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SQL Instructional Manual


You can import CSV files into tables using MySQL Workbench. Here's a step-by-step guide on how to do it:

Importing CSV Files into Tables using MySQL Workbench:

Open MySQL Workbench and connect to your database.
In the Navigator pane on the left, right-click on the schema (database) where you want to import the table, and select Set as Default Schema.
Click on the Server tab in the main menu and select Data Import.
In the Data Import/Restore view, select Import from Self-Contained File. Then, click on the ellipsis button (...) to navigate and select your CSV file.
Under Default Target Schema, select the schema (database) where you want to import the table.
Under Format of Imported Data, choose CSV.
In the Tables to Import section, you'll see the tables detected in the CSV file. Select the tables you want to import.
Click on the Start Import button at the bottom right to begin the import process.
Once the import is complete, you should see the imported table in the selected schema, and you can start querying it.
Note: Before importing, ensure that the CSV file is formatted correctly and matches the structure of the table you're importing into (or create a new table structure based on the CSV). It's also a good idea to back up your database before performing any import operations to prevent any unintended data loss or corruption.
If you're creating a new table from the CSV, you might need to adjust data types, set primary keys, or make other modifications after the import to ensure the table structure is optimal for your use case.

Introduction: Programming Relational Databases with SQL

Structured Query Language (SQL) is a domain-specific language used to manage and query data in relational databases.
step 1:
Start MySql Server Start your Mysql Workbench
step 2: find a database to connect to SHOW DATABASES
use <databasename>
** then you can start doing SQL stuff

Table of Contents

Basics
Show Databases
DROP Database Databasename
Show Tables
DROP Table Tablename
Data Query Language (DQL)
SELECT
JOINs
Subqueries
SORT
GROUP BY
Aggregation Functions
Data Manipulation Language (DML)
CRUD Operations
Data Control Language (DCL)
GRANT
REVOKE
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1. Basics

Show Databases

To view all databases:
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SHOW DATABASES;

Show Tables

To view all tables in a specific database:
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USE your_database_name;
SHOW TABLES;

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To list the fields (columns) of a table in MySQL, you can query the INFORMATION_SCHEMA.COLUMNS table. Here's how you can do it:

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SELECT COLUMN_NAME
FROM INFORMATION_SCHEMA.COLUMNS
WHERE TABLE_SCHEMA = 'your_database_name'
AND TABLE_NAME = 'your_table_name';

Replace your_database_name with the name of your database and your_table_name with the name of your table.
This SQL query will return a list of column names for the specified table in the specified database.
If you're using MySQL Workbench, you can also see the fields of a table by navigating to the table in the Navigator pane and clicking on it. The columns (fields) of the table will be displayed in the main pane.

2. Data Query Language (DQL)

SELECT

To retrieve all columns from a table:
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SELECT * FROM table_name;

JOINs

INNER JOIN

Returns rows when there is a match in both tables.
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SELECT A.column1, B.column2
FROM tableA A
INNER JOIN tableB B ON A.common_column = B.common_column;

LEFT JOIN (or LEFT OUTER JOIN)

Returns all rows from the left table, and the matched rows from the right table.
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SELECT A.column1, B.column2
FROM tableA A
LEFT JOIN tableB B ON A.common_column = B.common_column;

RIGHT JOIN (or RIGHT OUTER JOIN)

Returns all rows from the right table, and the matched rows from the left table.
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SELECT A.column1, B.column2
FROM tableA A
RIGHT JOIN tableB B ON A.common_column = B.common_column;

FULL JOIN (or FULL OUTER JOIN)

Returns rows when there is a match in one of the tables.
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SELECT A.column1, B.column2
FROM tableA A
FULL OUTER JOIN tableB B ON A.common_column = B.common_column;

Subqueries

Correlated Subqueries

A correlated subquery is a subquery that uses values from the outer query.
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SELECT column1, column2
FROM tableA A
WHERE column1 = (SELECT column1 FROM tableB B WHERE B.column2 = A.column2);

SORT

To sort results by a specific column:
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SELECT column1, column2
FROM table_name
ORDER BY column1 ASC; -- or DESC for descending

GROUP BY

Groups rows that have the same values in specified columns.
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SELECT column1, COUNT(column2)
FROM table_name
GROUP BY column1;

Aggregation Functions

COUNT

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SELECT COUNT(column_name)
FROM table_name;

SUM

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SELECT SUM(column_name)
FROM table_name;

AVG

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SELECT AVG(column_name)
FROM table_name;

MAX

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SELECT MAX(column_name)
FROM table_name;

MIN

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SELECT MIN(column_name)
FROM table_name;

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3. Data Manipulation Language (DML)

CRUD Operations

CREATE

Inserts new data into a table.
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INSERT INTO table_name (column1, column2)
VALUES (value1, value2);

READ

Queries data from a table.
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SELECT column1, column2
FROM table_name;

UPDATE

Modifies existing data in a table.
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UPDATE table_name
SET column1 = value1, column2 = value2
WHERE condition;

DELETE

Removes data from a table.
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DELETE FROM table_name
WHERE condition;

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4. Data Control Language (DCL)

GRANT

Gives user-specific privileges to a database.
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GRANT privilege_name
ON object_name
TO user_name;

REVOKE

Removes user-specific privileges.
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REVOKE privilege_name
ON object_name
FROM user_name;

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This is a basic overview of SQL commands and their functionalities. Depending on the database system you're using (e.g., MySQL, PostgreSQL, SQL Server), there might be slight variations or additional features. Always refer to the official documentation of the specific database system for comprehensive details.
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what other sql queries can I include
ChatGPT
Certainly! SQL is a vast language with many functionalities. Here are some additional SQL queries and concepts you might consider including in your instructional manual:
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Data Definition Language (DDL)

CREATE TABLE

Creates a new table.
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CREATE TABLE table_name (
column1 datatype1,
column2 datatype2,
...
);

ALTER TABLE

Modifies an existing table.
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ALTER TABLE table_name
ADD column_name datatype;

DROP TABLE

Deletes an existing table.
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DROP TABLE table_name;

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Advanced Query Techniques

DISTINCT

Eliminates duplicate rows from the result.
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SELECT DISTINCT column_name
FROM table_name;

LIMIT

Limits the number of rows returned.
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SELECT column_name
FROM table_name
LIMIT number;

UNION

Combines the result of two or more SELECT statements.
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SELECT column_name FROM table1
UNION
SELECT column_name FROM table2;

CASE

Adds conditions within the query.
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SELECT column1,
CASE
WHEN condition1 THEN result1
WHEN condition2 THEN result2
ELSE result3
END AS new_column_name
FROM table_name;

Common Table Expressions (CTE)

Temporary result set that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement.
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WITH CTE_name AS (
SELECT column1, column2
FROM table_name
)
SELECT * FROM CTE_name;

Window Functions

Operate on a set of rows and return a single aggregated value for each row.
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SELECT column1,
SUM(column2) OVER (PARTITION BY column3) AS new_column_name
FROM table_name;

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Data Integrity

Primary Key

Uniquely identifies each record in a table.
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CREATE TABLE table_name (
column1 datatype1 PRIMARY KEY,
column2 datatype2,
...
);

Foreign Key

Ensures referential integrity of data between two tables.
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CREATE TABLE table_name (
column1 datatype1,
column2 datatype2,
FOREIGN KEY (column1) REFERENCES other_table(other_column),
...
);

CHECK Constraint

Ensures that all values in a column satisfy a specific condition.
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CREATE TABLE table_name (
column1 datatype1 CHECK (condition),
...
);

INDEX

Improves the speed of data retrieval operations on a database table.
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CREATE INDEX index_name
ON table_name (column_name);

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Transactions

BEGIN, COMMIT, and ROLLBACK

Manages changes made by DML statements.
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BEGIN;
... -- SQL statements
COMMIT; -- or ROLLBACK;

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Triggers

CREATE TRIGGER

Automatically invokes a function when a specific event occurs.
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CREATE TRIGGER trigger_name
AFTER INSERT ON table_name
FOR EACH ROW
BEGIN
... -- SQL statements
END;

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Stored Procedures

CREATE PROCEDURE

Groups one or more SQL statements into a single procedure.
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CREATE PROCEDURE procedure_name()
BEGIN
... -- SQL statements
END;

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These are just a few additional SQL concepts and queries you can include. Depending on the depth and focus of your course, you might decide to delve deeper into some topics or introduce others like views, sequences, or database normalization techniques.
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discuss views with examples
ChatGPT
Certainly! Let's dive into views in SQL.

Views

A view is a virtual table based on the result-set of an SQL statement. It contains rows and columns, just like a real table. The fields in a view are fields from one or more real tables in the database. A view can be thought of as a saved SQL query or a virtual table, and it does not physically store data. Instead, it provides a way to access data from one or more tables in a more simplified or structured manner.

Advantages of Views:

Simplification: Views can simplify complex queries. Instead of writing a complex query every time, you can create a view and select data from the view.
Security: Views can be used to restrict access to sensitive data in tables. You can allow users to access the view but not the underlying tables.
Logical Structure: Views can present a different logical structure than the underlying tables, making data access more intuitive.
Consistency: If the structure of the underlying tables changes, views can remain consistent, ensuring that applications that rely on them don't break.

Examples:

1. Creating a View:

Suppose we have a table named employees with the following columns: id, first_name, last_name, department, and salary. If we want to create a view that shows only the names and departments of employees, we can do the following:

CREATE VIEW view_employee_names_departments AS
SELECT first_name, last_name, department
FROM employees;

Now, you can query the view just like you would a table:
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SELECT * FROM view_employee_names_departments;

2. Updating a View:

You can modify a view using the CREATE OR REPLACE VIEW statement. For instance, if you want to add the salary column to the above view:
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CREATE OR REPLACE VIEW view_employee_names_departments AS
SELECT first_name, last_name, department, salary
FROM employees;

3. Dropping a View:

To remove a view, you can use the DROP VIEW statement:
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DROP VIEW view_employee_names_departments;

4. Using Views for Aggregation:

Suppose you want a view that shows the average salary for each department:
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CREATE VIEW view_avg_salary_by_department AS
SELECT department, AVG(salary) AS avg_salary
FROM employees
GROUP BY department;

You can then query this view to get the average salary for each department:
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SELECT * FROM view_avg_salary_by_department;

Limitations:

Not all views are updatable. Depending on the database system and the complexity of the view, you might not be able to execute INSERT, UPDATE, or DELETE statements against a view.
Performance can be a concern. Since views are virtual tables, querying a view can sometimes be slower than querying the underlying tables directly, especially if the view is based on complex queries or joins multiple tables.
In conclusion, views are a powerful feature in SQL that can simplify data access, enhance security, and provide a consistent interface to data. However, they should be used judiciously, keeping in mind their limitations and potential performance implications.
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have you included contains and like
ChatGPT
I haven't covered the LIKE and CONTAINS operators in the previous sections. Let's delve into them now.

LIKE Operator

The LIKE operator is used in a WHERE clause to search for a specified pattern in a column. There are two wildcards often used in conjunction with the LIKE operator:
%: Represents zero or more characters.
_: Represents a single character.

Examples:

Find all employees with a first name starting with 'Jo':
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SELECT * FROM employees WHERE first_name LIKE 'Jo%';
Find all employees with a first name that has 'an' in any position:
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SELECT * FROM employees WHERE first_name LIKE '%an%';
Find all employees with a first name that is five characters long and ends with 'y':
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SELECT * FROM employees WHERE first_name LIKE '____y';

CONTAINS Operator

The CONTAINS operator is used for full-text search in some database systems like SQL Server. It searches for precise or fuzzy (less precise) matches to single words and phrases based on the full-text index.

Example:

Find all articles that contain the word 'database':
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SELECT * FROM articles WHERE CONTAINS(article_content, 'database');
Find all articles that contain the phrase 'relational database':
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SELECT * FROM articles WHERE CONTAINS(article_content, '"relational database"');
Note: The availability and exact syntax of the CONTAINS operator might vary depending on the database system. For instance, CONTAINS is specific to SQL Server's full-text search, while other databases might have their own mechanisms or functions for full-text search.
When using these operators, it's essential to be aware of potential performance implications, especially when dealing with large datasets. Proper indexing and understanding the specifics of the database system in use can help optimize query performance.
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