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From Alex Baranau <alex.barano...@gmail.com>
Subject Re: [ANN]: HBaseWD: Distribute Sequential Writes in HBase
Date Wed, 11 May 2011 20:01:03 GMT
> Can you remove the first version ?
Isn't it ok to keep it in JIRA issue?

> In HBaseWD, can you use reflection to detect whether Scan supports
setAttribute() ?
> If it does, can you encode start row and end row as "sourceScan" attribute
?

Yeah, smth like this is going to be implemented. Though I'd still want to
hear from the devs the story about Scan version.

> One consideration is that start row or end row may be quite long.

Yeah, that is was my though too at first. Though it might be ok, since we
anyways "transfer" start/stop rows with Scan object.

> What do you think ?

I'd love to hear from you is this variant I mentioned is what we are looking
at here:

> From what I understand, you want to distinguish scans fired by the same
distributed scan.
> I.e. group scans which were fired by single distributed scan. If that's
what you want, distributed
> scan can generate unique ID and set, say "sourceScan" attribute to its
value. This way we'll
> have <# of distinct "sourceScan" attribute values> = <number of
distributed scans invoked by
> client side> and two scans on server side will have the same "sourceScan"
attribute iff they
> "belong" to same distributed scan.


Alex Baranau
----
Sematext :: http://sematext.com/ :: Solr - Lucene - Nutch - Hadoop - HBase

On Wed, May 11, 2011 at 5:15 PM, Ted Yu <yuzhihong@gmail.com> wrote:

> Alex:
> Your second patch looks good.
> Can you remove the first version ?
>
> In HBaseWD, can you use reflection to detect whether Scan supports
> setAttribute() ?
> If it does, can you encode start row and end row as "sourceScan" attribute
> ?
>
> One consideration is that start row or end row may be quite long.
> Ideally we should store hash code of source Scan object as "sourceScan"
> attribute. But Scan doesn't implement hashCode(). We can add it, that would
> require running all Scan related tests.
>
> What do you think ?
>
> Thanks
>
>
> On Tue, May 10, 2011 at 5:46 AM, Alex Baranau <alex.baranov.v@gmail.com>wrote:
>
>> Sorry for the delay in response (public holidays here).
>>
>> This depends on what info you are looking for on server side.
>>
>> From what I understand, you want to distinguish scans fired by the same
>> distributed scan. I.e. group scans which were fired by single distributed
>> scan. If that's what you want, distributed scan can generate unique ID and
>> set, say "sourceScan" attribute to its value. This way we'll have <# of
>> distinct "sourceScan" attribute values> = <number of distributed scans
>> invoked by client side> and two scans on server side will have the same
>> "sourceScan" attribute iff they "belong" to same distributed scan.
>>
>> Is this what are you looking for?
>>
>> Alex Baranau
>>
>> P.S. attached patch for HBASE-3811<https://issues.apache.org/jira/browse/HBASE-3811>
>> .
>> P.S-2. should this conversation be moved to dev list?
>>
>> ----
>> Sematext :: http://sematext.com/ :: Solr - Lucene - Nutch - Hadoop -
>> HBase
>>
>> On Fri, May 6, 2011 at 12:06 AM, Ted Yu <yuzhihong@gmail.com> wrote:
>>
>>> Alex:
>>> What type of identification should we put in the map of the Scan object ?
>>> I am thinking of using the Id of RowKeyDistributor. But the user can use
>>> same distributor on multiple scans.
>>>
>>> Please share your thought.
>>>
>>>
>>> On Thu, Apr 21, 2011 at 8:32 AM, Alex Baranau <alex.baranov.v@gmail.com>wrote:
>>>
>>>> https://issues.apache.org/jira/browse/HBASE-3811
>>>>
>>>> Alex Baranau
>>>> ----
>>>> Sematext :: http://sematext.com/ :: Solr - Lucene - Nutch - Hadoop -
>>>> HBase
>>>>
>>>> On Thu, Apr 21, 2011 at 5:57 PM, Ted Yu <yuzhihong@gmail.com> wrote:
>>>>
>>>> > My plan was to make regions that have active scanners more stable -
>>>> trying
>>>> > not to move them when balancing.
>>>> > I prefer second approach - adding custom attribute(s) to Scan so that
>>>> the
>>>> > Scans created by the method below can be 'grouped'.
>>>> >
>>>> > If you can file a JIRA, that would be great.
>>>> >
>>>> > On Thu, Apr 21, 2011 at 7:23 AM, Alex Baranau <
>>>> alex.baranov.v@gmail.com
>>>> > >wrote:
>>>> >
>>>> > > Aha, so you want to "count" it as single scan (or just differently)
>>>> when
>>>> > > determining the load?
>>>> > >
>>>> > > The current code looks like this:
>>>> > >
>>>> > > class DistributedScanner:
>>>> > >  public static DistributedScanner create(HTable hTable, Scan
>>>> original,
>>>> > > AbstractRowKeyDistributor keyDistributor) throws IOException {
>>>> > >    byte[][] startKeys =
>>>> > > keyDistributor.getAllDistributedKeys(original.getStartRow());
>>>> > >    byte[][] stopKeys =
>>>> > > keyDistributor.getAllDistributedKeys(original.getStopRow());
>>>> > >    Scan[] scans = new Scan[startKeys.length];
>>>> > >    for (byte i = 0; i < startKeys.length; i++) {
>>>> > >      scans[i] = new Scan(original);
>>>> > >      scans[i].setStartRow(startKeys[i]);
>>>> > >      scans[i].setStopRow(stopKeys[i]);
>>>> > >    }
>>>> > >
>>>> > >    ResultScanner[] rss = new ResultScanner[startKeys.length];
>>>> > >    for (byte i = 0; i < scans.length; i++) {
>>>> > >      rss[i] = hTable.getScanner(scans[i]);
>>>> > >    }
>>>> > >
>>>> > >    return new DistributedScanner(rss);
>>>> > >  }
>>>> > >
>>>> > > This is client code. To make these scans "identifiable" we need
to
>>>> either
>>>> > > use some different (derived from Scan) class or add some attribute
>>>> to
>>>> > them.
>>>> > > There's no API for doing the latter. But we can do the former,
but I
>>>> > don't
>>>> > > really like the idea of creating extra class (with no extra
>>>> > functionality)
>>>> > > just to distinguish it from the base one.
>>>> > >
>>>> > > If you can share why/how do you want to treat them differently
on
>>>> server
>>>> > > side, that would be helpful.
>>>> > >
>>>> > > Alex Baranau
>>>> > > ----
>>>> > > Sematext :: http://sematext.com/ :: Solr - Lucene - Nutch - Hadoop
>>>> -
>>>> > HBase
>>>> > >
>>>> > > On Thu, Apr 21, 2011 at 4:58 PM, Ted Yu <yuzhihong@gmail.com>
>>>> wrote:
>>>> > >
>>>> > > > My request would be to make the distributed scan identifiable
from
>>>> > server
>>>> > > > side.
>>>> > > > :-)
>>>> > > >
>>>> > > > On Thu, Apr 21, 2011 at 5:45 AM, Alex Baranau <
>>>> > alex.baranov.v@gmail.com
>>>> > > > >wrote:
>>>> > > >
>>>> > > > > > Basically bucketsCount may not equal number of regions
for the
>>>> > > > underlying
>>>> > > > > > table.
>>>> > > > >
>>>> > > > > True: e.g. when there's only one region that holds data
for the
>>>> whole
>>>> > > > table
>>>> > > > > (not many records in table yet), distributed scan will
fire N
>>>> scans
>>>> > > > against
>>>> > > > > the same region.
>>>> > > > > On the other hand, in case there are huge number of regions
for
>>>> > single
>>>> > > > > table, each scan can span over multiple regions.
>>>> > > > >
>>>> > > > > > I need to deal with normal scan and "distributed
scan" at
>>>> server
>>>> > > side.
>>>> > > > >
>>>> > > > > With current implementation "distributed" scan won't
be
>>>> recognized as
>>>> > > > > something special on the server side. It will be an ordinary
>>>> scan.
>>>> > > Though
>>>> > > > > the number of scan will increase, given that the typical
>>>> situation is
>>>> > > > "many
>>>> > > > > regions for single table", the scans of the same "distributed
>>>> scan"
>>>> > are
>>>> > > > > likely not to hit the same region.
>>>> > > > >
>>>> > > > > Not sure if I answered your questions here. Feel free
to ask
>>>> more ;)
>>>> > > > >
>>>> > > > > Alex Baranau
>>>> > > > > ----
>>>> > > > > Sematext :: http://sematext.com/ :: Solr - Lucene - Nutch
-
>>>> Hadoop -
>>>> > > > HBase
>>>> > > > >
>>>> > > > > On Wed, Apr 20, 2011 at 2:10 PM, Ted Yu <yuzhihong@gmail.com>
>>>> wrote:
>>>> > > > >
>>>> > > > > > Alex:
>>>> > > > > > If you read this, you would know why I asked:
>>>> > > > > > https://issues.apache.org/jira/browse/HBASE-3679
>>>> > > > > >
>>>> > > > > > I need to deal with normal scan and "distributed
scan" at
>>>> server
>>>> > > side.
>>>> > > > > > Basically bucketsCount may not equal number of regions
for the
>>>> > > > underlying
>>>> > > > > > table.
>>>> > > > > >
>>>> > > > > > Cheers
>>>> > > > > >
>>>> > > > > > On Tue, Apr 19, 2011 at 11:11 PM, Alex Baranau <
>>>> > > > alex.baranov.v@gmail.com
>>>> > > > > > >wrote:
>>>> > > > > >
>>>> > > > > > > Hi Ted,
>>>> > > > > > >
>>>> > > > > > > We currently use this tool in the scenario
where data is
>>>> consumed
>>>> > > by
>>>> > > > > > > MapReduce jobs, so we haven't tested the performance
of pure
>>>> > > > > "distributed
>>>> > > > > > > scan" (i.e. N scans instead of 1) a lot. I
expect it to be
>>>> close
>>>> > to
>>>> > > > > > simple
>>>> > > > > > > scan performance, or may be sometimes even
faster depending
>>>> on
>>>> > your
>>>> > > > > data
>>>> > > > > > > access patterns. E.g. in case you write timeseries
data
>>>> > > (sequential)
>>>> > > > > > which
>>>> > > > > > > is written into the single region at a time,
then e.g. if
>>>> you
>>>> > > access
>>>> > > > > > delta
>>>> > > > > > > for further processing/analysis (esp. if from
not single
>>>> client)
>>>> > > > these
>>>> > > > > > > scans
>>>> > > > > > > are likely to hit the same region or couple
of regions at a
>>>> time,
>>>> > > > which
>>>> > > > > > may
>>>> > > > > > > perform worse comparing to many scans hitting
data that is
>>>> much
>>>> > > > better
>>>> > > > > > > spread over region servers.
>>>> > > > > > >
>>>> > > > > > > As for map-reduce job the approach should not
affect reading
>>>> > > > > performance
>>>> > > > > > at
>>>> > > > > > > all: it's just that there are bucketsCount
times more splits
>>>> and
>>>> > > > hence
>>>> > > > > > > bucketsCount times more Map tasks. In many
cases this even
>>>> > improves
>>>> > > > > > overall
>>>> > > > > > > performance of the MR job since work is better
distributed
>>>> over
>>>> > > > cluster
>>>> > > > > > > (esp. in situation when the aim is to constantly
process the
>>>> > coming
>>>> > > > > delta
>>>> > > > > > > which usually resides in one or just couple
of regions
>>>> depending
>>>> > on
>>>> > > > > > > processing frequency).
>>>> > > > > > >
>>>> > > > > > > If you can share details on your case, that
will help to
>>>> > understand
>>>> > > > > what
>>>> > > > > > > effect(s) to expect from using this approach.
>>>> > > > > > >
>>>> > > > > > > Alex Baranau
>>>> > > > > > > ----
>>>> > > > > > > Sematext :: http://sematext.com/ :: Solr -
Lucene - Nutch -
>>>> > Hadoop
>>>> > > -
>>>> > > > > > HBase
>>>> > > > > > >
>>>> > > > > > > On Wed, Apr 20, 2011 at 8:17 AM, Ted Yu <
>>>> yuzhihong@gmail.com>
>>>> > > wrote:
>>>> > > > > > >
>>>> > > > > > > > Interesting project, Alex.
>>>> > > > > > > > Since there're bucketsCount scanners compared
to one
>>>> scanner
>>>> > > > > > originally,
>>>> > > > > > > > have you performed load testing to see
the impact ?
>>>> > > > > > > >
>>>> > > > > > > > Thanks
>>>> > > > > > > >
>>>> > > > > > > > On Tue, Apr 19, 2011 at 10:25 AM, Alex
Baranau <
>>>> > > > > > alex.baranov.v@gmail.com
>>>> > > > > > > > >wrote:
>>>> > > > > > > >
>>>> > > > > > > > > Hello guys,
>>>> > > > > > > > >
>>>> > > > > > > > > I'd like to introduce a new small
java project/lib
>>>> around
>>>> > > HBase:
>>>> > > > > > > HBaseWD.
>>>> > > > > > > > > It
>>>> > > > > > > > > is aimed to help with distribution
of the load (across
>>>> > > > > regionservers)
>>>> > > > > > > > when
>>>> > > > > > > > > writing sequential (becasue of the
row key nature)
>>>> records.
>>>> > It
>>>> > > > > > > implements
>>>> > > > > > > > > the solution which was discussed
several times on this
>>>> > mailing
>>>> > > > list
>>>> > > > > > > (e.g.
>>>> > > > > > > > > here: http://search-hadoop.com/m/gNRA82No5Wk).
>>>> > > > > > > > >
>>>> > > > > > > > > Please find the sources at
>>>> > > > > > https://github.com/sematext/HBaseWD(there's
>>>> > > > > > > > > also
>>>> > > > > > > > > a jar of current version for convenience).
It is very
>>>> easy to
>>>> > > > make
>>>> > > > > > use
>>>> > > > > > > of
>>>> > > > > > > > > it: e.g. I added it to one existing
project with 1+2
>>>> lines of
>>>> > > > code
>>>> > > > > > (one
>>>> > > > > > > > > where I write to HBase and 2 for
configuring MapReduce
>>>> job).
>>>> > > > > > > > >
>>>> > > > > > > > > Any feedback is highly appreciated!
>>>> > > > > > > > >
>>>> > > > > > > > > Please find below the short intro
to the lib [1].
>>>> > > > > > > > >
>>>> > > > > > > > > Alex Baranau
>>>> > > > > > > > > ----
>>>> > > > > > > > > Sematext :: http://sematext.com/
:: Solr - Lucene -
>>>> Nutch -
>>>> > > > Hadoop
>>>> > > > > -
>>>> > > > > > > > HBase
>>>> > > > > > > > >
>>>> > > > > > > > > [1]
>>>> > > > > > > > >
>>>> > > > > > > > > Description:
>>>> > > > > > > > > ------------
>>>> > > > > > > > > HBaseWD stands for Distributing (sequential)
Writes. It
>>>> was
>>>> > > > > inspired
>>>> > > > > > by
>>>> > > > > > > > > discussions on HBase mailing lists
around the problem of
>>>> > > choosing
>>>> > > > > > > > between:
>>>> > > > > > > > > * writing records with sequential
row keys (e.g.
>>>> time-series
>>>> > > data
>>>> > > > > > with
>>>> > > > > > > > row
>>>> > > > > > > > > key
>>>> > > > > > > > >  built based on ts)
>>>> > > > > > > > > * using random unique IDs for records
>>>> > > > > > > > >
>>>> > > > > > > > > First approach makes possible to
perform fast range
>>>> scans
>>>> > with
>>>> > > > help
>>>> > > > > > of
>>>> > > > > > > > > setting
>>>> > > > > > > > > start/stop keys on Scanner, but creates
single region
>>>> server
>>>> > > > > > > hot-spotting
>>>> > > > > > > > > problem upon writing data (as row
keys go in sequence
>>>> all
>>>> > > records
>>>> > > > > end
>>>> > > > > > > up
>>>> > > > > > > > > written into a single region at a
time).
>>>> > > > > > > > >
>>>> > > > > > > > > Second approach aims for fastest
writing performance by
>>>> > > > > distributing
>>>> > > > > > > new
>>>> > > > > > > > > records over random regions but makes
not possible doing
>>>> fast
>>>> > > > range
>>>> > > > > > > scans
>>>> > > > > > > > > against written data.
>>>> > > > > > > > >
>>>> > > > > > > > > The suggested approach stays in the
middle of the two
>>>> above
>>>> > and
>>>> > > > > > proved
>>>> > > > > > > to
>>>> > > > > > > > > perform well by distributing records
over the cluster
>>>> during
>>>> > > > > writing
>>>> > > > > > > data
>>>> > > > > > > > > while allowing range scans over it.
HBaseWD provides
>>>> very
>>>> > > simple
>>>> > > > > API
>>>> > > > > > to
>>>> > > > > > > > > work with which makes it perfect
to use with existing
>>>> code.
>>>> > > > > > > > >
>>>> > > > > > > > > Please refer to unit-tests for lib
usage info as they
>>>> aimed
>>>> > to
>>>> > > > act
>>>> > > > > as
>>>> > > > > > > > > example.
>>>> > > > > > > > >
>>>> > > > > > > > > Brief Usage Info (Examples):
>>>> > > > > > > > > ----------------------------
>>>> > > > > > > > >
>>>> > > > > > > > > Distributing records with sequential
keys which are
>>>> being
>>>> > > written
>>>> > > > > in
>>>> > > > > > up
>>>> > > > > > > > to
>>>> > > > > > > > > Byte.MAX_VALUE buckets:
>>>> > > > > > > > >
>>>> > > > > > > > >    byte bucketsCount = (byte) 32;
// distributing into
>>>> 32
>>>> > > buckets
>>>> > > > > > > > >    RowKeyDistributor keyDistributor
=
>>>> > > > > > > > >                           new
>>>> > > > > > > > > RowKeyDistributorByOneBytePrefix(bucketsCount);
>>>> > > > > > > > >    for (int i = 0; i < 100; i++)
{
>>>> > > > > > > > >      Put put = new
>>>> > > > > > Put(keyDistributor.getDistributedKey(originalKey));
>>>> > > > > > > > >      ... // add values
>>>> > > > > > > > >      hTable.put(put);
>>>> > > > > > > > >    }
>>>> > > > > > > > >
>>>> > > > > > > > >
>>>> > > > > > > > > Performing a range scan over written
data (internally
>>>> > > > > <bucketsCount>
>>>> > > > > > > > > scanners
>>>> > > > > > > > > executed):
>>>> > > > > > > > >
>>>> > > > > > > > >    Scan scan = new Scan(startKey,
stopKey);
>>>> > > > > > > > >    ResultScanner rs = DistributedScanner.create(hTable,
>>>> scan,
>>>> > > > > > > > > keyDistributor);
>>>> > > > > > > > >    for (Result current : rs) {
>>>> > > > > > > > >      ...
>>>> > > > > > > > >    }
>>>> > > > > > > > >
>>>> > > > > > > > > Performing mapreduce job over written
data chunk
>>>> specified by
>>>> > > > Scan:
>>>> > > > > > > > >
>>>> > > > > > > > >    Configuration conf = HBaseConfiguration.create();
>>>> > > > > > > > >    Job job = new Job(conf, "testMapreduceJob");
>>>> > > > > > > > >
>>>> > > > > > > > >    Scan scan = new Scan(startKey,
stopKey);
>>>> > > > > > > > >
>>>> > > > > > > > >    TableMapReduceUtil.initTableMapperJob("table",
scan,
>>>> > > > > > > > >      RowCounterMapper.class,
>>>> ImmutableBytesWritable.class,
>>>> > > > > > > Result.class,
>>>> > > > > > > > > job);
>>>> > > > > > > > >
>>>> > > > > > > > >    // Substituting standard TableInputFormat
which was
>>>> set in
>>>> > > > > > > > >    // TableMapReduceUtil.initTableMapperJob(...)
>>>> > > > > > > > >    job.setInputFormatClass(WdTableInputFormat.class);
>>>> > > > > > > > >    keyDistributor.addInfo(job.getConfiguration());
>>>> > > > > > > > >
>>>> > > > > > > > >
>>>> > > > > > > > > Extending Row Keys Distributing Patterns:
>>>> > > > > > > > > -----------------------------------------
>>>> > > > > > > > >
>>>> > > > > > > > > HBaseWD is designed to be flexible
and to support custom
>>>> row
>>>> > > key
>>>> > > > > > > > > distribution
>>>> > > > > > > > > approaches. To define custom row
key distributing logic
>>>> just
>>>> > > > > > implement
>>>> > > > > > > > > AbstractRowKeyDistributor abstract
class which is really
>>>> very
>>>> > > > > simple:
>>>> > > > > > > > >
>>>> > > > > > > > >    public abstract class AbstractRowKeyDistributor
>>>> implements
>>>> > > > > > > > > Parametrizable {
>>>> > > > > > > > >      public abstract byte[] getDistributedKey(byte[]
>>>> > > > originalKey);
>>>> > > > > > > > >      public abstract byte[] getOriginalKey(byte[]
>>>> > adjustedKey);
>>>> > > > > > > > >      public abstract byte[][]
>>>> getAllDistributedKeys(byte[]
>>>> > > > > > > originalKey);
>>>> > > > > > > > >      ... // some utility methods
>>>> > > > > > > > >    }
>>>> > > > > > > > >
>>>> > > > > > > >
>>>> > > > > > >
>>>> > > > > >
>>>> > > > >
>>>> > > >
>>>> > >
>>>> >
>>>>
>>>
>>>
>>
>

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