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From Mohit Anchlia <mohitanch...@gmail.com>
Subject Re: Row distribution
Date Thu, 26 Jul 2012 19:50:01 GMT
On Thu, Jul 26, 2012 at 10:34 AM, Alex Baranau <alex.baranov.v@gmail.com>wrote:

> > Is there any specific best practice on how many regions one
> > should split a table into?
>
> As always, "it depends". Usually you don't want your RegionServers to serve
> more than 50 regions or so. The fewer the better. But at the same time you
> usually want your regions to be distributed over the whole cluster (so that
> you use all power). So, it might make sense to start with one region per RS
> (if your writes are more or less evenly distributed across pre-splitted
> regions) if you don't know about you data size. If you know that you'll
> need to have more regions because of how big is your data, then you might
> create more regions at the start (with pre-splitting), so that you avoid
> region splits operations (you really want to avoid them if you can).
> Of course, you need to take into account other tables in your cluster as
> well. I.e. "usually not more than 50 regions" total per regionserver.
>
>


> Thanks for the detailed explanation. I understand the regions per
> regionserver, which is essentially range of rows distributed accross the
> cluster for a given table. But who decides on how many regionservers to
> have in the cluster?
>


> > Just one more question, in the split keys that you described below, is it
> > based on the first byte value of the Key?
>
> yes. And the first byte contains readable char, because of
> Bytes.ToBytes(String.valueOf(i)). If you want to prefix with (byte) 0, ...,
> (byte) 9 (i.e. with 0x00, 0x01, ..., 0x09) then no need to convert to
> String.
>
>
How different is this mechanism as compared to regionsplitter that uses
default string md5 split. Just trying to understand the difference in how
different the key range is.

> Alex Baranau
> ------
> Sematext :: http://blog.sematext.com/ :: Hadoop - HBase - ElasticSearch -
> Solr
>
> On Thu, Jul 26, 2012 at 11:43 AM, Mohit Anchlia <mohitanchlia@gmail.com
> >wrote:
>
> > On Thu, Jul 26, 2012 at 7:16 AM, Alex Baranau <alex.baranov.v@gmail.com
> > >wrote:
> >
> > > Looks like you have only one region in your table. Right?
> > >
> > > If you want your writes to be distributed from the start (without
> waiting
> > > for HBase to fill table enough to split it in many regions), you should
> > > pre-split your table. In your case you can pre-split table with 10
> > regions
> > > (just an example, you can define more), with start keys: "", "1", "2",
> > ...,
> > > "9" [1].
> > >
> > > Just one more question, in the split keys that you described below, is
> it
> > based on the first byte value of the Key?
> >
> >
> > > Btw, since you are salting your keys to achieve distribution, you might
> > > also find this small lib helpful which implements most of the stuff for
> > you
> > > [2].
> > >
> > > Hope this helps.
> > >
> > > Alex Baranau
> > > ------
> > > Sematext :: http://blog.sematext.com/ :: Hadoop - HBase -
> ElasticSearch
> > -
> > > Solr
> > >
> > > [1]
> > >
> > >     byte[][] splitKeys = new byte[9][];
> > >     // the first region starting with empty key will be created
> > > automatically
> > >     for (int i = 1; i < splitKeys.length; i++) {
> > >       splitKeys[i] = Bytes.toBytes(String.valueOf(i));
> > >     }
> > >
> > >     HBaseAdmin admin = new HBaseAdmin(conf);
> > >     admin.createTable(tableDescriptor, splitKeys);
> > >
> > > [2]
> > > https://github.com/sematext/HBaseWD
> > >
> > >
> >
> http://blog.sematext.com/2012/04/09/hbasewd-avoid-regionserver-hotspotting-despite-writing-records-with-sequential-keys/
> > >
> > > On Wed, Jul 25, 2012 at 7:54 PM, Mohit Anchlia <mohitanchlia@gmail.com
> > > >wrote:
> > >
> > > > On Wed, Jul 25, 2012 at 6:53 AM, Alex Baranau <
> > alex.baranov.v@gmail.com
> > > > >wrote:
> > > >
> > > > > Hi Mohit,
> > > > >
> > > > > 1. When talking about particular table:
> > > > >
> > > > > For viewing rows distribution you can check out how regions are
> > > > > distributed. And each region defined by the start/stop key, so
> > > depending
> > > > on
> > > > > your key format, etc. you can see which records go into each
> region.
> > > You
> > > > > can see the regions distribution in web ui as Adrien mentioned. It
> > may
> > > > also
> > > > > be handy for you to query .META. table [1] which holds regions
> info.
> > > > >
> > > > > In cases when you use random keys or when you just not sure how
> data
> > is
> > > > > distributed in key buckets (which are regions), you may also want
> to
> > > look
> > > > > at HBase data on HDFS [2]. Since data is stored for each region
> > > > separately,
> > > > > you can see the size on the HDFS each one occupies.
> > > > >
> > > > > I did a scan and the data looks like as pasted below. It appears
> all
> > my
> > > > writes are going to just one server. My keys are of this type
> > > > [0-9]:[current timestamp]. Number between 0-9 is generated randomly.
> I
> > > > thought by having this random number I'll be able to place my keys on
> > > > multiple nodes. How should I approach this such that I am able to use
> > > other
> > > > nodes as well?
> > > >
> > > >
> > > >
> > > >  SESSION_TIMELINE1,,1343074465420.5831bbac53e59
> column=info:regioninfo,
> > > > timestamp=1343170773523, value=REGION => {NAME =>
> > > > 'SESSION_TIMELINE1,,1343074465420.5831bbac53e591c609918c0e2d7da7
> > > >  1c609918c0e2d7da7bf.                           bf.', STARTKEY => '',
> > > > ENDKEY => '', ENCODED => 5831bbac53e591c609918c0e2d7da7bf, TABLE
=>
> > > {{NAME
> > > > => 'SESSION_TIMELINE1', FAMILIES => [{NAM
> > > >                                                 E => 'S_T_MTX',
> > > BLOOMFILTER
> > > > => 'NONE', REPLICATION_SCOPE => '0', COMPRESSION => 'GZ', VERSIONS
=>
> > > '1',
> > > > TTL => '2147483647', BLOCKSIZE => '
> > > >                                                 65536', IN_MEMORY =>
> > > > 'false', BLOCKCACHE => 'true'}]}}
> > > >  SESSION_TIMELINE1,,1343074465420.5831bbac53e59 column=info:server,
> > > > timestamp=1343178912655, value=dsdb3.:60020
> > > >  1c609918c0e2d7da7bf.
> > > >
> > > > > 2. When talking about whole cluster, it makes sense to use cluster
> > > > > monitoring tool [3], to find out more about overall load
> > distribution,
> > > > > regions of multiple tables distribution, requests amount, and many
> > more
> > > > > such things.
> > > > >
> > > > > And of course, you can use HBase Java API to fetch some data of the
> > > > cluster
> > > > > state as well. I guess you should start looking at it from
> HBaseAdmin
> > > > > class.
> > > > >
> > > > > Alex Baranau
> > > > > ------
> > > > > Sematext :: http://blog.sematext.com/ :: Hadoop - HBase -
> > > ElasticSearch
> > > > -
> > > > > Solr
> > > > >
> > > > > [1]
> > > > >
> > > > > hbase(main):001:0> scan '.META.', {LIMIT=>1, STARTROW=>"mytable,,"}
> > > > > ROW
> > > > > COLUMN+CELL
> > > > >
> > > > >
> > > > >  mytable,,1341279432683.8fd61cd7ef426d2f233a4cd7e8b73845.
> > > > >  column=info:regioninfo, timestamp=1341279432625, value=REGION =>
> > {NAME
> > > > =>
> > > > > 'mytable,,1341279432683.8fd61cd7ef426d2f233a4cd7e8b73845.',
> STARTKEY
> > =>
> > > > > 'chicago', ENDKEY => 'new_york', ENCODED =>
> > > > > fd61cd7ef426d2f233a4cd7e8b73845, TABLE => {{NAME => 'mytable',
> > FAMILIES
> > > > =>
> > > > > [{NAME => 'job', BLOOMFILTER => 'NONE', REPLICATION_SCOPE =>
'0',
> > > > > COMPRESSION => 'NONE', VERSIONS => '1', TTL => '2147483647',
> > BLOCKSIZE
> > > =>
> > > > > '65536', IN_MEMORY => 'false', BLOCKCACHE => 'true'}]}}
> > > > >
> > > > >
> > > > >
> > > > >  mytable,,1341279432683.8fd61cd7ef426d2f233a4cd7e8b73845.
> > > > >  column=info:server, timestamp=1341279432673, value=myserver:60020
> > > > >
> > > > >
> > > > >  mytable,,1341279432683.8fd61cd7ef426d2f233a4cd7e8b73845.
> > > > >  column=info:serverstartcode, timestamp=1341279432673,
> > > > value=1341267474257
> > > > >
> > > > >
> > > > > 1 row(s) in 0.1980 seconds
> > > > >
> > > > > [2]
> > > > >
> > > > > ubuntu@ip-10-80-47-73:~$ sudo -u hdfs hadoop fs -du /hbase/mytable
> > > > > Found 130 items
> > > > > 3397        hdfs://hbase.master/hbase/mytable
> > > > > /02925d3c335bff7e273f392324f16dca
> > > > > 2682163424  hdfs://hbase.master/hbase/mytable
> > > > > /03231b8ae2b73317c4858b1a85c09ad2
> > > > > 1038862956  hdfs://hbase.master/hbase/mytable
> > > > > /04f911571593e931a9a3d9e2a6616236
> > > > > 1039181555  hdfs://hbase.master/hbase/mytable
> > > > > /0a177633196cae7b158836181d69dc0f
> > > > > 1076888812  hdfs://hbase.master/hbase/mytable
> > > > > /0d52fc477c41a9a236803234d44c7c06
> > > > >
> > > > > [3]
> > > > > You can get data from JMX directly using any tool you like or use:
> > > > > * Ganglia
> > > > > * SPM monitoring (
> > > > > http://sematext.com/spm/hbase-performance-monitoring/index.html)
> > > > > * others
> > > > >
> > > > >
> > > > > On Wed, Jul 25, 2012 at 1:59 AM, Adrien Mogenet <
> > > > adrien.mogenet@gmail.com
> > > > > >wrote:
> > > > >
> > > > > > From the web-interface, you can have such statistics when viewing
> > the
> > > > > > details of a table.
> > > > > > You can also develop your own "balance viewer" through the HBase
> > API
> > > > > (list
> > > > > > of RS, regions, storeFiles, their size, etc.)
> > > > > >
> > > > > > On Wed, Jul 25, 2012 at 7:32 AM, Mohit Anchlia <
> > > mohitanchlia@gmail.com
> > > > > > >wrote:
> > > > > >
> > > > > > > Is there an easy way to tell how my nodes are balanced
and how
> > the
> > > > rows
> > > > > > are
> > > > > > > distributed in the cluster?
> > > > > > >
> > > > > >
> > > > > >
> > > > > >
> > > > > > --
> > > > > > Adrien Mogenet
> > > > > > 06.59.16.64.22
> > > > > > http://www.mogenet.me
> > > > > >
> > > > >
> > > > >
> > > > >
> > > > > --
> > > > > Alex Baranau
> > > > > ------
> > > > > Sematext :: http://blog.sematext.com/ :: Hadoop - HBase -
> > > ElasticSearch
> > > > -
> > > > > Solr
> > > > >
> > > >
> > >
> > >
> > >
> > > --
> > > Alex Baranau
> > > ------
> > > Sematext :: http://blog.sematext.com/ :: Hadoop - HBase -
> ElasticSearch
> > -
> > > Solr
> > >
> >
>
>
>
> --
> Alex Baranau
> ------
> Sematext :: http://blog.sematext.com/ :: Hadoop - HBase - ElasticSearch -
> Solr
>

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