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Imagine trying to find out an important document in your otherwise cluttered room when you are in a hurry to your office. When you do not find it yourself, you have to call your mom or dad to get it for you. You all try hard to find them everywhere and by the time you get it you are already late to your office. Why did it happen? It is because you have not kept your belongings in a proper managed way. Similar is the case with your data. If you do not store it in a disciplined manner there will be data redundancy and duplicity. This is called database anomaly. Over the next few paragraphs, we will learn about the different aspects of database anomaly.
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A database anomaly is a fault in a database that usually emerges as a result of shoddy planning and storing everything in a flat database. In most cases, this is removed through the normalization procedure, which involves the joining and splitting of tables.
There are namely three types of anomalies:
Let us now understand them in more detail. For that let us create a table called Emp and fill it up with some data. Following are the code to create the table and insert data into it.
Insert Anamoly
An insertion anomaly is the inability to add data to the database due to the absence of other data.
Here in this example, if a person joins as a trainee, who has not been assigned a department it will be very difficult to enter his data into the table based on the current design as all the fields in the current table is mandatory and for the new joinee there is no department assigned
Update Anamoly
An update anomaly is a data inconsistency that results from data redundancy and a partial update.
Glenn currently looks after two department and leaves in Chennai. Suppose Glenn decides to change his address from Chennai to Kolkata. Since Glenn works in multiple departments and there are multiple entries for him in the table, data for the all the rows has to be changed. If by any chance, we miss out on one of the rows, it will create an error which we term as update anomaly.
Delete Anamoly
A deletion anomaly occurs when you delete a record that may contain attributes that shouldn't be deleted. In the above example, if we delete department D890, all the data related to that department will be deleted.
To remove all these above-mentioned anomalies, we implement various techniques like normalization, implementation constraints and keys. Next we will learn about those things in detail.
Constraints
In DBMS, constraints are the set of rules that ensures that when an authorized user modifies the database they do not disturb the data consistency and the constraints are specified within the DDL commands like “alter” and “create” command. There are several types of constraints available in DBMS and they are:
For example, ID is used as a key in the Student table because it is unique for each student. In the PERSON table, passport_number, license_number, SSN are keys since they are unique for each person.
Types of Keys
Following are the different types of keys:
Primary Key
Query
CREATE TABLE [dbo].[Employee]( [EmpID] [int] NOT NULL, [EmpName] [varchar](50) NOT NULL, [Salary] [int] NOT NULL, [Address] [varchar](200) NOT NULL,
PRIMARY KEY CLUSTERED
( [EmpID] ASC )WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY] ) ON [PRIMARY] GO
Candidate Key
For example: In the EMPLOYEE table, id is best suited for the primary key. The rest of the attributes, like SSN, Passport_Number, License_Number, etc., are considered a candidate key.
Super Key
Super key is an attribute set that can uniquely identify a tuple. A super key is a superset of a candidate key.
For example: In the above EMPLOYEE table, for(EMPLOEE_ID, EMPLOYEE_NAME), the name of two employees can be the same, but their EMPLYEE_ID can't be the same. Hence, this combination can also be a key.
The super key would be EMPLOYEE-ID (EMPLOYEE_ID, EMPLOYEE-NAME), etc.
Foreign Key
Query
ALTER TABLE [Sales].[SalesOrderDetail] WITH CHECK ADD CONSTRAINT [FK_SalesOrderDetail_SpecialOfferProduct_SpecialOfferIDProductID] FOREIGN KEY([SpecialOfferID], [ProductID]) REFERENCES [Sales].[SpecialOfferProduct] ([SpecialOfferID], [ProductID])
Alternate Key
There may be one or more attributes or a combination of attributes that uniquely identify each tuple in a relation. These attributes or combinations of the attributes are called the candidate keys. One key is chosen as the primary key from these candidate keys, and the remaining candidate key, if it exists, is termed the alternate key. In other words, the total number of alternate keys is the total number of candidate keys minus the primary key. The alternate key may or may not exist. If there is only one candidate key in a relation, it does not have an alternate key.
For example, employee relation has two attributes, Employee_Id and PAN_No, that act as candidate keys. In this relation, Employee_Id is chosen as the primary key, so the other candidate key, PAN_No, acts as the Alternate key.
Composite Key
Whenever a primary key consists of more than one attribute, it is known as a composite key. This key is also known as Concatenated Key.
For example, in employee relations, we assume that an employee may be assigned multiple roles, and an employee may work on multiple projects simultaneously. So the primary key will be composed of all three attributes, namely Emp_ID, Emp_role, and Proj_ID in combination. So these attributes act as a composite key since the primary key comprises more than one attribute.
Artificial Key
The key created using arbitrarily assigned data are known as artificial keys. These keys are created when a primary key is large and complex and has no relationship with many other relations. The data values of the artificial keys are usually numbered in serial order.
For example, the primary key, which is composed of Emp_ID, Emp_role, and Proj_ID, is large in employee relations. So it would be better to add a new virtual attribute to identify each tuple in the relation uniquely.
Normalization is the process of organizing data in a database. This includes creating tables and establishing relationships between those tables according to rules designed both to protect the data and to make the database more flexible by eliminating redundancy and inconsistent dependency.
A large database defined as a single relation may result in data duplication. This repetition of data may result in:
So to handle these problems, we should analyze and decompose the relations with redundant data into smaller, simpler, and well-structured relations that are satisfying desirable properties. Normalization is a process of decomposing relations into relations with fewer attributes.
In short normalization means:
The main reason for normalizing the relations is to remove these anomalies. Failure to eliminate anomalies leads to data redundancy and can cause data integrity and other problems as the database grows. Normalization consists of a series of guidelines that helps to guide you in creating a good database structure.
Types of Normalisation
Normalization works through a series of stages called Normal forms. The normal forms apply to individual relations. The relation is said to be in particular normal form if it satisfies constraints.
|
Normal Form |
Description |
|
1NF |
A relation is in 1NF if it contains an atomic value. |
|
2NF |
A relation will be in 2NF if it is in 1NF and all non-key attributes are fully functional dependant on the primary key |
|
3NF |
A relation will be in 3NF if it is in 2NF no transition dependency exist |
|
BCNF |
A stronger definition of 3NF is known as Boyce Codd normal form |
|
4NF |
A relation will be in 4 NF if it is in BCNF and has no multi valued dependency |
|
5NF |
A relation is in 5NF if it is in 4NF and does not contain any join dependency, joining should be lossless. |
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Over the last few paragraphs, we learned about the different aspects of database anomalies and ways to counter them. This write-up can be a way forward to further studies about database anomalies and how to prevent them while designing a real-time database.
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