Big Data and AI

Chapter 15: Performance Tuning & Best Practices When your SQL queries work correctly but run painfully slow on large tables, it’s time to diagnose, optimize, and monitor. This chapter covers the full lifecycle of performance tuning: reading execution plans, spotting bottlenecks, rewriting queries, tuning indexes, and tracking metrics over time. Whether you manage a handful of gigabytes or petabytes of data, these techniques will elevate your SQL from functional to blazing fast. 1. Reading and Interpreting Execution Plans Execution plans reveal how the database engine executes your queries. They show join strategies, index usage, sort operations, and estimated vs. actual row counts. Learning to read them is the first step toward optimization. 1.1 Generating an Execution Plan PostgreSQL : sql EXPLAIN ANALYZE SELECT * FROM orders WHERE order_date > '2025-01-01'; MySQL : sql EXPLAIN FORMAT=JSON SELECT * FROM orders WHERE order_date > '2025-01-01'; SQL Server : sql...

  Chapter 14: Transactions and Concurrency In a multiuser environment, concurrent access to the same data can lead to inconsistencies, lost updates, and other anomalies. Transactions and concurrency control ensure that your database remains accurate, reliable, and performant even under heavy load. In this chapter you’ll explore: The ACID properties: Atomicity, Consistency, Isolation, Durability Transaction control commands and best practices Isolation levels (READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, SERIALIZABLE) Techniques for detecting and resolving deadlocks Strategies for building robust, concurrent applications 1. The ACID Properties ACID defines the four guarantees every transaction must uphold: Atomicity A transaction is an all-or-nothing unit. Either every operation succeeds, or all effects are rolled back on failure. Consistency A transaction transforms the database from one valid state to another, respecting all schema constraints, triggers, and business r...

  Chapter 13: Views, Stored Procedures, and Functions As your database needs grow more sophisticated, embedding business logic and reusable patterns directly into the database saves time, reduces errors, and simplifies application code. In this chapter, you’ll learn how to: Define views to encapsulate complex queries and standardize data access Write stored procedures that automate multi-step tasks and maintain transactional integrity Create user-defined functions for reusable calculations and transformations Manage parameters , implement error handling , and assign permissions for safe, controlled execution Harnessing these programmability features turns your database from a simple data store into a powerful, self-documenting service layer. 1. Defining Views for Reusable Query Patterns A view is a virtual table—essentially a named SELECT statement—that you can query as if it were a real table. Views simplify queries, enforce security, and provide a stable interface when unde...

  By now, you’ve mastered the foundations of SQL—from basic queries to table design. In this final module, we’ll dive into three advanced pillars that elevate your database skills: Views, Stored Procedures, and Functions Transactions and Concurrency Performance Tuning & Best Practices These topics empower you to encapsulate logic, ensure data integrity under load, and optimize queries for real-world workloads. Views, Stored Procedures, and Functions Encapsulating complex logic in the database makes your applications cleaner and more maintainable. Views A view is a virtual table defined by a SQL query. Use views to: Simplify joins and aggregations into a single, reusable object Enforce data security by exposing only selected columns Provide backward compatibility when underlying schemas change Example: sql CREATE VIEW sales_summary AS SELECT product_id, DATE_TRUNC('month', sale_date) AS sale_month, SUM(quantity * price) AS total_revenue FROM sales GROUP BY product_id,...