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The following page has been changed by KevinWilliams:
http://wiki.apache.org/ws/Tuscany/TuscanyJava/DAS_Java_Overview/RDBDAS_Java_User_Guide/Partial_Update

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  = Partial Update =
  
- The RDB DAS uses the SDO Change Summary to generate a set of database write 
operations.  Rather than generated a statment to update all columns in a table 
that maps to a SDO DataObject Type, the DAS generates statements that update 
ony those columns that map to a modified DataObject property.  For example, 
consider a Customer DataObject that has had the LastName property changed.  The 
'''partial update''' statement generated by the DAS will look somthing like 
this:
+ The RDB DAS uses the SDO Change Summary to generate a set of database write 
operations.  Rather than generated a statement to update all columns in a table 
that maps to a SDO DataObject Type, the DAS generates statements that update 
ony those columns that map to a modified DataObject property.  For example, 
consider a Customer DataObject that has had the LastName property changed.  The 
'''partial update''' statement generated by the DAS will look something like 
this:
  {{{
     UPDATE CUSTOMER SET LASTNAME = ? WHERE ID = ?
  }}}
@@ -11, +11 @@

     UPDATE CUSTOMER SET FIRSTNAME = ?, SET LASTNAME = ?, SET ADDRESS = ?, SET 
PHONE = ?, SET AGE = ?, SET SHOESIZE = ? WHERE ID = ?
  }}}
  
- There are a couple of very good reasons for using a partial update approach.  
For one, a performance boost can be reaized by applications writing to very 
wide tables.  For example, this author has seen customer tables with over  100 
columns.  I can be a waste of resources to update 100 columns when only one was 
modified.  Another reason is that some databases also employ 
[http://en.wikipedia.org/wiki/Database_trigger triggers] on specific columns in 
a table.  Prtial updates are necessary when column triggers are employed to 
avoid tripping the trigger on every row update rather than only on updates to 
the specific column.
+ There are a couple of very good reasons for using a partial update approach.  
For one, a performance boost can be realized by applications writing to very 
wide tables.  For example, this author has seen customer tables with over 100 
columns.  I can be a waste of resources to update 100 columns when only one was 
modified.  Another reason is that some databases also employ 
[http://en.wikipedia.org/wiki/Database_trigger triggers] on specific columns in 
a table.  Partial updates are necessary when column triggers are employed to 
avoid tripping the trigger on every row update rather than only on updates to 
the specific column.
  

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