[openstack-dev] [oslo] memoizer aka cache

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


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.


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 ...
> 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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