SQL-Tutorials ❤️

In this module, I will be updating the topic wise SQL tutorial notes which is very useful for a fresher to start with MYSQL from basics

Introduction 👋

• SQL stands for Structured Query Language.
• This tutorial will give you a quick start to SQL.
• It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.

A Brief History of SQL ⌚

1970 − Dr. Edgar F. "Ted" Codd of IBM is known as the father of relational databases. He described a relational model for databases.

1974 − Structured Query Language appeared.

1978 − IBM worked to develop Codd's ideas and released a product named System/R.

1986 − IBM developed the first prototype of relational database and standardized by ANSI. The first relational database was released by Relational Software which later came to be known as Oracle.

Why to Learn SQL 🤔

• SQL is the standard language for Relational Database System.
• All the Relational Database Management Systems (RDMS) like MySQL, MS Access, Oracle, Sybase, Informix, Postgres and SQL Server use SQL as their standard database language.

Also, they are using different dialects, such as −

1) MS SQL Server using T-SQL,

2) Oracle using PL/SQL,

3) MS Access version of SQL is called JET SQL (native format) etc.

Features of MYSQL 🚀

1) High Performance

2) High Availability

3) Database mirroring

4) Database snapshots

5) CLR integration

6) Service Broker

7) DDL triggers

8) Ranking functions

9) Row version-based isolation levels

10) XML integration

11) TRY...CATCH

12) Database Mail

Applications of SQL ✔️

• SQL is one of the most widely used query language over the databases.
• I'm going to list few of them here:

- Allows users to access data in the relational database management systems.

- Allows users to describe the data.

- Allows users to define the data in a database and manipulate that data.

- Allows to embed within other languages using SQL modules, libraries & pre-compilers.

- Allows users to create and drop databases and tables.

- Allows users to create view, stored procedure, functions in a database.

- Allows users to set permissions on tables, procedures and views.

SQL Process ⌛

• When you are executing an SQL command for any RDBMS, the system determines the best way to carry out your request and SQL engine figures out how to interpret the task.
• There are various components included in this process. These components are −

1) Query Dispatcher

2) Optimization Engines

3) Classic Query Engine

4) SQL Query Engine, etc.

5) A classic query engine handles all the non-SQL queries, but a SQL query engine won't handle logical files.

SQL Commands 📌

• The standard SQL commands to interact with relational databases are CREATE, SELECT, INSERT, UPDATE, DELETE and DROP.
• These commands can be classified into the following groups based on their nature −

DDL - Data Definition Language 😀

CREATE

• Creates a new table, a view of a table, or other object in the database.

ALTER

• Modifies an existing database object, such as a table.

DROP

• Deletes an entire table, a view of a table or other objects in the database.

DML - Data Manipulation Language 😀

INSERT

• Creates a record.

UPDATE

= Modifies records.

DELETE

• Deletes records.

DCL - Data Control Language 😀

GRANT

• Gives a privilege to user.

REVOKE

• Takes back privileges granted from user.

TCL - Transaction Control Language 😀

COMMIT

• Save all the transactions to the database.

ROLLBACK

• Undo transactions that have not already been saved to the database.

SAVEPOINT

• Roll the transaction back to a certain point without rolling back the entire transaction.

Data Query Language 😀

SELECT

• select the attribute based on the condition described by WHERE clause.

RDBMS ⌛

• RDBMS stands for Relational Database Management System.
• RDBMS is the basis for SQL, and for all modern database systems like MS SQL Server, IBM DB2, Oracle, MySQL, and Microsoft Access.
• A Relational database management system (RDBMS) is a database management system (DBMS) that is based on the relational model as introduced by E. F. Codd.

Tables 🎰

• The data in an RDBMS is stored in database objects which are called as tables.
• This table is basically a collection of related data entries and it consists of numerous columns and rows.

Field 🎯

• Every table is broken up into smaller entities called fields.
• The fields in the CUSTOMERS table consist of ID, NAME, AGE, ADDRESS and SALARY.
• A field is a column in a table that is designed to maintain specific information about every record in the table.

Record 🎲

• A record is also called as a row of data is each individual entry that exists in a table.

Column 🏹

• A column is a vertical entity in a table that contains all information associated with a specific field in a table.

NULL value 🚫

• A NULL value in a table is a value in a field that appears to be blank, which means a field with a NULL value is a field with no value.
• It is very important to understand that a NULL value is different than a zero value or a field that contains spaces.
• A field with a NULL value is the one that has been left blank during a record creation.

SQL Constraints 🚩

• Constraints are the rules enforced on data columns on a table.
• These are used to limit the type of data that can go into a table.
• This ensures the accuracy and reliability of the data in the database.
• Constraints can either be column level or table level.
• Column level constraints are applied only to one column whereas, table level constraints are applied to the entire table.

Following are some of the most commonly used constraints available in SQL −

!) NOT NULL Constraint

− Ensures that a column cannot have a NULL value.

2) DEFAULT Constraint

− Provides a default value for a column when none is specified.

3) UNIQUE Constraint

− Ensures that all the values in a column are different.

4) PRIMARY Key

− Uniquely identifies each row/record in a database table.

5) FOREIGN Key

− Uniquely identifies a row/record in any another database table.

6) CHECK Constraint

− The CHECK constraint ensures that all values in a column satisfy certain conditions.

7) INDEX

− Used to create and retrieve data from the database very quickly.

Data Integrity 🚧

• The following categories of data integrity exist with each RDBMS −

Entity Integrity − There are no duplicate rows in a table.

Domain Integrity − Enforces valid entries for a given column by restricting the type, the format, or the range of values.

Referential integrity − Rows cannot be deleted, which are used by other records.

User-Defined Integrity − Enforces some specific business rules that do not fall into entity, domain or referential integrity.

Database Normalization 🚀

• Database normalization is the process of efficiently organizing data in a database.
• There are two reasons of this normalization process −

1) Eliminating redundant data, for example, storing the same data in more than one table.

2) Ensuring data dependencies make sense.

• Both these reasons are worthy goals as they reduce the amount of space a database consumes and ensures that data is logically stored.

• Normalization consists of a series of guidelines that help guide you in creating a good database structure.

• Normalization guidelines are divided into normal forms; think of a form as the format or the way a database structure is laid out.

• The aim of normal forms is to organize the database structure, so that it complies with the rules of first normal form, then second normal form and finally the third normal form.

• It is your choice to take it further and go to the fourth normal form, fifth normal form and so on, but in general, the third normal form is more than enough.

First Normal Form (1NF) Second Normal Form (2NF) Third Normal Form (3NF)

Various Syntax in SQL 🚨

SQL SELECT Statement

SELECT column1, column2....columnN
FROM   table_name;

SQL DISTINCT Clause

SELECT DISTINCT column1, column2....columnN
FROM   table_name;

SQL WHERE Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  CONDITION;

SQL AND/OR Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  CONDITION-1 {AND|OR} CONDITION-2;

SQL IN Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  column_name IN (val-1, val-2,...val-N);

SQL BETWEEN Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  column_name BETWEEN val-1 AND val-2;

SQL LIKE Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  column_name LIKE { PATTERN };

SQL ORDER BY Clause

SELECT column1, column2....columnN
FROM   table_name
WHERE  CONDITION
ORDER BY column_name {ASC|DESC};

SQL GROUP BY Clause

SELECT SUM(column_name)
FROM   table_name
WHERE  CONDITION
GROUP BY column_name;

SQL COUNT Clause

SELECT COUNT(column_name)
FROM   table_name
WHERE  CONDITION;

SQL HAVING Clause

SELECT SUM(column_name)
FROM   table_name
WHERE  CONDITION
GROUP BY column_name
HAVING (arithematic function condition);

SQL CREATE TABLE Statement

CREATE TABLE table_name(
    column1 datatype,
    column2 datatype,
    column3 datatype,
    .....
    columnN datatype,
    PRIMARY KEY( one or more columns )
);

SQL DROP TABLE Statement

DROP TABLE table_name;

SQL CREATE INDEX Statement

CREATE UNIQUE INDEX index_name
ON table_name ( column1, column2,...columnN);

SQL DROP INDEX Statement

ALTER TABLE table_name
DROP INDEX index_name;

SQL DESC Statement

DESC table_name;

SQL TRUNCATE TABLE Statement

TRUNCATE TABLE table_name;

SQL ALTER TABLE Statement

ALTER TABLE table_name {ADD|DROP|MODIFY} column_name {data_ype};

SQL ALTER TABLE Statement (Rename)

ALTER TABLE table_name RENAME TO new_table_name;

SQL INSERT INTO Statement

INSERT INTO table_name( column1, column2....columnN)
VALUES ( value1, value2....valueN);

SQL UPDATE Statement

UPDATE table_name
SET column1 = value1, column2 = value2....columnN=valueN
[ WHERE  CONDITION ];

SQL DELETE Statement

DELETE FROM table_name
WHERE  {CONDITION};

SQL CREATE DATABASE Statement

CREATE DATABASE database_name;

SQL DROP DATABASE Statement

DROP DATABASE database_name;

SQL USE Statement

USE database_name;

SQL COMMIT Statement

COMMIT;

SQL ROLLBACK Statement

ROLLBACK;

SQL - Data Types 🧐

• SQL Data Type is an attribute that specifies the type of data of any object.
• Each column, variable and expression has a related data type in SQL.
• You can use these data types while creating your tables.
• You can choose a data type for a table column based on your requirement.
• SQL Server offers six categories of data types for your use which are listed below −

1) Exact Numeric Data Types

2) Approximate Numeric Data Types

3) Date and Time Data Types

4) Character Strings Data Types

5) Unicode Character Strings Data Types

6) Binary Data Types

7) Misc Data Types

Operator in SQL ⚡

• An operator is a reserved word or a character used primarily in an SQL statement's WHERE clause to perform operation(s), such as comparisons and arithmetic operations.
• These Operators are used to specify conditions in an SQL statement and to serve as conjunctions for multiple conditions in a statement.

- Arithmetic operators

- Comparison operators

- Logical operators

- Operators used to negate conditions

SQL Arithmetic Operators ✨

+ (Addition)

• Adds values on either side of the operator.

- (Subtraction)

• Subtracts right hand operand from left hand operand.

* (Multiplication)

• Multiplies values on either side of the operator.

/ (Division)

• Divides left hand operand by right hand operand.

% (Modulus)

• Divides left hand operand by right hand operand and returns remainder.

SQL Comparison Operators ⭐

= (Equals)

• Checks if the values of two operands are equal or not, if yes then condition becomes true.

!= (NotEquals)

• Checks if the values of two operands are equal or not, if values are not equal then condition becomes true.

<> (Equality)

• Checks if the values of two operands are equal or not, if values are not equal then condition becomes true.

> (Greater than)

• Checks if the value of left operand is greater than the value of right operand, if yes then condition becomes true.

< (Lesser than)

• Checks if the value of left operand is less than the value of right operand, if yes then condition becomes true.

>= (Greater than or Equal to)

• Checks if the value of left operand is greater than or equal to the value of right operand, if yes then condition becomes true.

<= (Lesser than or Equal to)

• Checks if the value of left operand is less than or equal to the value of right operand, if yes then condition becomes true.

!< (Not Less than)

• Checks if the value of left operand is not less than the value of right operand, if yes then condition becomes true.

!> (Not Greater than)

• Checks if the value of left operand is not greater than the value of right operand, if yes then condition becomes true.

SQL Logical Operators ❗

ALL

• The ALL operator is used to compare a value to all values in another value set.

AND

• The AND operator allows the existence of multiple conditions in an SQL statement's WHERE clause.

ANY

• The ANY operator is used to compare a value to any applicable value in the list as per the condition.

BETWEEN

• The BETWEEN operator is used to search for values that are within a set of values, given the minimum value and the maximum value.

EXISTS

• The EXISTS operator is used to search for the presence of a row in a specified table that meets a certain criterion.

IN

• The IN operator is used to compare a value to a list of literal values that have been specified.

LIKE

• The LIKE operator is used to compare a value to similar values using wildcard operators.

NOT

• The NOT operator reverses the meaning of the logical operator with which it is used. Eg: NOT EXISTS, NOT BETWEEN, NOT IN, etc. This is a negate operator.

OR

• The OR operator is used to combine multiple conditions in an SQL statement's WHERE clause.

IS NULL

• The NULL operator is used to compare a value with a NULL value.

UNIQUE

• The UNIQUE operator searches every row of a specified table for uniqueness (no duplicates).

SQL - Expressions ✍

• SQL EXPRESSIONs are like formulae and they are written in query language. You can also use them to query the database for a specific set of data.

  SELECT column1, column2, columnN 
  FROM table_name 
  WHERE [CONDITION|EXPRESSION];
  

• There are different types of SQL expressions, which are mentioned below −

1) Boolean

2) Numeric

3) Date

Boolean Expressions

SELECT column1, column2, columnN 
FROM table_name 
WHERE SINGLE VALUE MATCHING EXPRESSION;

Numeric Expression

SELECT numerical_expression as  OPERATION_NAME
[FROM table_name
WHERE CONDITION] ;

Date Expressions

SELECT CURRENT_TIMESTAMP;

SELECT  GETDATE();

SQL - Using Joins ⭕

• The SQL Joins clause is used to combine records from two or more tables in a database.
• A JOIN is a means for combining fields from two tables by using values common to each.
• It is noticeable that the join is performed in the WHERE clause.
• Several operators can be used to join tables, such as =, <, >, <>, <=, >=, !=, BETWEEN, LIKE, and NOT; they can all be used to join tables.
• However, the most common operator is the equal to symbol.

There are different types of joins available in SQL −

1) INNER JOIN − returns rows when there is a match in both tables.

2) LEFT JOIN − returns all rows from the left table, even if there are no matches in the right table.

3) RIGHT JOIN − returns all rows from the right table, even if there are no matches in the left table.

4) FULL JOIN − returns rows when there is a match in one of the tables.

5) SELF JOIN − is used to join a table to itself as if the table were two tables, temporarily renaming at least one table in the SQL statement.

6) CARTESIAN JOIN − returns the Cartesian product of the sets of records from the two or more joined tables.

Various Syntax for Joins 🚨

1) INNER JOIN

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
INNER JOIN table2
ON table1.matching_column = table2.matching_column;


table1: First table.
table2: Second table
matching_column: Column common to both the tables.

2) LEFT JOIN

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
LEFT JOIN table2
ON table1.matching_column = table2.matching_column;


table1: First table.
table2: Second table
matching_column: Column common to both the tables.

3) RIGHT JOIN

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
RIGHT JOIN table2
ON table1.matching_column = table2.matching_column;


table1: First table.
table2: Second table
matching_column: Column common to both the tables.

4) FULL JOIN

SELECT table1.column1,table1.column2,table2.column1,....
FROM table1 
FULL JOIN table2
ON table1.matching_column = table2.matching_column;


table1: First table.
table2: Second table
matching_column: Column common to both the tables.

5) SELF JOIN

SELECT a.coulmn1 , b.column2
FROM table_name a, table_name b
WHERE some_condition;

table_name: Name of the table.
some_condition: Condition for selecting the rows.

6) CARTESIAN JOIN

SELECT table1.column1 , table1.column2, table2.column1...
FROM table1
CROSS JOIN table2;


table1: First table.
table2: Second table

SQL - UNIONS CLAUSE ✅

• The SQL UNION clause/operator is used to combine the results of two or more SELECT statements without returning any duplicate rows.
• To use this UNION clause, each SELECT statement must have

- The same number of columns selected

- The same number of column expressions

- The same data type and have them in the same order

UNION

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]
UNION
SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]

UNION ALL

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]
UNION ALL
SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]

INTERSECT

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]
INTERSECT
SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]

EXCEPT

SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]
EXCEPT
SELECT column1 [, column2 ]
FROM table1 [, table2 ]
[WHERE condition]

SQL - NULL Values ⛔

• SQL NULL is the term used to represent a missing value.

• A NULL value in a table is a value in a field that appears to be blank.

• A field with a NULL value is a field with no value.

• It is very important to understand that a NULL value is different than a zero value or a field that contains spaces.

  CREATE TABLE CUSTOMERS(
      ID   INT              NOT NULL,
      NAME VARCHAR (20)     NOT NULL,
      AGE  INT              NOT NULL,
      ADDRESS  CHAR (25) ,
      SALARY   DECIMAL (18, 2),       
      PRIMARY KEY (ID)
  );
  

SQL - Alias ✍

• The use of table aliases is to rename a table in a specific SQL statement.

• The renaming is a temporary change and the actual table name does not change in the database.

• The column aliases are used to rename a table's columns for the purpose of a particular SQL query.

  SELECT column1, column2....
  FROM table_name AS alias_name
  WHERE [condition];
  

SQL - Indexes ✌

• Indexes are special lookup tables that the database search engine can use to speed up data retrieval.
• Simply put, an index is a pointer to data in a table.
• An index in a database is very similar to an index in the back of a book.

The following guidelines indicate when the use of an index should be reconsidered.

- Indexes should not be used on small tables.

- Tables that have frequent, large batch updates or insert operations.

- Indexes should not be used on columns that contain a high number of NULL values.

- Columns that are frequently manipulated should not be indexed.

CREATE INDEX

CREATE INDEX index_name ON table_name;

Single-Column Indexes

• A single-column index is created based on only one table column.

  CREATE INDEX index_name
  ON table_name (column_name);
  

Unique Indexes

• Unique indexes are used not only for performance, but also for data integrity.

• A unique index does not allow any duplicate values to be inserted into the table.

  CREATE UNIQUE INDEX index_name
  on table_name (column_name);
  

Composite Indexes

• A composite index is an index on two or more columns of a table.

  CREATE INDEX index_name
  on table_name (column1, column2);
  

Implicit Indexes

• Implicit indexes are indexes that are automatically created by the database server when an object is created.
• Indexes are automatically created for primary key constraints and unique constraints.

DROP INDEX

DROP INDEX index_name;

SQL - ALTER TABLE ✍

• The SQL ALTER TABLE command is used to add, delete or modify columns in an existing table.
• You should also use the ALTER TABLE command to add and drop various constraints on an existing table.

New Column

ALTER TABLE table_name ADD column_name datatype;

DROP COLUMN

ALTER TABLE table_name DROP COLUMN column_name;

Change DATA TYPE

ALTER TABLE table_name MODIFY COLUMN column_name datatype;

NOT NULL

ALTER TABLE table_name MODIFY column_name datatype NOT NULL;

ADD UNIQUE CONSTRAINT