# What is this?
The smart way to cache outputs to cold storage.
- auto rebuilds cache when you edit function source code
- uses mutltiprocessing to keep main thread running fast while saving to disk
- excellent change-tracking of arguments thanks to `super_hash`
- can watch change-tracking of external vars and method attributes
- uses python pickle for saving function outputs, if `dill` is available it will use that instead
# How do I use this?
`pip install cool_cache`
```python
from cool_cache import cache, settings
settings.default_folder = None # disable caching to cold-storage, and instead cache to ram
# this is the default, but you can change it
settings.default_folder = "cache.ignore/"
#
#
# simple usage (updates whenever function is edited (excluding comments) or when args change)
#
#
@cache()
def things_with_args(a,b,c):
from time import sleep; sleep(1) # <- simulating a long-running process
return a + b + c
things_with_args(1,2,3) # not yet cached
things_with_args(1,2,3) # uses cache
things_with_args(9,9,9) # not yet cached
things_with_args(9,9,9) # uses cache
#
#
# external vars
#
#
external_counter = 0
@cache(depends_on=lambda:[external_counter])
def things_with_external(a,b,c):
global external_counter
from time import sleep; sleep(1) # <- simulating a long-running process
return external_counter + a + b + c
#
# behavior
#
things_with_external(4,5,6) # not yet cached
things_with_external(4,5,6) # uses cache
external_counter = 1
things_with_external(4,5,6) # not yet cached (because external_counter changed)
things_with_external(4,5,6) # uses cache
#
#
# filepath arguments
#
#
@cache(watch_filepaths=lambda arg1, arg2, arg3: [ arg1, arg2 ]) # because first two args are filepaths
def things_with_files(filepath1, filepath2, c):
with open(filepath1, 'r') as in_file1:
with open(filepath2, 'r') as in_file2:
return in_file1.readlines() + c + in_file2.readlines()
#
# behavior
#
things_with_files("./file1.txt", "./file2.txt", "hello") # not yet cached
things_with_files("./file1.txt", "./file2.txt", "hello") # cached
with open("./file2.txt",'w') as f: f.write(str(" world")) # <-- modify the file
things_with_files("./file1.txt", "./file2.txt", "hello") # not yet cached, because file change is detected
things_with_files("./file1.txt", "./file2.txt", "hello") # cached
#
#
# class methods (e.g. self)
#
#
class MyThing:
def __init__(self, path, other_stuff):
self.path = path
self.other_stuff = other_stuff
# for example: self.path changing will affect the cache, but self.other_stuff wont affect the cache
@cache(watch_attributes=lambda self:[ self.path, ])
def do_some_stuff(self, arg1):
from time import sleep; sleep(1)
return self.path + arg1
#
# bust=True wipes out all cached values for this function on the next run
#
@cache(bust=True)
def things(a,b,c):
return 10
```
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"description": "# What is this?\n\nThe smart way to cache outputs to cold storage.\n\n- auto rebuilds cache when you edit function source code\n- uses mutltiprocessing to keep main thread running fast while saving to disk\n- excellent change-tracking of arguments thanks to `super_hash`\n- can watch change-tracking of external vars and method attributes\n- uses python pickle for saving function outputs, if `dill` is available it will use that instead\n\n# How do I use this?\n\n`pip install cool_cache`\n\n```python\nfrom cool_cache import cache, settings\n\nsettings.default_folder = None # disable caching to cold-storage, and instead cache to ram\n# this is the default, but you can change it\nsettings.default_folder = \"cache.ignore/\"\n\n# \n# \n# simple usage (updates whenever function is edited (excluding comments) or when args change)\n# \n# \n@cache()\ndef things_with_args(a,b,c):\n \n from time import sleep; sleep(1) # <- simulating a long-running process\n \n return a + b + c\n\nthings_with_args(1,2,3) # not yet cached\nthings_with_args(1,2,3) # uses cache\nthings_with_args(9,9,9) # not yet cached\nthings_with_args(9,9,9) # uses cache\n\n\n# \n# \n# external vars\n# \n# \nexternal_counter = 0\n@cache(depends_on=lambda:[external_counter])\ndef things_with_external(a,b,c):\n global external_counter\n \n from time import sleep; sleep(1) # <- simulating a long-running process\n \n return external_counter + a + b + c\n\n\n# \n# behavior\n# \nthings_with_external(4,5,6) # not yet cached\nthings_with_external(4,5,6) # uses cache\nexternal_counter = 1\nthings_with_external(4,5,6) # not yet cached (because external_counter changed)\nthings_with_external(4,5,6) # uses cache\n\n# \n# \n# filepath arguments\n# \n# \n@cache(watch_filepaths=lambda arg1, arg2, arg3: [ arg1, arg2 ]) # because first two args are filepaths\ndef things_with_files(filepath1, filepath2, c):\n with open(filepath1, 'r') as in_file1:\n with open(filepath2, 'r') as in_file2:\n return in_file1.readlines() + c + in_file2.readlines()\n\n# \n# behavior\n# \nthings_with_files(\"./file1.txt\", \"./file2.txt\", \"hello\") # not yet cached\nthings_with_files(\"./file1.txt\", \"./file2.txt\", \"hello\") # cached\nwith open(\"./file2.txt\",'w') as f: f.write(str(\" world\")) # <-- modify the file\nthings_with_files(\"./file1.txt\", \"./file2.txt\", \"hello\") # not yet cached, because file change is detected\nthings_with_files(\"./file1.txt\", \"./file2.txt\", \"hello\") # cached\n\n# \n# \n# class methods (e.g. self)\n# \n# \nclass MyThing:\n def __init__(self, path, other_stuff):\n self.path = path\n self.other_stuff = other_stuff\n \n # for example: self.path changing will affect the cache, but self.other_stuff wont affect the cache\n @cache(watch_attributes=lambda self:[ self.path, ])\n def do_some_stuff(self, arg1):\n from time import sleep; sleep(1)\n return self.path + arg1\n\n# \n# bust=True wipes out all cached values for this function on the next run\n# \n@cache(bust=True)\ndef things(a,b,c):\n return 10\n\n```\n\n\n",
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