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From "Dan Hendry" <dan.hendry.j...@gmail.com>
Subject RE: Data Model Design for Login Servie
Date Fri, 18 Nov 2011 15:14:00 GMT
I they are not limited to repeating values but the Datastax docs[1] on secondary indexes certainly
seem to indicate they would be a poor fit for this case (high read load, many unique values).

 

[1] http://www.datastax.com/docs/1.0/ddl/indexes

 

Dan

 

From: Maciej Miklas [mailto:mac.miklas@googlemail.com] 
Sent: November-18-11 1:39
To: user@cassandra.apache.org
Subject: Re: Data Model Design for Login Servie

 

but secondary index is limited only to repeating values like enums. In my case I would have
performance issue. right?


On 18.11.2011, at 02:08, Maxim Potekhin <potekhin@bnl.gov> wrote:

1122: {
          gender: MALE
          birthdate: 1987.11.09
          name: Alfred Tester
          pwd: e72c504dc16c8fcd2fe8c74bb492affa
          alias1: alfred.tester@xyz.de
          alias2: alfred@aad.de
          alias3: alf@dd.de
         }

...and you can use secondary indexes to query on anything.

Maxim


On 11/17/2011 4:08 PM, Maciej Miklas wrote: 

Hallo all,

I need your help to design structure for simple login service. It contains about 100.000.000
customers and each one can have about 10 different logins - this results 1.000.000.000 different
logins.
    
Each customer contains following data:
- one to many login names as string, max 20 UTF-8 characters long
- ID as long - one customer has only one ID
- gender
- birth date
- name
- password as MD5

Login process needs to find user by login name.
Data in Cassandra is replicated - this is necessary to obtain all required login data in single
call. Also usually we expect low write traffic and heavy read traffic - round trips for reading
data should be avoided.
Below I've described two possible cassandra data models based on example: we have two users,
first user has two logins and second user has three logins
   
A) Skinny rows
 - row key contains login name - this is the main search criteria
 - login data is replicated - each possible login is stored as single row which contains all
user data - 10 logins for single customer create 10 rows, where each row has different key
and the same content

    // first 3 rows has different key and the same replicated data
        alfred.tester@xyz.de {
          id: 1122
          gender: MALE
          birthdate: 1987.11.09
          name: Alfred Tester
          pwd: e72c504dc16c8fcd2fe8c74bb492affa  
        },
        alfred@aad.de {
          id: 1122
          gender: MALE
          birthdate: 1987.11.09
          name: Alfred Tester
          pwd: e72c504dc16c8fcd2fe8c74bb492affa  
        },
        alf@dd.de {
          id: 1122
          gender: MALE
          birthdate: 1987.11.09
          name: Alfred Tester
          pwd: e72c504dc16c8fcd2fe8c74bb492affa  
        },
    
    // two following rows has again the same data for second customer
        manfred@xyz.de {
          id: 1133
          gender: MALE
          birthdate: 1997.02.01
          name: Manfredus Maximus
          pwd: e44c504ff16c8fcd2fe8c74bb492adda  
        },
        roberrto@xyz.de {
          id: 1133
          gender: MALE
          birthdate: 1997.02.01
          name: Manfredus Maximus
          pwd: e44c504ff16c8fcd2fe8c74bb492adda  
        }
    
B) Rows grouped by alphabetical prefix
- Number of rows is limited - for example first letter from login name
- Each row contains all logins which benign with row key - row with key 'a' contains all logins
which begin with 'a'
- Data might be unbalanced, but we avoid skinny rows - this might have positive performance
impact (??)
- to avoid super columns each row contains directly columns, where column name is the user
login and column value is corresponding data in kind of serialized form (I would like to have
is human readable)

    a {
        alfred.tester@xyz.de:"1122;MALE;1987.11.09;
                                 Alfred Tester;e72c504dc16c8fcd2fe8c74bb492affa",
        
        alfred@aad.de@xyz.de:"1122;MALE;1987.11.09;
                                 Alfred Tester;e72c504dc16c8fcd2fe8c74bb492affa",
            
        alf@dd.de@xyz.de:"1122;MALE;1987.11.09;
                                 Alfred Tester;e72c504dc16c8fcd2fe8c74bb492affa"
      },
            
    m {
        manfred@xyz.de:"1133;MALE;1997.02.01;
                  Manfredus Maximus;e44c504ff16c8fcd2fe8c74bb492adda"    
      },
            
    r {
        roberrto@xyz.de:"1133;MALE;1997.02.01;
                  Manfredus Maximus;e44c504ff16c8fcd2fe8c74bb492adda"    
            
      }

Which solution is better, especially for better read performance? Do you have better idea?

Thanks,
Maciej

 

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