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the topic is
“Normalization Process removes the anomalies from the data but ultimately it affects the Query Performance why..?Justify the statement.”
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kuch smjh aye to justify kren na kisi ko smjh ayi k kia justify krna he
ye kio DML oprations ki Query performance pr affect krta he koi bta skta he plzzzzzzzzzzzzzzz
Positive Affect: Normalization save our space by removing redundancy and anomalies and also save our record for curruption.
Negative Affect: It makes the DML query slow because number of joins are involved.
Fariha Chaudhry gud keep it up
This is because the Normalization process removes the anomalies from the data but ultimately it affects the Query performance of operations
Positive Affect: Normalization save our space by removing redundancy and anomalies and also save our record for corruption or damage.
Negative Affect: It makes the DML query slow because number of joins are involved
Fariha Unrelated meterial. Tariq bhai weldon....Really appreciate to you.....
Using of normalization in sql is good because.....
Database normalization is the process of organizing the fields and tables of a relational database to minimize redundancy and dependency. Normalization usually involves dividing large tables into smaller (and less redundant) tables and defining relationships between them. The objective is to isolate data so that additions, deletions, and modifications of a field can be made in just one table and then propagated through the rest of the database via the defined relationships.
CS403 GDB SOLUTION
Positive Affect: Normalization save our space by removing redundancy and anomalies and also
save our record for corruption.
Negative Affect: It makes the DML query slow because number of joins is involved.
You can take idea from following paragraphs!
Application of normal forms beyond third normal
form can tend to produce too many entities, resulting in too many
entities in SQL query joins. Too many entities in SQL query joins can
reduce system performance for any type of database. The more entities in a
join, the more difficult queries are to tune. Also, more query complexity
makes it more difficult for a database query optimizer to make a best guess
at the fastest execution path for a query. The result is poor performance.
From a purely commercial perspective, good performance is much more
important than granular perfection in relational database design. It’s not
about the design but more about satisfied customers and end users. Poor
response time from a computer system will upset people. In fact, poor
response time can be much more than simply upsetting because it can
impact business and the bottom line for a company.
Over normalization using third normal forms and beyond can lead to
poor performance in both OLTP and data warehouse type databases. Over normalization
is more commercially in top-down designed Java object
applications. In this situation, an object structure is imposed onto a relational
database. Object and relational data structures are completely different
methodologies, because the fine details of granularity are inherent in
object modeling. The same is true of extreme application of normal forms,
but that creates too many entities, too much processing built into a database
model, and ultimately highly complex SQL coding and poor performance
as a result.