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From "David Ezzio (asmtp)" <>
Subject Re: Comparison Metrics for OpenJPA's ConcurrentHashMap
Date Fri, 01 Jun 2007 13:25:26 GMT
Hi Marc,

I still was not able to get HighScaleLib to work. It produces a 
SecurityException when attempting to get the Unsafe object. I decided to 
avoid changing the relevant security policy.

On the other hand, I did test Emory University's backport of the 
java.util.concurrent package. This provides to Java 1.4 a 
ConcurrentHashMap implementation that is compatible with the one found 
in Java 5 and 6.

I also realized that I could get another metric from my numbers, the 
percentage of time the threads were suspended during the metered 
operations. Then I reran the tests for Emory's backport using fewer 
threads. In the classic pattern of an overloaded CPU, the higher thread 
count both lowers throughput and increases response time.  (Throughput 
is yet another number I haven't extracted from the data, although it is 
obvious when running the tests.)

All of the previous tests were run with 5 writing and 10 reading 
threads. Only backport was additionally tested with 2 writing and 4 
reading threads. The suspended percentage is approximate since the 
adding and updating tests have slightly different numbers.

Implementation  Add   Remove  Update  Find-a  Find-r  Find-u  Suspended
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
Backport (5/10) 43.2   36.8    40.4     0.3     0.2    0.5       62%
Backport (2/4)   6.1    3.1     3.3     0.3     0.2    0.3        4%


David Ezzio (asmtp) wrote:
> Hi Marc,
> I did plug it in, but it failed straightaway on a security issue.  I 
> should probably read its documentation. :)  I'll try it again along with 
> the backport lib done by Emory U.
> David
> Marc Prud'hommeaux wrote:
>> David-
>> That is very interesting.
>> Did you also take a look at the one at 
>> ? 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

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