How to put variables on the stack/context in Python

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2

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.

2009-06-16 14:16
by Staale
What's wrong with this design? It looks good. It looks like it meets your need. Perhaps you should update your question to show where or how you want to change this code. Or what this code isn't doing properly - S.Lott 2009-06-16 15:43
The last bit is what I want, but don't know how to implement. The current design is ok, but I feel it's to verbose to type out. I would rather just use decorated methods, so it becomes simpler to use and write. Ie. it's cleaning up the current design - Staale 2009-06-16 16:13


5

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))
2009-06-16 18:43
by Steef
That looks great, will give it a shot tomorrow - Staale 2009-06-16 19:03


2

this question seems to be two question

  • a) sharing db connection
  • b) caching/Memoizing

b) you have answered yourselves

a) I don't seem to understand why you need to put it on stack? you can do one of these

  1. you can use a class and connection could be attribute of it
  2. you can decorate all your function so that they get a connection from central location
  3. each function can explicitly use a global connection method
  4. you can create a connection and pass around it, or create a context object and pass around context,connection can be a part of context

etc, etc

2009-06-16 14:35
by Anurag Uniyal
I used a db connection, because I have experience with using this across a request in Java, and it's the same concept I am trying at. I don't want to use a context variable, as that enforces structure on the methods I am decorating, I want the decorator to be separate and self-managed - Staale 2009-06-16 14:40


0

You could use a global variable wrapped in a getter function:

def getConnection():
    global connection
    if connection:
        return connection
    connection=createConnection()
    return connection
2009-06-16 14:27
by Dan Lorenc


0

"you get a request, and open a database connection.... you close the database connection."

This is what objects are for. Create the connection object, pass it to other objects, and then close it when you're done. Globals are not appropriate. Simply pass the value around as a parameter to the other objects that are doing the work.

"Each report consist of multiple parts, each part can rely on different calculations, sometimes different parts relies in part on the same calculation.... I need to cache them"

This is what objects are for. Create a dictionary with useful calculation results and pass that around from report part to report part.

You don't need to mess with "stack variables", "static thread local" or anything like that. Just pass ordinary variable arguments to ordinary method functions. You'll be a lot happier.


class MemoizedCalculation( object ):
    pass

class Fibonacci( MemoizedCalculation ):
    def __init__( self ):
       self.cache= { 0: 1, 1: 1 }
    def __call__( self, arg ):
       if arg not in self.cache:
           self.cache[arg]= self(arg-1) + self(arg-2)
       return self.cache[arg]

class MathContext( object ):
    def __init__( self ):
        self.fibonacci = Fibonacci()

You can use it like this

>>> mc= MathContext()
>>> mc.fibonacci( 4 )
5

You can define any number of calculations and fold them all into a single container object.

If you want, you can make the MathContext into a formal Context Manager so that it work with the with statement. Add these two methods to MathContext.

def __enter__( self ):
    print "Initialize"
    return self
def __exit__( self, type_, value, traceback ):
    print "Release"

Then you can do this.

with  MathContext() as mc:
    print mc.fibonacci( 4 )

At the end of the with statement, you can guaranteed that the __exit__ method was called.

2009-06-16 14:40
by S.Lott
Calculations can depend on other calculations, and I want to avoid having to do if not x in cache: cache[x] = calc() in every method that depends on x. The Memoize pattern moves in the right direction, but when I use this on multiple files, there is no central part where I can clear out data no longer needed - Staale 2009-06-16 14:55
This is still garden-variety OO design. One object depends on another object. You can easily have objects that exhibit memoization behavior without resorting to "globals" of any kind - S.Lott 2009-06-16 15:14
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