Would this help for 0.99+? https://issues.apache.org/jira/browse/HBASE-10413
On Tue, Mar 17, 2015 at 12:35 PM, Gabriel Reid <[email protected]> wrote: > That sounds like it would work pretty well, although the situation where a > custom Scan is used is still problematic. > > I think Hannibal [1] does some clever stuff as far as figuring out data > size as well (I think just via HBase RPC and not by looking at HDFS), there > could be some useful ideas in there. > > - Gabriel > > 1. https://github.com/sentric/hannibal > > > On Tue, Mar 17, 2015 at 5:27 PM Micah Whitacre <[email protected]> > wrote: > >> 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 <[email protected]> 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 <[email protected]> >>> 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 <[email protected]> >>>> 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] >>>>> >>>>> https://github.com/apache/crunch/blob/apache-crunch-0.8.4/crunch-hbase/src/main/java/org/apache/crunch/io/hbase/HBaseSourceTarget.java#L173 >>>>> >>>>> Thanks >>>>> Nithin >>>>> >>>> >>> >>> >>> -- >>> Director of Data Science >>> Cloudera <http://www.cloudera.com> >>> Twitter: @josh_wills <http://twitter.com/josh_wills> >>> >> >> -- Director of Data Science Cloudera <http://www.cloudera.com> Twitter: @josh_wills <http://twitter.com/josh_wills>
