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Description

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.

Code Quality Rank: L5
Programming language: Python
License: BSD 3-clause "New" or "Revised" License
Tags: HTTP     Caching     Functions     Decorator    
Latest version: v1.2.0

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README

Cashier

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.

Installation

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

Usage

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

  • 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, 
       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)

Performance Benchmark

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)