Persistent caching for python functions
Persistent caching for python functions
Simply add a decorator to a python function and cache the results for future use. Extremely handy when you are dealing with I/O heavy operations which seldom changes or CPU intensive functions as well.
Anatomically, once a function is called, result from the function is cached into an SQLite3 database locally, with an expiry time. There is a maximum length for the cache to prevent cache flooding the file system.
pip install cashier
Or you can clone the source and run setup.py
git clone git@github.com:atmb4u/cashier.git
cd cashier
python setup.py install
from cashier import cache
@cache()
def complex_function(a,b,c,d):
return complex_calculation(a,b,c,d)
If you go ahead on the above configuration, following are the default values
cache_file : .cache
8460010000Falsefrom cashier import cache
@cache(cache_file="sample.db", cache_time=7200, cache_length=1000,
retry_if_blank=True)
def complex_function(a, b, c, d):
return complex_calculation(a, b, c, d)
cache_file : SQLite3 file name to which cached data should be written into (defaults to .cache)
cache_time : how long should the data be cached in seconds (defaults to 1 day)
cache_length : how many different arguments and corresponding data should be cached (defaults to 10000)
retry_if_blank : If True, will retry for the data if blank data is cached ( default is False)
For reproducing results, run python test.py from the project root.
No Cache Run: 9.932126 seconds
First Caching Run: 9.484081 seconds
Cached Run: 0.606016 seconds (16 x faster)