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From Leonidas Fegaras <fega...@cse.uta.edu>
Subject Re: [ANNOUNCEMENT] A query system for BSP processing
Date Fri, 24 Aug 2012 12:01:51 GMT
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.

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
> .

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