Clarity and Readability:
Write SQL statements that are not only correct but also easy to read and maintain. For instance:

SELECT employee_id, first_name, last_name 
FROM employees 
WHERE department_id = 10;
      

Instead of:

SELECT e_id, fname, lname 
FROM employees 
WHERE d_id = 10;
      

Appropriate Use of Aliases:
Use column and table aliases to improve readability, especially in complex queries:

SELECT e.employee_id, e.first_name, e.last_name, d.department_name
FROM employees AS e
JOIN departments AS d ON e.department_id = d.department_id;

Consistent Formatting:
Adopt a consistent formatting style for indentation, capitalization of SQL keywords, and alias naming. This not only aids review by others but can also reduce errors when modifying queries.

Parameterization:
Use parameterized queries to avoid SQL injection and to allow query caching by the database engine.

Subqueries:
Subqueries can be used in SELECT, FROM, or WHERE clauses. They are useful when you need to first compute a result before performing a join or filter operation. For example:

SELECT employee_id, first_name, last_name
FROM employees
WHERE department_id IN (SELECT department_id FROM departments WHERE location_id = 1700);

Use Cases and Considerations:

• When the subquery returns a single or a small dataset.
• When logical separation improves clarity, but know that some complex subqueries might be less performant than a well-indexed JOIN.

JOINs:
JOINs are generally more performant as they allow the use of indexes and are generally optimized by the DBMS. For example, instead of using a subquery:

SELECT e.employee_id, e.first_name, e.last_name
FROM employees AS e
JOIN departments AS d ON e.department_id = d.department_id
WHERE d.location_id = 1700;

Use Cases and Considerations:

• When merging rows from two or more tables efficiently is required.
• When relational integrity must be enforced by retrieving data from multiple tables.

Avoid SELECT *:
Only retrieve the columns you need. This minimizes I/O and reduces memory consumption.

-- Problematic approach:
SELECT * FROM orders;

-- Optimized approach:
SELECT order_id, order_date, customer_id FROM orders;

Filtering Early:
Use filters (WHERE clauses) as early as possible in your queries to reduce the number of rows processed:

SELECT order_id, order_date
FROM orders
WHERE order_date >= '2023-01-01';

Index Usage:
Ensure that indexed columns appear in JOIN conditions and WHERE filters to take advantage of faster data access.

Using Derived Tables and Common Table Expressions (CTEs): Recursive Queries and Modular Query Design

• Introduction to CTEs:
  CTEs improve the readability and maintainability of SQL queries. They provide a way to define temporary result sets that can be referenced within a SELECT, INSERT, UPDATE, or DELETE statement.

• Non-Recursive CTE Example:

  WITH TopOrders AS (
      SELECT order_id, amount
      FROM orders
      WHERE amount > 1000
  )
  SELECT * FROM TopOrders;
  

• Recursive CTE Example:
  Recursive queries are particularly useful for hierarchical data (e.g., organizational charts, bill of materials).

  WITH EmployeeHierarchy AS (
      SELECT employee_id, manager_id, first_name, last_name, 0 AS level
      FROM employees
      WHERE manager_id IS NULL
      
      UNION ALL
      
      SELECT e.employee_id, e.manager_id, e.first_name, e.last_name, eh.level + 1
      FROM employees e
      JOIN EmployeeHierarchy eh ON e.manager_id = eh.employee_id
  )
  SELECT * FROM EmployeeHierarchy;
  

• Modular Query Design:
  CTEs enable decomposition of complex queries into smaller, logical units that can be developed, tested, and optimized individually.

Understanding the Execution Plan:
Every SQL engine can provide a detailed plan outlining how it intends to execute the query. For example, using the EXPLAIN keyword in MySQL or PostgreSQL:

EXPLAIN SELECT employee_id, first_name, last_name
FROM employees
WHERE department_id = 10;

Key areas to examine:

• Join methods: Nested loops, hash joins, merge joins.
• Index utilization: Whether indexes are used or a full table scan is occurring.
• Cost estimates: Estimated time and resource usage.

Example:
A query performing a full table scan vs. one using an index:

• Without Index:

  EXPLAIN SELECT * FROM orders WHERE customer_id = 12345;
  

• With Index on customer_id:

  CREATE INDEX idx_customer ON orders(customer_id);
  EXPLAIN SELECT * FROM orders WHERE customer_id = 12345;
  

The revised execution plan would show an Index Seek, significantly reducing the scanning of unnecessary rows.

Full Table Scans:
Recognize queries where the engine is forced to read every row. Use indexes to target specific rows:

-- Problematic if table is large:
SELECT * FROM orders WHERE order_status = 'Completed';

-- Optimized if order_status is indexed:
CREATE INDEX idx_order_status ON orders(order_status);

Missing or Inefficient Indexes:
Learn how to identify when indexes should be added or modified. Use hints provided by the execution plan to decide if composite indexes are necessary for queries with multiple predicates.

Overuse of Subqueries and Redundant Joins:
Overcomplicated query logic can result in multiple subqueries which could be consolidated:

-- Consolidated query using JOINs or CTEs may perform better
WITH ActiveCustomers AS (
    SELECT customer_id FROM customers WHERE status = 'Active'
)
SELECT o.order_id, o.order_date
FROM orders AS o
JOIN ActiveCustomers ac ON o.customer_id = ac.customer_id
WHERE o.order_date >= '2023-01-01';

Partitioning Tables:
Break large tables into partitions based on time, region, or other criteria to improve query performance.

Regular Maintenance:
Update statistics, rebuild indexes, and perform periodic clean-ups.

Caching Strategies:
Use caching for frequently executed queries or results to reduce repetitive computation.

Query Parameterization:
Avoid dynamic SQL where possible. Instead, utilize stored procedures and parameterized queries to benefit from plan caching.

Encapsulation of Business Logic:
Stored procedures allow you to encapsulate complex business logic on the server side. This centralizes the logic and can reduce network traffic by executing multiple operations in a single call.

CREATE PROCEDURE GetEmployeeDetails
    @DepartmentID INT
AS
BEGIN
    SELECT employee_id, first_name, last_name
    FROM employees
    WHERE department_id = @DepartmentID;
END;

Performance and Security Benefits:

• Precompiled Execution: Stored procedures are often precompiled or cached, which speeds up their execution.
• Reduced risk of SQL injection because the input parameters are handled safely.
• Easier to maintain and update business logic as it's centralized in the procedure rather than across multiple applications.

Use Cases:
Triggers automatically execute in response to specific events (INSERT, UPDATE, DELETE). They are useful for auditing changes or enforcing business rules.

CREATE TRIGGER trg_AuditEmployeeUpdate
ON employees
AFTER UPDATE
AS
BEGIN
    INSERT INTO employee_audit (employee_id, change_date, changed_by)
    SELECT employee_id, GETDATE(), SYSTEM_USER
    FROM inserted;
END;

Best Practices:

• Keep triggers lightweight. Heavy logic can slow down the DML operations.
• Avoid cascading triggers that call other triggers unless absolutely necessary.
• Document triggers well so that their side-effects are clear to developers and DBAs.

Definition and Use-Cases:

Window functions (also known as analytic functions) allow you to perform calculations across a set of related rows, or a “window” of rows, without collapsing them into a single output row. Unlike aggregate functions (e.g., SUM, AVG) used with GROUP BY, window functions preserve each row and add analytical context to it.

They are especially useful in reporting, analytics, and trend analysis, where row-by-row comparisons and cumulative computations are common.

• PARTITION BY: Divides the result set into partitions to perform the window function independently in each.

• ORDER BY: Determines the order of rows within the window.

• ROWS BETWEEN: Specifies a window frame relative to the current row (e.g., 2 rows before to current).

Common Use-Cases and Examples:

Query:

SELECT order_id, order_date, amount,
       SUM(amount) OVER (ORDER BY order_date) AS RunningTotal
FROM orders;

Input:

Output:

Query:

SELECT order_id, order_date, amount,
       AVG(amount) OVER (
           ORDER BY order_date
           ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
       ) AS MovingAvg
FROM orders;

Input:

Output:

Moving average is calculated over the current row and two before it (3 values total, or fewer if not available).

Query:

SELECT employee_id, department, salary,
       RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS SalaryRank
FROM employees;

Input:

Output:

RANK() assigns the same rank to equal salaries and skips the next rank accordingly.

Query:

SELECT order_id, order_date, amount,
       LAG(amount) OVER (ORDER BY order_date) AS PrevAmount,
       LEAD(amount) OVER (ORDER BY order_date) AS NextAmount
FROM orders;

Input:

Output:

Benefits:

• • Reduces the need for self-joins or subqueries.
  • Enhances performance by letting the DBMS optimize the window operations.