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Re: HBase Performance Improvements?
I don't think there is.  You need to have a table seeded with the right
regions in order to run the bulk loader jobs.

My machines are sufficiently fast that it did not take that long to sort.
 One thing I did do to speed this up was add a mapper to the job that
generates the splits,  which would calculate the size of each KeyValue.  So
instead of passing around the KeyValue's I would pass around just the size
of the KeyValues.  You could do a similar thing with the Puts.  Here are my
keys/values for the job in full:

Mapper:

KeyIn: ImmutableBytesWritable
ValueIn: KeyValue

KeyOut: ImmutableBytesWritable
ValueOut: IntWritable

Reducer:

KeyIn: ImmutableBytesWritable
ValueIn: IntWritable

At this point I would just add up the ints from the IntWritable.  This cuts
down drastically on the amount of data passed around in the sort.

Hope this helps.  If it is still too slow you might have to experiment with
using many reducers and making sure you don't have holes or regions that
are too big due to the way the keys are partitioned.  I was lucky enough to
not have to go that far.
On Thu, May 10, 2012 at 11:55 AM, Something Something <
[EMAIL PROTECTED]> wrote:

> I am beginning to get a sinking feeling about this :(  But I won't give up!
>
> Problem is that when I use one Reducer the job runs for a long time.  I
> killed it after about an hour.  Keep in mind, we do have a decent cluster
> size.  The Map stage completes in a minute & when I set no. of reducers to
> 0 (which is not what we want) the job completes in 12 minutes.  In other
> words, sorting is taking very  very long!  What could be the problem?
>
> Is there no other way to do the bulk upload without first *learning* the
> data?
>
> On Thu, May 10, 2012 at 7:15 AM, Bryan Beaudreault <
> [EMAIL PROTECTED]
> > wrote:
>
> > Since our Key was ImmutableByteWritable (representing a rowKey) and the
> > Value was KeyValue, there could be many KeyValue's per row key (thus
> values
> > per hadoop key in the reducer).  So yes, what we did is very much the
> same
> > as what you described.  Hadoop will sort the ImutableByteWritable keys
> > before sending them to the reducer.  This is the primary sort.  We then
> > loop the values for each key, adding up the size of each KeyValue until
> we
> > reach the region size.  Each time that happens we record the rowKey from
> > the hadoop key and use that as the start key for a new region.
> >
> > Secondary sort is not necessary unless the order of the values matter for
> > you.  In this case (with the row key as the reducer key), I don't think
> > that matters.
> >
> > On Thu, May 10, 2012 at 3:22 AM, Something Something <
> > [EMAIL PROTECTED]> wrote:
> >
> > > Thank you Tim & Bryan for the responses.  Sorry for the delayed
> response.
> > > Got busy with other things.
> > >
> > > Bryan - I decided to focus on the region split problem first.  The
> > > challenge here is to find the correct start key for each region, right?
> > > Here are the steps I could think of:
> > >
> > > 1)  Sort the keys.
> > > 2)  Count how many keys & divide by # of regions we want to create.
> >  (e.g.
> > > 300).  This gives us # of keys in a region (region size).
> > > 3)  Loop thru the sorted keys & every time region size is reached,
> write
> > > down region # & starting key.  This info can later be used to create
> the
> > > table.
> > >
> > > Honestly, I am not sure what you mean by "hadoop does this
> > automatically".
> > > If you used a single reducer, did you use secondary sort
> > > (setOutputValueGroupingComparator) to sort the keys?  Did you loop thru
> > the
> > > *values* to find regions?  Would appreciate it if you would describe
> this
> > > MR job.  Thanks.
> > >
> > >
> > > On Wed, May 9, 2012 at 8:25 AM, Bryan Beaudreault
> > > <[EMAIL PROTECTED]>wrote:
> > >
> > > > I also recently had this problem, trying to index 6+ billion records
> > into
> > > > HBase.  The job would take about 4 hours before it brought down the