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From <fhue...@gmail.com>
Subject Re: Reading from HBase problem
Date Tue, 09 Jun 2015 09:53:09 GMT
Thank you very much!






From: Hilmi Yildirim
Sent: ‎Tuesday‎, ‎9‎. ‎June‎, ‎2015 ‎11‎:‎40
To: user@flink.apache.org




Done
https://issues.apache.org/jira/browse/FLINK-2188


Am 09.06.2015 um 11:26 schrieb Fabian Hueske:





Would you mind opening a JIRA for this issue? 

-> https://issues.apache.org/jira/browse/FLINK


I can do it as well, but you know all the details.

Thanks, Fabian



2015-06-09 11:03 GMT+02:00 Hilmi Yildirim <hilmi.yildirim@neofonie.de>:


I want to add that I run the Flink job on a cluster with 13 machines and each machine has
13 processing slots which results in a total number of processing slots of 169. 




Am 09.06.2015 um 10:59 schrieb Hilmi Yildirim:


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>:


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>:


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> 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

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