Persistent caching for python functions
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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

git clone
cd cashier
python install


from cashier import 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

  • cache_time : 84600

  • cache_length : 10000

  • retry_if_blank : False

Advanced Usage

from cashier import cache

@cache(cache_file="sample.db", cache_time=7200, cache_length=1000, 
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)

Performance Benchmark

For reproducing results, run python from the project root.

No Cache Run: 9.932126 seconds

First Caching Run: 9.484081 seconds

Cached Run: 0.606016 seconds (16 x faster)