Joins in SQL SERVER

Sql Server is all about data & the data may be stored on the multiple places(tables) which could be queried to trace information at any point of time. Whenever thinking about retrieving the concerned information from all the tables in the form of report the joins come in picture. Joins are an very important part of database & without using them it is really impossible to even think about database.

These are the most used & main type of joins in SqlServer,

1. Inner Join
2. Outer Join
3. Cross Join

Use below mentioned script on any of your test database for understanding the various types with example.

CREATE TABLE STUDENTS(STUDENTID INT IDENTITY(1,1), NAME VARCHAR(100), MOBILENO BIGINT)

CREATE TABLE SPECIALCOURSE(ID INT IDENTITY(1,1), STUDENTID INT, STATUS BIT)

INSERT INTO STUDENTS(NAME, MOBILENO)

SELECT 'AJAY',9797979797

UNION ALL

SELECT 'SANJAY',1234567890

UNION ALL

SELECT 'SAMMY',9874563210

UNION ALL

SELECT 'SAMUAL',8789456512

UNION ALL

SELECT 'GANESH',7412589630

UNION ALL

SELECT 'MAHESH', 9632587400

INSERT INTO SPECIALCOURSE(STUDENTID, STATUS)

SELECT 1,0

UNION ALL

SELECT 3,1

Inner Join

1.1) Inner Join:

Inner join is the most used join type in SqlServer. Whenever trying to check the concerned data only from the system the inner join is used. Inner join is very useful whenever user needs to get the only matching data from the concerned tables. To use this join type the matching columns from all the required tables needs to be mention so while executing query SqlServer engine consider the specified column for returning the proper information.

Ex. Code:

SELECT A.STUDENTID, A.NAME, A.MOBILENO, B.STATUS

FROM STUDENTS A INNER JOIN SPECIALCOURSE B ON A.STUDENTID = B.STUDENTID

1.2)Self Join:

Another type of inner join is Self join which could be defined as join within same table for extraction of related data within same table. The best example for this will be Manager/Employee relationship between same table. Unfortunately I have not designed my table which has this relationship otherwise I could have showed the example query.

Outer Join

2.1) Left Outer Join:

Whenever we wish to get all the data from the left table we need to use Left Outer Join.. Left join will return all the matching rows from the left table & in case of no match it will return NULL.
It is really useful when we want to find all the information from one side table with the relevant information from second table.

Here the left table is STUDENTS & in second table i.e. SPECIALCOURSE there is some information about few students who have joined special course.

Ex. Code:

SELECT A.STUDENTID, A.NAME, A.MOBILENO, B.STATUS

FROM STUDENTS A LEFT JOIN SPECIALCOURSE B ON A.STUDENTID = B.STUDENTID

2.2) Right Outer Join:

There may be a situation during the query when user needs information from second second & the matching or non-matching records from first table. In these scenarios developer will not swap the table from left to right as there is already a provision called Right Outer Join where the purpose can be achieved by using Right Outer Join.

In this scenario user will receive all the the information from second(Right) table & the matching information from first table.

Ex. Code:

SELECT A.STUDENTID, A.NAME, A.MOBILENO, B.STATUS

FROM STUDENTS A RIGHT JOIN SPECIALCOURSE B ON A.STUDENTID = B.STUDENTID

2.3) FULL Outer Join:

FULL outer join do not look for the match between the two tables & returns all the information from both the tables. This kind of results may be required when there is a need of all the data from system which both the tables.

Ex. Code:

SELECT A.STUDENTID, A.NAME, A.MOBILENO, B.STATUS

FROM STUDENTS A FULL OUTER JOIN SPECIALCOURSE B ON A.STUDENTID = B.STUDENTID

CROSS JOIN

Working with Cross join like working with multiplication between tables. Whenever there is some kind of situation when a user needs to map all the records from left table with all records of left table then in this situation the Cross join is brought into picture.

Here in this below query Students table 6 records 6 records & SPECIALCOURSE table has 2 records. As mentioned above it will return the multiplication the output will be 6 * 2 = 12 records & in output all the students will be mapped to the all SPECIALCOURSE records.

Ex. Code:

SELECT A.STUDENTID, A.NAME, A.MOBILENO, B.STATUS

FROM STUDENTS A CROSS JOIN SPECIALCOURSE B

Note: If the any of the two tables do not have record then there will be no output from this join.

Like other join type this join type needs not to have a ON condition.

Normalization VS De-Normalization

Normalization & De-Normalization

Normalization can be described as a breaking up of the information into multiple parts or logical units in the form of tables. The concept of normalization is used for organizing the data in database in a managed manner where the redundancy of data does not happen & data remains accurate.

Normalization is basically a process of reducing redundancy i.e. Data repetition in database for similar type of information where-in De-normalizations only limited number of tables are created. There are lot of many more merits & demerits of Normalization & De-normalization so let’s look them.

 Normalization:

As mentioned above Normalization is a process of reducing redundancy i.e. Data repetition in database for similar type of information so While applying normalization on database the data is spilt into multiple sets of table like, master tables are created for each category of information so that the same string do not gets repeated into system. Normalization is a process of set guidelines which needs to be followed for implementing normalization.

A non-normalized database may contain some tables which may contain the most of data into a few tables. There are some points which indicate the general problems of non-normalized database.

1.       Data is stored in few tables only instead of multiple tables but this is not good as data is easily identical which is bad practice from the security perspective

2.        Query performance will be affected as the table will grow drastically

3.       Activities like Update will be affected causing the poor efficiency of database and Data integrity.

In a process of applying normalization on database few points needs to be keep in mind among which some are as follow,

·         Data should not be redundant (Duplicate) or very minimum for some cases for ex. In a candidate management system where candidates information is stored. Here in this case the address & other optional information is not required to stored in a same table as it is not always required while searching basic information about candidate.

In the process of normalization naming convention is always an important point because in vice-versa to de-normalization the data is split into multiple tables & during the needs the tables needs to be joined with one another & in this case the identical & proper naming convention will play a key role.

Normalization benefits the database in many ways some of them are as follow,

1.       Proper database organization

2.       Avoiding redundant data

3.       Database consistency and integrity in a which assures the accurate & co-related data within same database.

4.       Proper & Secure database system as an unknown & unfamiliar user cannot find & understand data easily.

5.       Referential integrity means the values of one table is dependent upon the one or more than one number of columns

6.       Having a parent child relations between tables so that any referential data is not lost

As like the other thing normalization also has some drawbacks which is that due to heavy normalization during the processing of data during the insert, update or select all the relational objects are required to be bring in use which cause the high CPU use & IO operation increase so although the huge benefits the normalization should be carried out wisely.

De-Normalization:

Almost described most of the differences between normalization & de-normalization & the various pros & cons of both concepts

Description of de-normalization in simple terms “Instead of storing data into multiple space a few number of tables where most of the data can be stored for stress free environment without caring about too much of data hiding methodology”