This part of the course teaches how to analyze query performance and recognize patterns or pitfalls that can lead to slower queries. It focuses on understanding and interpreting execution plans and optimizing indexes.

A. Analyzing Query Execution Plans

• 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.

B. Identifying Inefficiencies

• 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';
  

C. Best Practices for Query Optimization in Large Databases

• 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.