RDBMS normalization concepts with examples


Following topics are covered in this blog post with examples:

    1.      Normalization
    2.      First normal form
    3.      Second normal form
    4.      Third normal form
    5.      Boyce-codd normal form
    6.      Codd’s rules


What is Normalization?
Normalization is the process of efficiently organizing data in a database. There are two goals of the normalization process: eliminating redundant data (for example, storing the same data in more than one table) and ensuring data dependencies make sense (only storing related data in a table). Both of these are worthy goals as they reduce the amount of space a database consumes and ensure that data is logically stored.
The Normal Forms
The database community has developed a series of guidelines for ensuring that databases are normalized. These are referred to as normal forms and are numbered from one (the lowest form of normalization, referred to as first normal form or 1NF) through five (fifth normal form or 5NF). In practical applications, you'll often see 1NF, 2NF, and 3NF along with the occasional 4NF. Fifth normal form is very rarely seen and won't be discussed in this article.
First Normal Form (1NF)
First normal form (1NF) sets the very basic rules for an organized database:
·         Eliminate duplicative columns from the same table.
·         Create separate tables for each group of related data and identify each row with a unique column or set of columns (the primary key).

Second Normal Form (2NF)

Second normal form (2NF) further addresses the concept of removing duplicative data:
  • Meet all the requirements of the first normal form.
  • Remove subsets of data that apply to multiple rows of a table and place them in separate tables.
  • Create relationships between these new tables and their predecessors through the use of foreign keys.
Normalizing Your Database: First Normal Form (1NF)
First Normal Form (1NF) sets the very basic rules for an organized database:
  • Eliminate duplicative columns from the same table.
  • Create separate tables for each group of related data and identify each row with a unique column (the primary key).
What do these rules mean when contemplating the practical design of a database? It’s actually quite simple.
The first rule dictates that we must not duplicate data within the same row of a table. Within the database community, this concept is referred to as the atomicity of a table. Tables that comply with this rule are said to be atomic. Let’s explore this principle with a classic example – a table within a human resources database that stores the manager-subordinate relationship. For the purposes of our example, we’ll impose the business rule that each manager may have one or more subordinates while each subordinate may have only one manager.
Intuitively, when creating a list or spreadsheet to track this information, we might create a table with the following fields:
  • Manager
  • Subordinate1
  • Subordinate2
  • Subordinate3
  • Subordinate4
However, recall the first rule imposed by 1NF: eliminate duplicative columns from the same table. Clearly, the Subordinate1-Subordinate4 columns are duplicative. Take a moment and ponder the problems raised by this scenario. If a manager only has one subordinate – the Subordinate2-Subordinate4 columns are simply wasted storage space (a precious database commodity). Furthermore, imagine the case where a manager already has 4 subordinates – what happens if she takes on another employee? The whole table structure would require modification.
At this point, a second bright idea usually occurs to database novices: We don’t want to have more than one column and we want to allow for a flexible amount of data storage. Let’s try something like this:
  • Manager
  • Subordinates
where the Subordinates field contains multiple entries in the form "Mary, Bill, Joe"
This solution is closer, but it also falls short of the mark. The subordinates column is still duplicative and non-atomic. What happens when we need to add or remove a subordinate? We need to read and write the entire contents of the table. That’s not a big deal in this situation, but what if one manager had one hundred employees? Also, it complicates the process of selecting data from the database in future queries.
Here’s a table that satisfies the first rule of 1NF:
  • Manager
  • Subordinate
In this case, each subordinate has a single entry, but managers may have multiple entries.
Now, what about the second rule: identify each row with a unique column or set of columns (the primary key)? You might take a look at the table above and suggest the use of the subordinate column as a primary key. In fact, the subordinate column is a good candidate for a primary key due to the fact that our business rules specified that each subordinate may have only one manager. However, the data that we’ve chosen to store in our table makes this a less than ideal solution. What happens if we hire another employee named Jim? How do we store his manager-subordinate relationship in the database?
It’s best to use a truly unique identifier (such as an employee ID) as a primary key. Our final table would look like this:
  • Manager ID
  • Subordinate ID

Normalizing Your Database: Second Normal Form (2NF)
Recall the general requirements of 2NF:
  • Remove subsets of data that apply to multiple rows of a table and place them in separate tables.
  • Create relationships between these new tables and their predecessors through the use of foreign keys.
These rules can be summarized in a simple statement: 2NF attempts to reduce the amount of redundant data in a table by extracting it, placing it in new table(s) and creating relationships between those tables.

Let's look at an example. Imagine an online store that maintains customer information in a database. They might have a single table called Customers with the following elements:
  • CustNum
  • FirstName
  • LastName
  • Address
  • City
  • State
  • ZIP
A brief look at this table reveals a small amount of redundant data. We're storing the "Sea Cliff, NY 11579" and "Miami, FL 33157" entries twice each. Now, that might not seem like too much added storage in our simple example, but imagine the wasted space if we had thousands of rows in our table. Additionally, if the ZIP code for Sea Cliff were to change, we'd need to make that change in many places throughout the database.
In a 2NF-compliant database structure, this redundant information is extracted and stored in a separate table. Our new table (let's call it ZIPs) might have the following fields:
  • ZIP
  • City
  • State
If we want to be super-efficient, we can even fill this table in advance -- the post office provides a directory of all valid ZIP codes and their city/state relationships. Surely, you've encountered a situation where this type of database was utilized. Someone taking an order might have asked you for your ZIP code first and then knew the city and state you were calling from. This type of arrangement reduces operator error and increases efficiency.

Now that we've removed the duplicative data from the Customers table, we've satisfied the first rule of second normal form. We still need to use a foreign key to tie the two tables together. We'll use the ZIP code (the primary key from the ZIPs table) to create that relationship. Here's our new Customers table:
  • CustNum
  • FirstName
  • LastName
  • Address
  • ZIP
We've now minimized the amount of redundant information stored within the database and our structure is in second normal form!

--NORMALIZATION
--FIRST NORMAL FORM
--If you see this table. we have customer_trx_line_id in both the tables
--ra_customer_trx_all & ra_customer_trx_lines_all. Each customer_trx_id may have more than one customer_trx_line_id
--instead of having multiple columns for each customer_trx_line we put only one column having multiple records for
--each line, 
select * from ra_customer_trx_all
select * from ra_customer_trx_lines_all where customer_trx_id  = 10094
-- now instead of having many line_id columns,we create only one line_id column and multiple records
-- & make it a primary key
--SECOND NORMAL FORM
--If you see this table. we have cust_trx_type_id column,
--This column is a foreign key here, but is a primary key in ra_cust_trx_types_all table
--instead of using all the data related to customer transaction (like trx name, trx_type(inv, CM, DM), trx_status)
--in ra_customer_trx_all table, we place this related data in a separate table & use the ID column (cust_trx_type_id)
--to logically join the data

select * from ra_customer_trx_all rcta
select * from ra_cust_trx_types_all rctt
rcta.cust_trx_type_id = rctt.cust_trx_type_id
-- now we can refer the customer transaction details using the above join condition.

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