Overview of the SQL Query Language

     

    Structured Query Language (SQL) is the standard language for managing and manipulating relational databases. SQL allows users to create, read, update, and delete (CRUD) data stored in a relational database. SQL commands can be categorized into several types: Data Query Language (DQL), Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL).

    SQL Data Definition

     

    Data Definition Language (DDL) includes commands that define the structure of the database schema:

    CREATE :

    This command helps in creating the new database, new table, table view, and other objects of the database.

    UPDATE :

    This command helps in updating or changing the stored data in the database.

    DELETE :

    This command helps in removing or erasing the saved records from the database tables. It erases single or multiple tuples from the tables of the database.

    SELECT :

    This command helps in accessing the single or multiple rows from one or multiple tables of the database. We can also use this command with the WHERE clause.

    DROP : 

    This command helps in deleting the entire table, table view, and other objects from the database.

    INSERT :

    This command helps in inserting the data or records into the database tables. We can easily insert the records in single as well as multiple rows of the table.

     

    Advantages of SQL

    1. No Programming Needed: Simple syntax with minimal coding.
    2. High-Speed Query Processing: Fast data retrieval and manipulation.
    3. Standardized Language: Follows ISO and ANSI standards for consistency.
    4. Portability: Usable on various devices and integrates with other applications.
    5. Interactive Language: Easy to learn and use, handles complex queries.
    6. Multiple Data Views: Allows creating different views for various users.

    Disadvantages of SQL

    1. Cost: Some versions can be expensive.
    2. Complex Interface: Difficult for beginners to manage.
    3. Partial Database Control: Users may not have full control over database rules and logic.

    Basic Structure of SQL Queries 

    The basic structure of an SQL query includes the following keywords:

    1. SELECT: This is the first and mandatory keyword in an SQL query. It specifies the columns or expressions you want to retrieve from the database.

    2. FROM: After the SELECT keyword, you use FROM to indicate which table or tables the data should be retrieved from.

    3. WHERE: This is an optional clause that allows you to filter the rows returned by the query based on specified conditions. It follows the FROM clause.

    4. GROUP BY: This clause is used to group rows that have the same values in specified columns into summary rows. It is often used in conjunction with aggregate functions like COUNT(), SUM(), AVG(), etc.

    5. HAVING: Similar to the WHERE clause, HAVING is used specifically with the GROUP BY clause to filter groups based on specified conditions.

    6. ORDER BY: This clause is used to sort the result set in ascending (ASC) or descending (DESC) order based on specified columns.

    Example of the basic structure:

    In this example:

    • SELECT specifies the columns to retrieve.
    • FROM specifies the table from which to retrieve the data.
    • WHERE filters the rows based on a condition.
    • GROUP BY groups rows with the same values in column1.
    • HAVING further filters groups based on a condition.
    • ORDER BY sorts the result set in ascending order based on column1.

    Set Operations in SQL

     

    Set operations in SQL allow combining the results of two or more queries into a single result set. These operations are useful for performing complex queries across multiple tables or result sets. The primary set operations in SQL are UNION, UNION ALL, INTERSECT, and EXCEPT.

    1. UNION:

      • Combines the results of two queries and removes duplicate rows from the result set.
      • Useful when you want a unified set of results from multiple queries without duplicates.
      • Both queries must have the same number of columns and compatible data types.

    2. UNION ALL:

      • Combines the results of two queries and includes all duplicate rows.
      • Useful when you want a unified set of results from multiple queries with all duplicates preserved.
      • Both queries must have the same number of columns and compatible data types.

    3. INTERSECT:

      • Returns only the rows that are common to the results of both queries.
      • Useful for finding common elements between two datasets.
      • Both queries must have the same number of columns and compatible data types.

    4. EXCEPT:

      • Returns the rows from the first query that are not present in the second query.
      • Useful for finding elements that exist in one dataset but not in another.
      • Both queries must have the same number of columns and compatible data types.

    Key Points:

    • UNION and UNION ALL merge result sets vertically.
    • INTERSECT finds common rows between result sets.
    • EXCEPT finds rows in one result set not present in another.
    • Each operation requires that the participating queries have matching columns in terms of number and data types.

     

    Null Values

     

    Null Values in SQL represent missing or unknown data. They are used when a data entry is not applicable or is yet to be determined. Key points about null values:

    • IS NULL: A condition used to check for null values.
    • IS NOT NULL: A condition used to check for non-null values.
    • Handling Nulls: Functions and operations often need to explicitly handle nulls to avoid unexpected results, as nulls can affect the outcome of expressions and comparisons.

    Aggregate Functions

     

    Aggregate Functions perform calculations on a set of values and return a single value. Common aggregate functions include:

    • COUNT(): Counts the number of rows.
    • SUM(): Calculates the total sum of a numeric column.
    • AVG(): Computes the average of a numeric column.
    • MIN(): Finds the minimum value in a column.
    • MAX(): Finds the maximum value in a column.
    • GROUP BY: Often used with aggregate functions to group the result set by one or more columns.

    Nested Sub-queries

     

    Nested Sub-queries are queries within another SQL query. They are used to perform complex queries by breaking them down into simpler sub-queries.

    • Sub-query in SELECT: Used to return a single value that is used in the main query.
    • Sub-query in FROM: Acts as a derived table or a temporary result set.
    • Sub-query in WHERE: Used to filter records based on the result of another query.
    • Correlated Sub-query: A sub-query that references columns from the outer query.

    Modification of the Database

     

    Modification of the Database involves operations that change the data within the database. These include:

    • INSERT: Adds new records to a table.
      • Syntax: INSERT INTO table_name (column1, column2) VALUES (value1, value2);
    • UPDATE: Modifies existing records in a table.
      • Syntax: UPDATE table_name SET column1 = value1 WHERE condition;
    • DELETE: Removes records from a table.
      • Syntax: DELETE FROM table_name WHERE condition;

    These operations are essential for maintaining and updating the data in a relational database system.