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From "Edward J. Yoon" <edwardy...@apache.org>
Subject Re: [ANNOUNCEMENT] A query system for BSP processing
Date Wed, 05 Sep 2012 07:51:56 GMT
If you can open source this then I'm sure the ASF community can help
you and make this software better.

Pls feel free to ask us if you need any assistance donating source
code to the ASF or contributing to the Hama project in the future.

On Thu, Aug 30, 2012 at 11:40 PM, Leonidas Fegaras <fegaras@cse.uta.edu> wrote:
> Yes sure. I have fixed the bug with the repeat stopping condition but I have
> only tested pagerank on my small cluster. I still need to fix the k-means
> clustering (it's a special case because you improve a fixed number of
> points).
> Leonidas
>
>
> On Aug 30, 2012, at 9:02 AM, Edward J. Yoon wrote:
>
>> Shall we work together?
>>
>> On Fri, Aug 24, 2012 at 9:01 PM, Leonidas Fegaras <fegaras@cse.uta.edu>
>> wrote:
>>>
>>> Thank you very much for your interest and for testing my system.
>>> It seems that my release was premature: It worked for some random data
>>> but
>>> didn't for some others. It's a minor logical error that I will try to fix
>>> in
>>> the next few days. The problem is with the stopping condition of the
>>> repeat
>>> expression that calculates the new pagerank from the old. It must stop if
>>> ALL peers reach  the specified precision. This is done by having those
>>> peers
>>> that need to continue send a message to others to continue. It seems that
>>> now when all peers agree at the same time, the program works fine. But if
>>> one finishes sooner, instead of continuing the repeat loop, it runs away
>>> to
>>> the next BSP step that follows the repeat, then exits prematurely and the
>>> system hangs. The casting errors are due to the run-away peers executing
>>> the
>>> wrong BSP steps reading wrong messages. Queries without repeat though are
>>> OK.
>>> By the way, I had a problem exchanging large amount of data during sync
>>> (I
>>> discussed this with Thomas).  My solution was to to break a BSP superstep
>>> into multiple substeps so that each substep can handle a max number of
>>> messages. Of course my program has to collect all messages in a vector in
>>> memory. When the vector is too big, it is spilled in a local file. This
>>> moved the problem from the Hama side to my side and allowed me to handle
>>> larger data, especially in joins. I think this problem of exchanging
>>> large
>>> amount of data during a superstep is currently a weakness of Hama.
>>> Leonidas
>>>
>>>
>>>
>>> On 08/24/2012 04:15 AM, Thomas Jungblut wrote:
>>>>
>>>>
>>>> BTW, should we feature this on our website?
>>>>
>>>> 2012/8/24 Thomas Jungblut <thomas.jungblut@gmail.com>
>>>>
>>>>> Hi Leonidas!
>>>>>
>>>>> I have to admit that I have known what is going on (and had to keep
>>>>> silent), but I have to say: Thank you very much!
>>>>> This will help many people writing BSPs in a more easier way.
>>>>>
>>>>> Of course this is not as fast as the native BSP code, Hive and Pig
>>>>> suffer
>>>>> from the same problems in MR.
>>>>> But it gives people the opportunity to develop faster and get their
>>>>> code
>>>>> in production with just a minor time expense.
>>>>>
>>>>> And I think, that we will help you gladly on improving the BSP part of
>>>>> your framework. At least I would do ;)
>>>>>
>>>>> Thanks!
>>>>>
>>>>> 2012/8/24 Edward J. Yoon <edwardyoon@apache.org>
>>>>>
>>>>> Here's my few test results on Oracle BDA (40G/s infiniband network).
>>>>>>
>>>>>>
>>>>>> It seems slow than our PageRank example.
>>>>>>
>>>>>> P.S., There are some errors so I couldn't test large-scale.
>>>>>> (java.lang.ClassCastException: hadoop.mrql.MR_int cannot be cast
to
>>>>>> hadoop.mrql.Inv and java.lang.Error: Cannot clear a non-materialized
>>>>>> sequence ..., etc.)
>>>>>>
>>>>>>
>>>>>>
>>>>>> == 100K nodes and 1M edges ==
>>>>>>
>>>>>> *** Using 10 BSP tasks (out of a max 10). Each task will handle about
>>>>>> 2383611 bytes of input data.
>>>>>>
>>>>>> Run time: 30.384 secs
>>>>>>
>>>>>> *** Using 20 BSP tasks (out of a max 20). Each task will handle about
>>>>>> 1191805 bytes of input data.
>>>>>>
>>>>>> Run time: 24.412 secs
>>>>>>
>>>>>> On Fri, Aug 24, 2012 at 9:36 AM, Edward J. Yoon
>>>>>> <edwardyoon@apache.org>
>>>>>> wrote:
>>>>>>>
>>>>>>>
>>>>>>> Wow, very interesting. I'm going to install and test on my large
>>>>>>
>>>>>>
>>>>>> cluster.
>>>>>>>
>>>>>>>
>>>>>>> On Fri, Aug 24, 2012 at 4:41 AM, Leonidas Fegaras
>>>>>>> <fegaras@cse.uta.edu>
>>>>>>
>>>>>>
>>>>>> wrote:
>>>>>>>>
>>>>>>>>
>>>>>>>> Dear Hama users,
>>>>>>>> I am pleased to announce that the MRQL query processing system
can
>>>>>>>> now
>>>>>>>> evaluate SQL-like queries on a Hama cluster. MRQL is available
at:
>>>>>>>>
>>>>>>>> http://lambda.uta.edu/mrql/
>>>>>>>>
>>>>>>>> MRQL (the Map-Reduce Query Language) is an SQL-like query
language
>>>>>>>> for
>>>>>>>> large-scale, distributed data analysis. MRQL is powerful
enough to
>>>>>>>> express most common data analysis tasks over many different
kinds of
>>>>>>>> raw data, including hierarchical data and nested collections,
such
>>>>>>>> as
>>>>>>>> XML data. MRQL can run in two modes: in MR (Map-Reduce) mode
using
>>>>>>>> Apache Hadoop and in BSP (Bulk Synchronous Parallel) mode
using
>>>>>>>> Apache
>>>>>>>> Hama. Both modes use Apache's HDFS to read and write their
data.
>>>>>>>>
>>>>>>>> Note that, the BSP mode is currently experimental (not fine-tuned
>>>>>>>> yet)
>>>>>>>> and lacks any fault-tolerance (if an error occurs, the entire
job
>>>>>>>> must
>>>>>>>> be restarted). Due to our limited resources, MRQL has only
been
>>>>>>>> tested
>>>>>>>> on a small cluster (7-nodes/28-cores). We compared the BSP
mode with
>>>>>>>> the MR mode by evaluating a pagerank query over a small graph
(100K
>>>>>>>> nodes, 1M edges) and found that BSP mode is about 4.5 times
faster
>>>>>>>> than the MR mode. Please let me know if you'd like to contribute
to
>>>>>>>> this project by testing MRQL on a larger cluster.
>>>>>>>> Best regards,
>>>>>>>> Leonidas Fegaras
>>>>>>>> University of Texas at Arlington
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> Best Regards, Edward J. Yoon
>>>>>>> @eddieyoon
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> Best Regards, Edward J. Yoon
>>>>>> @eddieyoon
>>>>>>
>>>>>
>>>> .
>>>>
>>>
>>
>>
>>
>> --
>> Best Regards, Edward J. Yoon
>> @eddieyoon
>
>



-- 
Best Regards, Edward J. Yoon
@eddieyoon

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