances of subclasses will be treated as valid tasks when present in
fabfiles loaded by the :doc:`fab </usage/fab>` tool.
For details on how to implement and use `~fabric.tasks.Task` subclasses,
please see the usage documentation on :ref:`new-style tasks
<new-style-tasks>`.
"""
name = 'undefined'
use_task_objects = True
aliases = None
is_default = False
# TODO: make it so that this wraps other decorators as expected
def __init__(self, alias=None, aliases=None, default=False, name=None,
*args, **kwargs):
if alias is not None:
self.aliases = [alias, ]
if aliases is not None:
self.aliases = aliases
if name is not None:
self.name = name
self.is_default = default
def __details__(self):
return get_task_details(self.run)
def run(self):
raise NotImplementedError
def get_hosts_and_effective_roles(self, arg_hosts, arg_roles, arg_exclude_hosts, env=None):
"""
Return a tuple containing the host list the given task should be using
and the roles being used.
See :ref:`host-lists` for detailed documentation on how host lists are
set.
.. versionchanged:: 1.9
"""
env = env or {'hosts': [], 'roles': [], 'exclude_hosts': []}
roledefs = env.get('roledefs', {})
# Command line per-task takes precedence over anything else.
if arg_hosts or arg_roles:
return merge(arg_hosts, arg_roles, arg_exclude_hosts, roledefs), arg_roles
# Decorator-specific hosts/roles go next
func_hosts = getattr(self, 'hosts', [])
func_roles = getattr(self, 'roles', [])
if func_hosts or func_roles:
return merge(func_hosts, func_roles, arg_exclude_hosts, roledefs), func_roles
# Finally, the env is checked (which might contain globally set lists
# from the CLI or from module-level code). This will be the empty list
# if these have not been set -- which is fine, this method should
# return an empty list if no hosts have been set anywhere.
env_vars = list(map(_get_list(env), "hosts roles exclude_hosts".split()))
env_vars.append(roledefs)
return merge(*env_vars), env.get('roles', [])
def get_pool_size(self, hosts, default):
# Default parallel pool size (calculate per-task in case variables
# change)
default_pool_size = default or len(hosts)
# Allow per-task override
# Also cast to int in case somebody gave a string
from_task = getattr(self, 'pool_size', None)
pool_size = int(from_task or default_pool_size)
# But ensure it's never larger than the number of hosts
pool_size = min((pool_size, len(hosts)))
# Inform user of final pool size for this task
if state.output.debug:
print("Parallel tasks now using pool size of %d" % pool_size)
return pool_size
class WrappedCallableTask(Task):
"""
Wraps a given callable transparently, while marking it as a valid Task.
Generally used via `~fabric.decorators.task` and not directly.
.. seealso:: `~fabric.docs.unwrap_tasks`, `~fabric.decorators.task`
"""
def __init__(self, callable, *args, **kwargs):
super(WrappedCallableTask, self).__init__(*args, **kwargs)
self.wrapped = callable
# Don't use getattr() here -- we want to avoid touching self.name
# entirely so the superclass' value remains default.
if hasattr(callable, '__name__'):
if self.name == 'undefined':
self.__name__ = self.name = callable.__name__
else:
self.__name__ = self.name
if hasattr(callable, '__doc__'):
self.__doc__ = callable.__doc__
if hasattr(callable, '__module__'):
self.__module__ = callable.__module__
def __call__(self, *args, **kwargs):
return self.run(*args, **kwargs)
def run(self, *args, **kwargs):
return self.wrapped(*args, **kwargs)
def __getattr__(self, k):
return getattr(self.wrapped, k)
def __details__(self):
orig = self
while 'wrapped' in orig.__dict__:
orig = orig.__dict__.get('wrapped')
return get_task_details(orig)
def requires_parallel(task):
"""
Returns True if given ``task`` should be run in parallel mode.
Specifically:
* It's been explicitly marked with ``@parallel``, or:
* It's *not* been explicitly marked with ``@serial`` *and* the global
parallel option (``env.parallel``) is set to ``True``.
"""
return (
(state.env.parallel and not getattr(task, 'serial', False))
or getattr(task, 'parallel', False)
)
def _parallel_tasks(commands_to_run):
return any(map(
lambda x: requires_parallel(crawl(x[0], state.commands)),
commands_to_run
))
def _is_network_error_ignored():
return not state.env.use_exceptions_for['network'] and state.env.skip_bad_hosts
def _parallel_wrap(task, args, kwargs, queue, name, env):
# Wrap in another callable that:
# * expands the env it's given to ensure parallel, linewise, etc are
# all set correctly and explicitly
# * nukes the connection cache to prevent shared-access problems
# * knows how to send the tasks' return value back over a Queue
# * captures exceptions raised by the task
state.env.update(env)
try:
state.connections.clear()
queue.put({'name': name, 'result': task.run(*args, **kwargs)})
except BaseException as e: # We really do want to capture everything
# SystemExit implies use of abort(), which prints its own
# traceback, host info etc -- so we don't want to double up
# on that. For everything else, though, we need to make
# clear what host encountered the exception that will
# print.
if type(e) is not SystemExit:
if not (isinstance(e, NetworkError) and _is_network_error_ignored()):
sys.stderr.write("!!! Parallel execution exception under host %r:\n" % name)
queue.put({'name': name, 'result': e})
# Here, anything -- unexpected exceptions, or abort()
# driven SystemExits -- will bubble up and terminate the
# child process.
if not (isinstance(e, NetworkError) and _is_network_error_ignored()):
raise
def _execute(task, host, my_env, args, kwargs, jobs, queue, multiprocessing):
"""
Primary single-host work body of execute()
"""
# Log to stdout
if state.output.running and not hasattr(task, 'return_value'):
print("[%s] Executing task '%s'" % (host, my_env['command']))
# Create per-run env with connection settings
local_env = to_dict(host)
local_env.update(my_env)
# Set a few more env flags for parallelism
if queue is not None:
local_env.update({'parallel': True, 'linewise': True})
# Handle parallel execution
if queue is not None: # Since queue is only set for parallel
name = local_env['host_string']
# Stuff into Process wrapper
kwarg_dict = {
'task': task,
'args': args,
'kwargs': kwargs,
'queue': queue,
'name': name,
'env': local_env,
}
p = multiprocessing.Process(target=_parallel_wrap, kwargs=kwarg_dict)
# Name/id is host string
p.name = name
# Add to queue
jobs.append(p)
# Handle serial execution
else:
with settings(**local_env):
return task.run(*args, **kwargs)
def _is_task(task):
return isinstance(task, Task)
def execute(task, *args, **kwargs):
"""
Execute ``task`` (callable or name), honoring host/role decorators, etc.
``task`` may be an actual callable object, or it may be a registered task
name, which is used to look up a callable just as if the name had been
given on the command line (including :ref:`namespaced tasks <namespaces>`,
e.g. ``"deploy.migrate"``.
The task will then be executed once per host in its host list, which is
(again) assembled in the same manner as CLI-specified tasks: drawing from
:option:`-H`, :ref:`env.hosts <hosts>`, the `~fabric.decorators.hosts` or
`~fabric.decorators.roles` decorators, and so forth.
``host``, ``hosts``, ``role``, ``roles`` and ``exclude_hosts`` kwargs will
be stripped out of the final call, and used to set the task's host list, as
if they had been specified on the command line like e.g. ``fab
taskname:host=hostname``.
Any other arguments or keyword arguments will be passed verbatim into
``task`` (the function itself -- not the ``@task`` decorator wrapping your
function!) when it is called, so ``execute(mytask, 'arg1',
kwarg1='value')`` will (once per host) invoke ``mytask('arg1',
kwarg1='value')``.
:returns:
a dictionary mapping host strings to the given task's return value for
that host's execution run. For example, ``execute(foo, hosts=['a',
'b'])`` might return ``{'a': None, 'b': 'bar'}`` if ``foo`` returned
nothing on host `a` but returned ``'bar'`` on host `b`.
In situations where a task execution fails for a given host but overall
progress does not abort (such as when :ref:`env.skip_bad_hosts
<skip-bad-hosts>` is True) the return value for that host will be the
error object or message.
.. seealso::
:ref:`The execute usage docs <execute>`, for an expanded explanation
and some examples.
.. versionadded:: 1.3
.. versionchanged:: 1.4
Added the return value mapping; previously this function had no defined
return value.
"""
my_env = {'clean_revert': True}
results = {}
# Obtain task
is_callable = callable(task)
if not (is_callable or _is_task(task)):
# Assume string, set env.command to it
my_env['command'] = task
task = crawl(task, state.commands)
if task is None:
msg = "%r is not callable or a valid task name" % (my_env['command'],)
if state.env.get('skip_unknown_tasks', False):
warn(msg)
return
else:
abort(msg)
# Set env.command if we were given a real function or callable task obj
else:
dunder_name = getattr(task, '__name__', None)
my_env['command'] = getattr(task, 'name', dunder_name)
# Normalize to Task instance if we ended up with a regular callable
if not _is_task(task):
task = WrappedCallableTask(task)
# Filter out hosts/roles kwargs
new_kwargs, hosts, roles, exclude_hosts = parse_kwargs(kwargs)
# Set up host list
my_env['all_hosts'], my_env['effective_roles'] = task.get_hosts_and_effective_roles(hosts, roles,
exclude_hosts, state.env)
parallel = requires_parallel(task)
if parallel:
# Import multiprocessing if needed, erroring out usefully
# if it can't.
try:
import multiprocessing
except ImportError:
import traceback
tb = traceback.format_exc()
abort(tb + """
At least one task needs to be run in parallel, but the
multiprocessing module cannot be imported (see above
traceback.) Please make sure the module is installed
or that the above ImportError is fixed.""")
else:
multiprocessing = None
# Get pool size for this task
pool_size = task.get_pool_size(my_env['all_hosts'], state.env.pool_size)
# Set up job queue in case parallel is needed
queue = multiprocessing.Queue() if parallel else None
jobs = JobQueue(pool_size, queue)
if state.output.debug:
jobs._debug = True
# Call on host list
if my_env['all_hosts']:
# Attempt to cycle on hosts, skipping if needed
for host in my_env['all_hosts']:
try:
results[host] = _execute(
task, host, my_env, args, new_kwargs, jobs, queue,
multiprocessing
)
except NetworkError as e:
results[host] = e
# Backwards compat test re: whether to use an exception or
# abort
if not state.env.use_exceptions_for['network']:
func = warn if state.env.skip_bad_hosts else abort
error(e.message, func=func, exception=e.wrapped)
else:
raise
# If requested, clear out connections here and not just at the end.
if state.env.eagerly_disconnect:
disconnect_all()
# If running in parallel, block until job queue is emptied
if jobs:
err = "One or more hosts failed while executing task '%s'" % (
my_env['command']
)
jobs.close()
# Abort if any children did not exit cleanly (fail-fast).
# This prevents Fabric from continuing on to any other tasks.
# Otherwise, pull in results from the child run.
ran_jobs = jobs.run()
for name, d in six.iteritems(ran_jobs):
if d['exit_code'] != 0:
if isinstance(d['results'], NetworkError) and \
_is_network_error_ignored():
error(d['results'].message, func=warn, exception=d['results'].wrapped)
elif isinstance(d['results'], BaseException):
error(err, exception=d['results'])
else:
error(err)
results[name] = d['results']
# Or just run once for local-only
else:
with settings(**my_env):
results['<local-only>'] = task.run(*args, **new_kwargs)
# Return what we can from the inner task executions
return results
Zerion Mini Shell 1.0