Forget about sleep, there's work to be done! I realized that the drawback I
mentioned with my solution, where concurrent calls to the memoized function
with different sets of arguments would be handled one-by-one, could easily
be resolved by having the cache inside synced-memoize store futures for
calculating the value, rather than the value itself - some small changes
and everything should hopefully work well (don't ask me about overhead,
though!)
(defn synced-memoize [f]
(let [mem (atom {})
handle-ch (chan)]
(go-loop []
(let [[args ret-ch] (<! handle-ch)]
(>! ret-ch (if-let [e (find @mem args)]
(val e)
(let [ret (future (apply f args))]
(swap! mem assoc args ret)
ret)))
(recur)))
(fn [& args]
(if-let [e (find @mem args)]
(deref (val e))
(let [ret (chan)]
(>!! handle-ch [args ret])
(deref (<!! ret)))))))
(def lookup
(let [calc-value* (synced-memoize calc-value)]
(fn [cache key]
(if (contains? @cache key)
(@cache key)
((swap! cache assoc key (calc-value* key)) key)))))
Den måndagen den 1:e september 2014 kl. 23:55:58 UTC+2 skrev Marcus
Magnusson:
>
> Huh, I gave it some more thought - of course, the reason why memoize won't
> help us here is that there is no synchronization between simultaneous
> invocations with the same set of arguments. This is typically not a problem
> if the underlying function is fast, but in our case it would be neat with
> an alternative. I got the idea of writing a channel-based version of
> memoize, which will ensure that invoking the memoized function
> simultaneously with the same arguments will only call the underlying
> function once. Below is a quick implementation with one obvious drawback
> (and probably tons of bugs and points of improvement!), namely that if the
> underlying function is costly, and we invoke the memoized function with a
> bunch of different non-cached set of arguments, then calculating the values
> will be done one-by-one. This could be fixed for example by having
> handle-ch delegate to another channel, where we have one channel per set of
> arguments - I'll leave that for someone else, or for when I've had some
> sleep and realized what a bad idea it was... :) Note that I haven't worked
> much with core.async, and that there is probably a much more
> straightforward solution (for example the one given by Thomas Heller), so
> please let me know of any issues with this:
>
>
> (defn synced-memoize [f]
> (let [mem (atom {})
> handle-ch (chan)]
> (go-loop []
> (let [[args ret-ch] (<! handle-ch)]
> (>! ret-ch (if-let [e (find @mem args)]
> (val e)
> (let [ret (apply f args)]
> (swap! mem assoc args ret)
> ret)))
> (recur)))
> (fn [& args]
> (if-let [e (find @mem args)]
> (val e)
> (let [ret (chan)]
> (>!! handle-ch [args ret])
> (<!! ret))))))
>
> (def lookup
> (let [calc-value* (synced-memoize calc-value)]
> (fn [cache key]
> (if (contains? @cache key)
> (@cache key)
> (swap! cache assoc key (calc-value* key))))))
>
>
>
> Den måndagen den 1:e september 2014 kl. 22:35:56 UTC+2 skrev Marcus
> Magnusson:
>>
>> I reckon if that worked, there would be no need for memoize anyway, but I
>> don't think swap! will allow for it. I'm far from an expert on swap! or
>> atoms, but several swap!s may be run simultaneously on a single atom (and
>> swap! may re-run the swapping function if the atom has been changed since
>> the swapping function was invoked). In other words, if two swap!s are
>> invoked at about the same time for the same key (which is not currently in
>> the cache), both invocations may be given the same value of the cache at
>> that moment, which will lead to the value being re-calculated in both
>> invocations - and even if calc-value is memoized, the same thing may occur
>> in the memoized function.
>>
>> As I said, I'm far from an expert, so I wrote a small test that shows
>> that calc-value may indeed be called more than once (core.async here is
>> simply to ensure that each invocation of calc-value is printed on its own
>> line):
>>
>>
>> (def out-ch (chan 100))
>>
>> (go-loop []
>> (println (<! out-ch))
>> (recur))
>>
>> (defn calc-value [x]
>> (put! out-ch x)
>> (* x x))
>>
>> (def cache (atom {}))
>>
>> (def lookup
>> (let [calc-value* (memoize calc-value)]
>> (fn [cache key]
>> ((swap! cache (fn [cache']
>> (if (contains? cache' key)
>> cache'
>> (assoc cache' key (calc-value* key)))))
>> key))))
>>
>>
>> => (dotimes [_ 1000]
>> (future (lookup cache (rand-int 20))))
>> 11
>> 12
>> 16
>> 1
>> nil
>> 6
>> 15
>> 17
>> 18
>> 14
>> 2
>> 5
>> 19
>> 10
>> 0
>> 7
>> *9*
>> *4*
>> *4*
>> *13*
>> *9*
>> *13*
>>
>>
>> Den måndagen den 1:e september 2014 kl. 21:32:01 UTC+2 skrev Alex
>> Baranosky:
>>>
>>> I believe you can replace:
>>> (when-not (contains? @cache k)
>>> (swap! cache assoc k (calc-value* k))))
>>>
>>> with:
>>> (swap! cache (fn [cache']
>>> (if (contains? cache' k)
>>> cache'
>>> (assoc cache' k (calc-value* k)))))
>>>
>>> Note: I haven't run this code
>>>
>>>
>>> On Mon, Sep 1, 2014 at 2:20 AM, Colin Fleming <colin.ma...@gmail.com>
>>> wrote:
>>>
>>>> Hi Thomas,
>>>>
>>>> Normally I'd agree with you, but in my case it actually works quite
>>>> well since I don't need to expire or worry about sizing. This is for
>>>> caching objects on IntelliJ parse tree elements, and the element with its
>>>> cached values is thrown away every time the document is parsed, so these
>>>> are pretty transient caches. In this particular case the calculation isn't
>>>> too heavyweight either so recalculating it in the unlikely event of
>>>> contention isn't a big deal. In fact, this discussion and the related
>>>> thinking has made me realise that my original solution was actually
>>>> sufficient for my somewhat strange use case, which is nice :-). For more
>>>> typical server side caching, I agree that Guava would be a great solution.
>>>>
>>>> Cheers,
>>>> Colin
>>>>
>>>>
>>>> On 1 September 2014 20:54, Thomas Heller <th.h...@gmail.com> wrote:
>>>>
>>>>> As much as I like Clojure and atoms, I do not think they are a good
>>>>> fit for caching. Not only is it impossible to address the concurrency
>>>>> issues related to multiple threads loading the same object, but you also
>>>>> have to do expiration and size management yourself. Immutability doesn't
>>>>> help much for caching either. There is core.cache that does some bits but
>>>>> I
>>>>> probably would recommend using something like CacheBuilder from the guava
>>>>> libs:
>>>>>
>>>>> See
>>>>> https://code.google.com/p/guava-libraries/
>>>>>
>>>>> http://docs.guava-libraries.googlecode.com/git/javadoc/com/google/common/cache/CacheBuilder.html
>>>>>
>>>>> Its Java but the Clojure<->Java interop is so good that it doesn't
>>>>> matter much.
>>>>>
>>>>> On Saturday, August 30, 2014 7:27:05 AM UTC+2, Colin Fleming wrote:
>>>>>>
>>>>>> Hi all,
>>>>>>
>>>>>> I want to use a map to cache values based on a key. I'm planning to
>>>>>> use an atom for this. My basic operation is "give me the value for this
>>>>>> key" - if the value exists in the map then that value should be
>>>>>> returned,
>>>>>> otherwise a new value should be calculated, inserted in the map and then
>>>>>> returned. My plan is to implement something like the following:
>>>>>>
>>>>>>
>>>>>> (defn ensure [cache key] (if (contains? cache key) cache (assoc
>>>>>> cache key (calc-value key))))(let [value (get (swap! cache ensure key)
>>>>>> key)] ... do my thing with value ...)
>>>>>>
>>>>>>
>>>>>> So 'ensure' ensures that the cache contains the value for key, the
>>>>>> swap! operation returns the cache with the value and then I get it out.
>>>>>> This works but feels a little clumsy, is there a better way to do this?
>>>>>>
>>>>>> Also, looking at the Atom source code, I see that this will cause a
>>>>>> CAS operation even if the value returned from swap! is identical to the
>>>>>> original value. It seems like a reasonable optimisation would be to
>>>>>> check
>>>>>> if the values are identical and not update if so - is there a reason
>>>>>> this
>>>>>> might not be a good idea?
>>>>>>
>>>>>> Thanks,
>>>>>> Colin
>>>>>>
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