openjpa-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Marc Prud'hommeaux <mprud...@apache.org>
Subject Re: Comparison Metrics for OpenJPA's ConcurrentHashMap
Date Tue, 29 May 2007 19:27:24 GMT
David-

That is very interesting.

Did you also take a look at the one at http://sourceforge.net/ 
projects/high-scale-lib ? They say its performance only shines for  
high thread/cpu counts, but it might be interesting to see where its  
numbers lie in the range.



On May 29, 2007, at 11:01 AM, David Ezzio (asmtp) wrote:

> Recently, I did some testing of Map implementations under concurrency.
>
> My primary purpose was to verify the reliability of OpenJPA's  
> ConcurrentHashMap implementation. As I got into it, I saw the  
> opportunity to get some performance metrics out of the test.
>
> The biggest part of my task was coming up with a reliable and  
> useful testing framework. I design it with the following two  
> factors in mind: First, I wanted to test the edge conditions where  
> an entry had just been added or removed or where a key's value had  
> just been updated. The idea is that a number of threads add,  
> remove, and update entries, while other threads check to see if  
> these recent modifications are visible (or in the case of removals,  
> not visible). Second, I wanted the testing framework itself to be  
> free of synchronization. If the testing framework used  
> synchronization then it would tend to serialize the readers and  
> writers and thereby mask concurrency issues in the map  
> implementation under test.
>
> The testing framework uses a non-synchronizing, non-blocking FIFO  
> queue as the mechanism for the writing threads to communicate their  
> recent modifications to the reading threads.
>
> To prevent writing threads from overwriting recent modifications  
> before they could be read and verified, the testing framework walks  
> the hash map keys in in a linear (or in the case of updates,  
> circular) order. By using a hash map with a large enough capacity,  
> readers have the time to verify the recent modifications before the  
> writer threads come back to modify that part of the key space again.
>
> Using an adapter for the map implementation, the testing framework  
> starts five writer threads and ten reader threads at the same time.  
> These threads run wide open for 30 seconds, except that the readers  
> will give up their time slice if they find nothing on the queue.  
> The HashMaps were all sized for the needed capacity upon creation,  
> so no resizing occurred during testing.
>
> I got some interesting results.
>
> Four implementations were tested, Java's unsynchronized HashMap  
> implementation, Java's synchronized HashMap implementation, Java's  
> ConcurrentHashMap implementation, and OpenJPA's ConcurrentHashMap  
> implementation.
>
> Only Java's unsynchronized HashMap failed, as expected, under test.  
> Under test, this implementation demonstrates its inability to  
> handle concurrency. The other three implementations worked  
> flawlessly under test.
>
> The java.util.concurrent.ConcurrentHashMap implementation  
> (available with Java 5 and 6) was the fastest implementation tested.
>
> Java's synchronized wrapper for the HashMap implementation is one  
> to two orders of magnitude slower than Java's ConcurrentHashMap  
> implementation.
>
> OpenJPA's ConcurrentHashMap compares equally with Java's  
> ConcurrentHashMap in find operations and is 2-4 times slower in  
> mutating operations.
>
> Implementation   Add   Remove   Update  Find-a  Find-r  Find-u
> ---------------+------+-------+--------+-------+-------+------
> synchronized     103     35       50      40      37     54
> concurrent      13.2    6.4      6.1     0.6     0.3    1.1
> OpenJPA         29.8   26.6     27.9     0.6     0.6    0.6
>
>
> Legend:
>
> synchronized:
> java.util.Collections.synchronizedMap(new java.util.HashMap())
> concurrent: java.util.concurrent.ConcurrentHashMap
> OpenJPA: org.apache.openjpa.lib.util.concurrent.ConcurrentHashMap
>
> Add: time for average add operation
> Remove: time for average remove operation
> Update: time for average update of new value for existing key
> Find-a: time to find a recent addition
> Find-r: time to NOT find a recent removal
> Find-u: time to find a recent update
>
> These times (in microseconds) are representative, but are not the  
> average of several runs. The tests were run on a Dell Dual Core  
> laptop under Windows. The performance meter was pegged during the  
> tests.
>
> David Ezzio
>
>


Mime
View raw message