devcache 1.0.2

Provides caching decorator to help speedup development

# devcache A configurable decorator allows methods to return persistently stored data from a cache instead of a normal call.

The use case is to speed up development by caching data from long-running methods

Installation

pip install devcache

Situation

You're working on a project that syncs data from a Database to a CRM.

def get_crm_data():  # Takes multiple minutes
    ...

def get_db_data():  # Takes multiple minutes
    ...    

def compare_and_report():
    crm_data = get_crm_data()
    db_data = get_db_data()
    ...
    diff = ...
    result = save_data(diff)
    ...
    send_report(report)

def save_data(data):  # Takes more than a minute
    ...

As you are trying to improve compare_and_report it takes 5 minutes for everytime you run.

A possible solution would be to use devcache, like so:

Decorate appropriate methods

@devcache(group='crm')
def get_crm_data():  # Takes multiple minutes
     ...

@devcache(group='db')
def get_db_data():  # Takes multiple minutes
    ...    

@devcache(group='save')
def save_data(data):  # Takes more than a minute
    ...

Create a config at ~/.devcache/devcache.yaml

props:
    1:
        group: crm
        use_cache: true
    2:
        group: db
        use_cache: true
    3:
        group: save
        use_cache: true

Now the methods will pull data from the cache as use_cache is true. If a change was required to any saved data set the use_cache to false and data will be generated and stored fresh in the cache.

Now using the cache each testing iteration takes seconds instead of minutes.

Other useful configuration

refresh: true  # refresh true will ignore use_cache and refresh all cached data 
enabled: false # will disable everything and will not save new values to cache
props:
    1:
        group: crm
        use_cache: false
    2:
        group: db
        use_cache: true
    3:
        group: save
        use_cache: true
    -1:  # first props match is used.  
         # ordering is by the key (ie -1, 1 ,2 ,3)   
         # 'group' is optional
        pattern: '.*sfdc.*' # matches fully qualified name of the method.  
                            # this pattern would match everything in a module called sfdc
        use_cache: true
        enabled: true # Can turn off props with enabled.  Will allow for other props to potentially match 

Other devcache args

# devcache defaults to not take into account the args
@devcache(group='crm', key_args=('a', ))
def my_method(a, b, c):  # Will cache result using only arg 'a' value as part of the key 
    ...        

@devcache(group='crm', ignore_key_args=('c', ))
def another_method(a, b, c):  # Will cache result using arg 'a', 'b' value as part of the key ignoring 'c' 
    ...        

@devcache(config_file='../alternate.yaml')
def method3(a, b, c):  # specify another configuration 
    ...        

Important Warning

This project is only useful to speed up development and is a security risk.

Best practice would be to not include devcache in project requirements for production and only installing it locally.

Creating project specific decorator will allow for functionality to work in the desired env and not break the other.

For example:

def cacher(config_file=None, group=None, key_args=None, ignore_key_args=None):
    def noop_decorator(func):
        return func  # pass through

    try:
        from devcache import devcache
        return devcache(config_file=config_file, group=group, key_args=key_args, ignore_key_args=ignore_key_args)
    except:
        return noop_decorator

Using @cacher decorator would have use a pass through decorator for prod but use devcache where it's installed.