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.
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))