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From Hilmi Yildirim <hilmi.yildi...@neofonie.de>
Subject Re: Reading from HBase problem
Date Tue, 09 Jun 2015 08:59:05 GMT
Correct.

I also counted the rows with Spark and Hive. Both returned the same 
value which is nearly 100 mio. rows. But Flink returns 102 mio. rows.

Best Regards,
Hilmi

Am 09.06.2015 um 10:47 schrieb Fabian Hueske:
> OK, so the problem seems to be with the HBase InputFormat.
>
> I guess this issue needs a bit of debugging.
> We need to check if records are emitted twice (or more often) and if 
> that is the case which records.
> Unfortunately, this issue only seems to occur with large tables :-(
>
> Did I got that right, that the HBase format returns about 2M (~2%) 
> more records than are contained in the HBase table?
>
> Cheers, Fabian
>
> 2015-06-09 10:34 GMT+02:00 Hilmi Yildirim <hilmi.yildirim@neofonie.de 
> <mailto:hilmi.yildirim@neofonie.de>>:
>
>     Hi,
>     Now I tested the "count" method. It returns the same result as the
>     flatmap.groupBy(0).sum(1) method.
>
>     Furthermore, the Hbase contains nearly 100 mio. rows but the
>     result is 102 mio.. This means that the HbaseInput reads more rows
>     than the HBase contains.
>
>     Best Regards,
>     Hilmi
>
>
>     Am 08.06.2015 um 23:29 schrieb Fabian Hueske:
>>     Hi Hilmi,
>>
>>     I see two possible reasons:
>>
>>     1) The data source / InputFormat is not properly working, so not
>>     all HBase records are read/forwarded, or
>>     2) The aggregation / count is buggy
>>
>>     Roberts suggestion will use an alternative mechanism to do the
>>     count. In fact, you can count with groupBy(0).sum() and
>>     accumulators at the same time.
>>     If both counts are the same, this will indicate that the
>>     aggregation is correct and hint that the HBase format is faulty.
>>
>>     In any case, it would be very good to know your findings. Please
>>     keep us updated.
>>
>>     One more hint, if you want to do a full aggregate, you don't have
>>     to use a "dummy" key like "a". Instead, you can work with
>>     Tuple1<Long> and directly call sum(0) without doing the groupBy().
>>
>>     Best, Fabian
>>
>>     2015-06-08 17:36 GMT+02:00 Robert Metzger <rmetzger@apache.org
>>     <mailto:rmetzger@apache.org>>:
>>
>>         Hi Hilmi,
>>
>>         if you just want to count the number of elements, you can
>>         also use accumulators, as described here [1].
>>         They are much more lightweight.
>>
>>         So you need to make your flatMap function a
>>         RichFlatMapFunction, then call getExecutionContext().
>>         Use a long accumulator to count the elements.
>>
>>         If the results with the accumulator are consistent (the exact
>>         element count), then there is a severe bug in Flink. But I
>>         suspect that the accumulator will give you the same result
>>         (off by +-5)
>>
>>         Best,
>>         Robert
>>
>>
>>         [1]: http://slideshare.net/robertmetzger1/apache-flink-hands-on
>>
>>         On Mon, Jun 8, 2015 at 3:04 PM, Hilmi Yildirim
>>         <hilmi.yildirim@neofonie.de
>>         <mailto:hilmi.yildirim@neofonie.de>> wrote:
>>
>>             Hi,
>>             I implemented a simple Flink Batch job which reads from
>>             an HBase Cluster of 13 machines and with nearly 100
>>             million rows. The hbase version is 1.0.0-cdh5.4.1. So, I
>>             imported hbase-client 1.0.0-cdh5.4.1.
>>             I implemented a flatmap which creates a tuple ("a", 1L)
>>             for each row . Then, I use groupBy(0).sum(1).writeAsTest.
>>             The result should be the number of rows. But, the result
>>             is not correct. I run the job multiple times and the
>>             result flactuates by +-5. I also run the job for a
>>             smaller table with 100.000 rows and the result is correct.
>>
>>             Does anyone know the reason for that?
>>
>>             Best Regards,
>>             Hilmi
>>
>>             -- 
>>             --
>>             Hilmi Yildirim
>>             Software Developer R&D
>>
>>             http://www.neofonie.de
>>
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>>
>

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