Question

In essence, I want to put a variable on the stack, that will be reachable by all calls below that part on the stack until the block exits. In Java I would solve this using a static thread local with support methods, that then could be accessed from methods.

Typical example: you get a request, and open a database connection. Until the request is complete, you want all code to use this database connection. After finishing and closing the request, you close the database connection.

What I need this for, is a report generator. Each report consist of multiple parts, each part can rely on different calculations, sometimes different parts relies in part on the same calculation. As I don’t want to repeat heavy calculations, I need to cache them. My idea is to decorate methods with a cache decorator. The cache creates an id based on the method name and module, and it’s arguments, looks if it has this allready calculated in a stack variable, and executes the method if not.

I will try and clearify by showing my current implementation. Want I want to do is to simplify the code for those implementing calculations.

First, I have the central cache access object, which I call MathContext:

class MathContext(object):
    def __init__(self, fn): 
        self.fn = fn
        self.cache = dict()
    def get(self, calc_config):
        id = create_id(calc_config)
        if id not in self.cache:
            self.cache[id] = calc_config.exec(self)
        return self.cache[id]

The fn argument is the filename the context is created in relation to, from where data can be read to be calculated.

Then we have the Calculation class:

 class CalcBase(object):
     def exec(self, math_context):
         raise NotImplementedError

And here is a stupid Fibonacci example. Non of the methods are actually recursive, they work on large sets of data instead, but it works to demonstrate how you would depend on other calculations:

class Fibonacci(CalcBase):
    def __init__(self, n): self.n = n
    def exec(self, math_context):
        if self.n < 2: return 1
        a = math_context.get(Fibonacci(self.n-1))
        b = math_context.get(Fibonacci(self.n-2))
        return a+b

What I want Fibonacci to be instead, is just a decorated method:

@cache
def fib(n):
    if n<2: return 1
    return fib(n-1)+fib(n-2)

With the math_context example, when math_context goes out of scope, so does all it’s cached values. I want the same thing for the decorator. Ie. at point X, everything cached by @cache is dereferrenced to be gced.

Answer 1

I went ahead and made something that might just do what you want. It can be used as both a decorator and a context manager:

from __future__ import with_statement
try:
    import cPickle as pickle
except ImportError:
    import pickle


class cached(object):
    """Decorator/context manager for caching function call results.
    All results are cached in one dictionary that is shared by all cached
    functions.

    To use this as a decorator:
        @cached
        def function(...):
            ...

    The results returned by a decorated function are not cleared from the
    cache until decorated_function.clear_my_cache() or cached.clear_cache()
    is called

    To use this as a context manager:

        with cached(function) as function:
            ...
            function(...)
            ...

    The function's return values will be cleared from the cache when the
    with block ends

    To clear all cached results, call the cached.clear_cache() class method
    """

    _CACHE = {}

    def __init__(self, fn):
        self._fn = fn

    def __call__(self, *args, **kwds):
        key = self._cache_key(*args, **kwds)
        function_cache = self._CACHE.setdefault(self._fn, {})
        try:
            return function_cache[key]
        except KeyError:
            function_cache[key] = result = self._fn(*args, **kwds)
            return result

    def clear_my_cache(self):
        """Clear the cache for a decorated function
        """
        try:
            del self._CACHE[self._fn]
        except KeyError:
            pass # no cached results

    def __enter__(self):
        return self

    def __exit__(self, type, value, traceback):
        self.clear_my_cache()

    def _cache_key(self, *args, **kwds):
        """Create a cache key for the given positional and keyword
        arguments. pickle.dumps() is used because there could be
        unhashable objects in the arguments, but passing them to 
        pickle.dumps() will result in a string, which is always hashable.

        I used this to make the cached class as generic as possible. Depending
        on your requirements, other key generating techniques may be more
        efficient
        """
        return pickle.dumps((args, sorted(kwds.items())), pickle.HIGHEST_PROTOCOL)

    @classmethod
    def clear_cache(cls):
        """Clear everything from all functions from the cache
        """
        cls._CACHE = {}


if __name__ == '__main__':
    # used as decorator
    @cached
    def fibonacci(n):
        print "calculating fibonacci(%d)" % n
        if n == 0:
            return 0
        if n == 1:
            return 1
        return fibonacci(n - 1) + fibonacci(n - 2)

    for n in xrange(10):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))


    def lucas(n):
        print "calculating lucas(%d)" % n
        if n == 0:
            return 2
        if n == 1:
            return 1
        return lucas(n - 1) + lucas(n - 2)

    # used as context manager
    with cached(lucas) as lucas:
        for i in xrange(10):
            print 'lucas(%d) = %d' % (i, lucas(i))

    for n in xrange(9, -1, -1):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))

    cached.clear_cache()

    for n in xrange(9, -1, -1):
        print 'fibonacci(%d) = %d' % (n, fibonacci(n))