[openstack-dev] [oslo] memoizer aka cache

Stephen Gran stephen.gran at theguardian.com
Thu Jan 23 16:46:34 UTC 2014


Hi,

First, I think common routines are great.  More DRY is always good.

Second, my personal feeling is that when you see a hard-coded in-memory 
cache like this, it's probably something that should be moved to be 
behind a more generic caching framework that allows for different 
backends such as memcache for larger deployments.

If you're interested in something like that, I'm sure it would be useful.

Cheers,

On 23/01/14 16:07, Shawn Hartsock wrote:
> I would like to have us adopt a memoizing caching library of some kind
> for use with OpenStack projects. I have no strong preference at this
> time and I would like suggestions on what to use.
>
> I have seen a number of patches where people have begun to implement
> their own caches in dictionaries. This typically confuses the code and
> mixes issues of correctness and performance in code.
>
> Here's an example:
>
> We start with:
>
> def my_thing_method(some_args):
>      # do expensive work
>      return value
>
> ... but a performance problem is detected... maybe the method is
> called 15 times in 10 seconds but then not again for 5 minutes and the
> return value can only logically change every minute or two... so we
> end up with ...
>
> _GLOBAL_THING_CACHE = {}
>
> def my_thing_method(some_args):
>      key = key_from(some_args)
>       if key in _GLOBAL_THING_CACHE:
>           return _GLOBAL_THING_CACHE[key]
>       else:
>            # do expensive work
>            _GLOBAL_THING_CACHE[key] = value
>            return value
>
> ... which is all well and good... but now as a maintenance programmer
> I need to comprehend the cache mechanism, when cached values are
> invalidated, and if I need to debug the "do expensive work" part I
> need to tease out some test that prevents the cache from being hit.
> Plus I've introduced a new global variable. We love globals right?
>
> I would like us to be able to say:
>
> @memoize(seconds=10)
> def my_thing_method(some_args):
>      # do expensive work
>      return value
>
> ... where we're clearly addressing the performance issue by
> introducing a cache and limiting it's possible impact to 10 seconds
> which allows for the idea that "do expensive work" has network calls
> to systems that may change state outside of this Python process.
>
> I'd like to see this done because I would like to have a place to
> point developers to during reviews... to say: use "common/memoizer" or
> use "Bob's awesome memoizer" because Bob has worked out all the cache
> problems already and you can just use it instead of worrying about
> introducing new bugs by building your own cache.
>
> Does this make sense? I'd love to contribute something... but I wanted
> to understand why this state of affairs has persisted for a number of
> years... is there something I'm missing?
>


-- 
Stephen Gran
Senior Systems Integrator - theguardian.com
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