Re: Reading from HBase problem

Hilmi Yildirim Tue, 09 Jun 2015 02:05:06 -0700

I want to add that I run the Flink job on a cluster with 13 machines andeach machine has 13 processing slots which results in a total number ofprocessing 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 samevalue 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 ifthat 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.yildi...@neofonie.de<mailto:hilmi.yildi...@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 <rmetz...@apache.org
    <mailto:rmetz...@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.yildi...@neofonie.de
        <mailto:hilmi.yildi...@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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Re: Reading from HBase problem

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