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HBase >> mail # user >> How to de-nomarlize for this situation in HBASE Table


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Re: How to de-nomarlize for this situation in HBASE Table
Hi,

Is there any other way instead of using HOME/Work/etc? we expect some 10
such types may come in future.. hence asking

regards,
Rams

On Fri, Jan 18, 2013 at 10:24 AM, Sonal Goyal <[EMAIL PROTECTED]> wrote:

> A rowkey is associated with the complete row. So you could have client id
> as the rowkey. Hbase allows different qualifiers within a column family, so
> you could potentially do the following:
>
> 1. You could have qualifiers like home address street 1, home address
> street 2, home address city, office address street 1 etc kind of qualifiers
> under physical address column family.
> 2. If you access entire address and not city, state individually, you could
> have the complete address concatenated and saved in one quailifer under
> physical address family using qualifiers like home, office, extra.....
>
> A good link to get started is
> http://hbase.apache.org/book/datamodel.html#conceptual.view
>
> Best Regards,
> Sonal
> Real Time Analytics for BigData <https://github.com/sonalgoyal/crux>
> Nube Technologies <http://www.nubetech.co>
>
> <http://in.linkedin.com/in/sonalgoyal>
>
>
>
>
> On Fri, Jan 18, 2013 at 10:09 AM, Ramasubramanian Narayanan <
> [EMAIL PROTECTED]> wrote:
>
> > Hi Sonal,
> >
> > In that case, the problem is how to store multiple physical address sets
> in
> > the same column family.. what rowkey to be used for this scenario..
> >
> > A Physical address will contain the following fields (need to store
> > multiple physical address like this):
> > Physical address type : Home/office/other/etc
> > Address line1:
> > ..
> > ..
> > Address line 4:
> > State :
> > City:
> > Country:
> >
> > regards,
> > Rams
> >
> >
> > On Fri, Jan 18, 2013 at 10:00 AM, Sonal Goyal <[EMAIL PROTECTED]>
> > wrote:
> >
> > > How about client id as the rowkey, with column families as physical
> > > address, email address, telephone address? within each cf, you could
> have
> > > various qualifiers. For eg in physical address, you could have home
> > Street,
> > > office street etc.
> > >
> > > Best Regards,
> > > Sonal
> > > Real Time Analytics for BigData <https://github.com/sonalgoyal/crux>
> > > Nube Technologies <http://www.nubetech.co>
> > >
> > > <http://in.linkedin.com/in/sonalgoyal>
> > >
> > >
> > >
> > >
> > > On Fri, Jan 18, 2013 at 9:46 AM, Ramasubramanian Narayanan <
> > > [EMAIL PROTECTED]> wrote:
> > >
> > > > Hi Sonal,
> > > >
> > > > 1. will fetch all demographic details of customer based on client ID
> > > > 2. Fetch the particular type of address along with other demographic
> > for
> > > a
> > > > client.. for example, HOME Physical address or HOME Telephone address
> > or
> > > > office Email address etc.,
> > > >
> > > > regards,
> > > > Rams
> > > >
> > > > On Fri, Jan 18, 2013 at 9:29 AM, Sonal Goyal <[EMAIL PROTECTED]>
> > > > wrote:
> > > >
> > > > > What are your data access patterns?
> > > > >
> > > > > Best Regards,
> > > > > Sonal
> > > > > Real Time Analytics for BigData <
> https://github.com/sonalgoyal/crux>
> > > > > Nube Technologies <http://www.nubetech.co>
> > > > >
> > > > > <http://in.linkedin.com/in/sonalgoyal>
> > > > >
> > > > >
> > > > >
> > > > >
> > > > > On Fri, Jan 18, 2013 at 9:04 AM, Ramasubramanian Narayanan <
> > > > > [EMAIL PROTECTED]> wrote:
> > > > >
> > > > > > Hi,
> > > > > >
> > > > > > I have the following relational tables.. I want to denormalize
> and
> > > > bring
> > > > > it
> > > > > > all into single HBASE table... Pls help how it could be done..
> > > > > >
> > > > > >
> > > > > > 1. Client Master Table
> > > > > > 2. Physical Address Table (there might be 'n' number of address
> > that
> > > > can
> > > > > be
> > > > > > captured against each client ID)
> > > > > > 3. Email Address Table (there might be 'n' number of address that
> > can
> > > > be
> > > > > > captured against each client ID)
> > > > > > 4. Telephone Address Table (there might be 'n' number of address
> > that
> > > > can
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB