添加条件以检查是否已解析project_item和branch

Question

有基于project_item和分支的ssh命令正在运行。我不想在已经运行的project_item和分支组合上运行ssh命令，所以我尝试添加一个条件来检查project_item和分支组合是否已经解析，有关于如何添加它的想法吗？

  for project_item in projects:
      branch = manifest_data(project_item)
      print "PROJECT_ITEM: " + project_item
      print "BRANCH: " + branch
      /*********condition to check if the project_item/branch combo is already parsed/***********
      mft = manifest_table(project_item,branch)
      if 'win32' in OS_Type and branch != 'PROJECT NOT ENBALED':
        #print OS_Type
        cmd = "\"\"%s\Git\\bin\sh.exe\" --login -c \"ssh -l user -p 29418 review-android.company.com gerrit query project:%s branch:%s --format=JSON status:open --commit-message --current-patch-set\"\" >> gerrit_output.txt" %(os.environ['ProgramFiles(x86)'], project_item,branch)
      elif 'linux' in OS_Type:
        #print OS_Type
        cmd = "ssh -l user -p 29418 review-android.company.com gerrit query project:%s branch:%s --format=JSON status:open --commit-message --current-patch-set\"\" >> gerrit_output.txt" %(project_item,branch)

Answer 1

如果您只是想避免运行同一命令两次，您只需记住该命令，然后检查您是否已经运行了该命令：

# Create a set to remember your executed commands. Sets are better than
# lists here, since you will repeatedly check if an item exists.
# See: https://wiki.python.org/moin/TimeComplexity
executed_commands = set()

for project_item in projects:
    # Your posted code...

    identifier = hash((project_item, branch))
    # check if command has already been executed
    if identifier not in executed_commands:
        executed_commands.add(identifier)

        # Actually run cmd...

Answer 2

我不完全理解你的问题，但你似乎是在问如何检查一个函数是否已经被调用一次，以便只计算一次？

"memoize“模式很好地实现了这一点。一个版本的can be found here。

完整的代码如下所示：

import collections
import functools

class memoized(object):
   '''Decorator. Caches a function's return value each time it is called.
   If called later with the same arguments, the cached value is returned
   (not reevaluated).
   '''
   def __init__(self, func):
      self.func = func
      self.cache = {}
   def __call__(self, *args):
      if not isinstance(args, collections.Hashable):
         # uncacheable. a list, for instance.
         # better to not cache than blow up.
         return self.func(*args)
      if args in self.cache:
         return self.cache[args]
      else:
         value = self.func(*args)
         self.cache[args] = value
         return value
   def __repr__(self):
      '''Return the function's docstring.'''
      return self.func.__doc__
   def __get__(self, obj, objtype):
      '''Support instance methods.'''
      return functools.partial(self.__call__, obj)

@memoized
def fibonacci(n):
   "Return the nth fibonacci number."
   if n in (0, 1):
      return n
   return fibonacci(n-1) + fibonacci(n-2)

print fibonacci(12)

它只是缓存函数的返回值。你可能需要一些更智能的缓存，然后你应该检查一个真正的缓存，当然，但看起来你只是在优化，所以这可能是一个开始。

Python中的缓存已经在这里讨论过了：Is there a Python caching library?

Answer 3

为什么不简单地将已解析对象的散列存储在列表中？

parsed_list=[]
for project_item in projects:
    branch = manifest_data(project_item)
    item_hash = hash(branch+project_item)
    print "PROJECT_ITEM: " + project_item
    print "BRANCH: " + branch
    if item_hash not in parsed_list:
        parsed_list.append(item_hash)
        # more code