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From Micah Whitacre <>
Subject Re: Question about HBaseSourceTarget
Date Tue, 17 Mar 2015 16:26:56 GMT
Could we make an estimate based on # of regions * hbase.hregion.max.filesize?
 The case where this would overestimate would be if someone pre-split a
table upon creation.   Otherwise as the table fills up over time in theory
each region would grow and split evenly (and possibly hit max size and
therefore split again).

On Tue, Mar 17, 2015 at 11:20 AM, Josh Wills <> wrote:

> Also open to suggestion here-- this has annoyed me for some time (as
> Gabriel pointed out), but I don't have a good fix for it.
> On Tue, Mar 17, 2015 at 9:10 AM, Gabriel Reid <>
> wrote:
>> Hi Nithin,
>> This is a long-standing issue in Crunch (I think it's been present since
>> Crunch was originally open-sourced). I'd love to get this fixed somehow,
>> although it seems to not be trivial to do -- it can be difficult to
>> accurately estimate the size of data that will come from an HBase table,
>> especially considering that filters and selections of a subset of columns
>> can be done on an HBase table.
>> One short-term way of working around this is to add a simple identity
>> function directly after the HBaseSourceTarget that implements the
>> scaleFactor method to manipulate the calculated size of the HBase data, but
>> this is just another hack.
>> Maybe the better solution would be to estimate the size of the HBase
>> table based on its size on HDFS when using the HBaseFrom.table(String)
>> method, and then also overload the HBaseFrom.table(String, Scan) method to
>> also take a long value which is the estimated byte size (or perhaps scale
>> factor) of the table content that is expected to be returned from the given
>> Scan.
>> Any thoughts on either of these?
>> - Gabriel
>> On Tue, Mar 17, 2015 at 1:51 PM Nithin Asokan <>
>> wrote:
>>> Hello,
>>> I came across a unique behavior while using HBaseSourceTarget. Suppose I
>>> have a job(from MRPipeline) that reads from HBase using HBaseSourceTarget
>>> and passes all the data to a reduce phase, the number of reducers set by
>>> planner will be equal to 1. The reason being [1]. So, it looks like the
>>> planner assumes there is only about 1Gb of data that's read from the
>>> source, and sets the number of reducers accordingly. However, let's say
>>> my
>>> HBase scan is returning very less data or huge amounts of data. The
>>> planner
>>> still assigns 1 reducer(crunch.bytes.per.reduce.task=1Gb). What more
>>> interesting is, if there are dependent jobs, the planner will set the
>>> number of reducers based on the initially determined size from HBase
>>> source.
>>> As a fix for the above problem, I can set the number of reducers on the
>>> groupByKey(), but that does not offer much flexibility when dealing with
>>> data that is of varying sizes. The other option, is to have a map only
>>> job
>>> that reads from HBase and writes to HDFS and have a run(). The next job
>>> will determine the size right, since FileSourceImpl calculates the size
>>> on
>>> disk.
>>> I noticed the comment on HBaseSourceTarget, and was wondering if there
>>> was
>>> anything planned to have it implemented.
>>> [1]
>>> Thanks
>>> Nithin
> --
> Director of Data Science
> Cloudera <>
> Twitter: @josh_wills <>

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