3.4 SQL and Database Queries

SQL is the backbone of database management, letting you create, modify, and query data with precision. It's like having a superpower to control vast amounts of information effortlessly.

In this section, we dive into SQL's structure, key commands, and advanced techniques. From basic SELECT statements to complex JOINs and optimizations, you'll learn how to wield SQL like a pro in managing databases.

Data Definition and Manipulation

SQL Fundamentals and Structure

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• SQL (Structured Query Language) manages and manipulates relational databases, encompassing Data Definition Language (DDL) and Data Manipulation Language (DML) statements
• Proper syntax and structure of SQL statements require semicolons to terminate statements and correct ordering of clauses
• Understanding data types (INTEGER, VARCHAR, DATE) and constraints (PRIMARY KEY, FOREIGN KEY, NOT NULL) enables effective creation and manipulation of database objects
• SQL statements follow a general structure:

  COMMAND [OPTION] [TARGET] [ACTION] [CONDITION];

CREATE and INSERT Statements

• CREATE statement defines new database objects (tables, views, indexes) specifying structure and constraints
  • Example:

    CREATE TABLE Customers (CustomerID INT PRIMARY KEY, Name VARCHAR(50), Email VARCHAR(100));

• INSERT statement adds new records or rows into existing tables
  • Single row insertion:

    INSERT INTO Customers (CustomerID, Name, Email) VALUES (1, 'John Doe', 'john@example.com');

  • Multiple row insertion:

    INSERT INTO Customers (CustomerID, Name, Email) VALUES (2, 'Jane Smith', 'jane@example.com'), (3, 'Bob Johnson', 'bob@example.com');

UPDATE and DELETE Statements

• UPDATE statement modifies existing data within a table based on specified conditions
  • Example:

    UPDATE Customers SET Email = 'newemail@example.com' [WHERE](https://www.fiveableKeyTerm:where) CustomerID = 1;

• DELETE statement removes records from a table based on criteria or deletes all records if no condition specified
  • Example with condition:

    DELETE FROM Customers WHERE CustomerID = 2;

  • Example without condition (deletes all records):

    DELETE FROM Customers;

Data Retrieval with SQL

Basic SELECT and WHERE Clauses

• SELECT statement forms foundation of data retrieval specifying columns to retrieve from tables
  • Example:

    SELECT Name, Email FROM Customers;

• WHERE clause filters data based on specified conditions retrieving rows meeting criteria
  • Example:

    SELECT * FROM Customers WHERE Name LIKE 'J%';

• Comparison operators (=, <>, <, >, <=, >=) and logical operators (AND, OR, NOT) create complex conditions
  • Example:

    SELECT * FROM Products WHERE Price > 50 AND Category = 'Electronics';

JOINs and Table Relationships

• JOINs combine rows from multiple tables based on related columns
• Common JOIN types include INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
  • INNER JOIN example:

    SELECT Orders.OrderID, Customers.Name FROM Orders INNER JOIN Customers ON Orders.CustomerID = Customers.CustomerID;

  • LEFT JOIN example:

    SELECT Customers.Name, Orders.OrderID FROM Customers LEFT JOIN Orders ON Customers.CustomerID = Orders.CustomerID;

• Subqueries nested within main queries perform complex data retrieval operations
  • Example:

    SELECT Name FROM Customers WHERE CustomerID IN (SELECT CustomerID FROM Orders WHERE TotalAmount > 1000);

Advanced Querying Techniques

• ORDER BY clause sorts result set in ascending or descending order based on columns
  • Example:

    SELECT * FROM Products ORDER BY Price DESC;

• GROUP BY clause groups rows with same values in specified columns often used with aggregate functions
  • Example:

    SELECT Category, [COUNT](https://www.fiveableKeyTerm:count)(*) AS ProductCount FROM Products GROUP BY Category;

• HAVING clause specifies search condition for groups or aggregates filtering grouped results
  • Example:

    SELECT Category, [AVG](https://www.fiveableKeyTerm:avg)(Price) AS AvgPrice FROM Products GROUP BY Category HAVING AVG(Price) > 100;

SQL Functions and Aggregations

Built-in SQL Functions

• SQL provides wide range of functions for manipulating and analyzing data
• String functions modify or analyze text data
  • Example:

    SELECT UPPER(Name) AS UppercaseName, LENGTH(Email) AS EmailLength FROM Customers;

• Date functions manipulate and calculate dates and times
  • Example:

    SELECT OrderID, OrderDate, DATEDIFF(day, OrderDate, GETDATE()) AS DaysSinceOrder FROM Orders;

• Numeric functions perform mathematical operations and calculations
  • Example:

    SELECT ProductName, Price, ROUND(Price * 0.9, 2) AS DiscountedPrice FROM Products;

Aggregate Functions and Analysis

• Aggregate functions perform calculations on sets of values returning single results
• Common aggregate functions include COUNT(), SUM(), AVG(), MAX(), MIN()
  • Example:

    SELECT COUNT(*) AS TotalCustomers, AVG(TotalPurchases) AS AveragePurchase FROM Customers;

• DISTINCT keyword with aggregate functions performs calculations on unique values
  • Example:

    SELECT COUNT(DISTINCT Category) AS UniqueCategories FROM Products;

• Window functions enable calculations across row sets related to current row
  • Example:

    SELECT ProductName, Price, AVG(Price) OVER (PARTITION BY Category) AS AvgCategoryPrice FROM Products;

Custom Functions and Conditional Processing

• User-defined functions (UDFs) extend SQL functionality performing custom calculations or data manipulations
  • Example:

    CREATE FUNCTION dbo.CalculateDiscount(@price DECIMAL(10,2), @discountRate DECIMAL(5,2)) RETURNS DECIMAL(10,2) AS BEGIN RETURN @price * (1 - @discountRate) END;

• CASE statement allows conditional processing in SQL queries enabling different results based on conditions
  • Example:

    SELECT ProductName, Price, CASE WHEN Price < 50 THEN 'Budget' WHEN Price < 100 THEN 'Mid-range' ELSE 'Premium' END AS PriceCategory FROM Products;

• NULL values impact function results requiring careful handling for accurate analysis
  • Example:

    SELECT AVG(COALESCE(Salary, 0)) AS AverageSalary FROM Employees;

Query Optimization

Query Execution and Analysis

• Query execution plans provide insight into database engine query processing identifying performance bottlenecks
• EXPLAIN or similar tools analyze query execution providing valuable optimization information
  • Example:

    EXPLAIN SELECT * FROM Orders WHERE CustomerID = 1000;

• Regular database maintenance including updating statistics and reorganizing indexes maintains optimal query performance

Indexing and Performance Strategies

• Proper indexing strategies significantly improve query performance
  • Example:

    CREATE INDEX idx_CustomerID ON Orders (CustomerID);

• Avoid using wildcard characters (%) at beginning of LIKE patterns improving string search efficiency
  • Efficient:

    SELECT * FROM Customers WHERE LastName LIKE 'Smith%';

  • Less efficient:

    SELECT * FROM Customers WHERE LastName LIKE '%Smith';

• Limit use of subqueries favoring JOINs for better query performance when possible
• Table partitioning and materialized views improve performance for large datasets or complex analytical queries
  • Example of table partitioning:

    CREATE TABLE Sales (SaleID INT, SaleDate DATE, Amount DECIMAL(10,2)) PARTITION BY RANGE (YEAR(SaleDate));