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Database Optimization

Master SQL Query Optimization & Performance

Master SQL Query Optimization & Performance

Transform slow databases into high-performance systems. Learn query optimization techniques, indexing strategies, performance tuning, and scaling methods for production environments.

Query Optimization
Indexing Strategies
Performance Tuning

Query Optimization Example: Before & After

-- BEFORE: Slow Query with Full Table Scan
SELECT *  -- Bad: Selecting all columns
FROM orders o
JOIN customers c ON o.customer_id = c.id
WHERE YEAR(o.order_date) = 2024
AND c.country = 'USA'
ORDER BY o.total_amount DESC;

-- AFTER: Optimized Query
CREATE INDEX idx_orders_date_amount ON orders(order_date, total_amount);
CREATE INDEX idx_customers_country ON customers(country);

EXPLAIN ANALYZE  -- Use to analyze query plan
SELECT o.id, o.order_date, o.total_amount, c.name
FROM orders o
INNER JOIN customers c ON o.customer_id = c.id
WHERE o.order_date >= '2024-01-01'
AND o.order_date < '2025-01-01'
AND c.country = 'USA'
ORDER BY o.total_amount DESC
LIMIT 100;  -- Pagination for large datasets

Module: Query Optimization Techniques

Master techniques to transform slow queries into high-performance operations

1

Optimization Fundamentals

  • Introduction to Query Optimization
  • SQL Execution Plans Explained
  • How the Query Optimizer Works
  • Cost-Based Optimization Concepts
  • Avoiding Full Table Scans
2

Query Writing Best Practices

  • Efficient WHERE Clause Usage
  • Reducing Subqueries with Joins
  • Using LIMIT/OFFSET Effectively
  • Avoiding SELECT * (Column Selection)
  • Optimizing Complex JOIN Queries
3

Advanced Optimization

  • Reducing Sorting/ORDER BY Overhead
  • Dealing with Large Data Sets
  • Pagination Strategies
  • Table Partitioning Concepts
  • Real-World Optimization Examples

Use EXPLAIN

Always analyze query execution plans before optimization

! Avoid Functions in WHERE

Functions prevent index usage: WHERE YEAR(date) = 2024

Minimize Subqueries

Convert correlated subqueries to JOINs when possible

Specific Column Selection

SELECT id, name instead of SELECT * for better performance

Module: Indexing Best Practices

Design and implement effective indexes for optimal database performance

4

Index Fundamentals

  • What Are Indexes & How They Work
  • Types of Indexes Overview
  • B-Tree Index Structure
  • Hash Index Applications
  • Creating Effective Indexes
5

Index Design Strategy

  • When to Use & Avoid Indexes
  • Choosing the Right Columns
  • Composite Index Ordering Rules
  • Covering Indexes for SELECT
  • Index Maintenance Procedures
6

Index Management

  • Monitoring Index Usage
  • Common Indexing Mistakes
  • Over-Indexing Problems
  • Duplicate Indexes Issues
  • Database-Specific Indexing

B-Tree Index

Default index type

Range queries, sorting

Hash Index

Exact match lookups

Equality comparisons only

Composite Index

Multiple columns

Order matters significantly

Unique Index

Enforce uniqueness

Data integrity constraints

Cardinality Matters

Index high-cardinality columns (more unique values)

Composite Index Order

Place most selective columns first in composite indexes

Avoid Over-Indexing

Each index adds write overhead – balance read vs write needs

Monitor & Maintain

Regularly rebuild fragmented indexes for optimal performance

Module: Performance Tuning

Identify and resolve database performance bottlenecks at all levels

7

Performance Analysis

  • Understanding Database Bottlenecks
  • Server Configuration Tuning
  • Buffer Pool Size Optimization
  • Cache Configuration Strategies
  • Connection Pooling Implementation
8

Monitoring Tools

  • Query Performance Monitoring
  • EXPLAIN / EXPLAIN ANALYZE Usage
  • Slow Query Logs Configuration
  • Performance Schema (MySQL)
  • pg_stat Statements (PostgreSQL)
9

Advanced Tuning

  • Optimizing Read & Write Operations
  • Table Partitioning Strategies
  • Sharding Concepts & Implementation
  • Memory & Storage Optimization
  • Reducing Locking & Blocking

EXPLAIN

View query execution plan

EXPLAIN ANALYZE

Actual execution statistics

Slow Query Log

Identify problem queries

Performance Schema

Detailed performance metrics

Database Scaling Strategies

Scale databases to handle growing workloads and high-concurrency demands

Vertical Scaling

Scale Up: Add more resources to existing server

  • Increase CPU/RAM capacity
  • Upgrade storage to SSD/NVMe
  • Add more memory for caching
  • Simple to implement
  • Single point of failure

Horizontal Scaling

Scale Out: Add more servers to distribute load

  • Sharding across multiple servers
  • Read replicas for read-heavy loads
  • Master-slave replication
  • Better fault tolerance
  • More complex to manage

Connection Pooling

Reuse database connections to reduce overhead

Query Caching

Cache frequent query results at application level

Read Replicas

Offload read queries to replica servers

Asynchronous Processing

Use queues for non-critical operations

Cloud Database Optimization

Optimize performance for managed database services in cloud environments

AWS RDS

Parameter groups, Enhanced monitoring, Read replicas, Performance Insights

GCP Cloud SQL

Query insights, Automatic storage increase, Flags configuration, Maintenance windows

Azure SQL

Query Performance Insight, Automatic tuning, Hyperscale tier, Intelligent Insights

E-Commerce Platform Optimization

High-traffic retail site with 10M+ products:

  • Query optimization reduced page load from 3.2s to 0.8s
  • Composite indexes on product searches
  • Read replicas for product catalog queries
  • Query caching for frequent searches
  • Connection pooling for Black Friday traffic

Financial Analytics System

Real-time trading analytics with 100K+ transactions/second:

  • Partitioning by transaction date
  • Covering indexes for aggregation queries
  • In-memory tables for hot data
  • Sharding by customer region
  • Asynchronous reporting generation

Production Optimization Best Practices

Proven strategies for maintaining optimal database performance in production

10

Monitoring & Alerting

  • Real-time performance monitoring
  • Automated alerting for bottlenecks
  • Capacity planning metrics
  • Trend analysis for growth
  • Anomaly detection setup
11

Maintenance Procedures

  • Regular index maintenance
  • Statistics updates scheduling
  • Backup optimization strategies
  • Vacuum/defragmentation cycles
  • Version upgrade planning
12

Continuous Optimization

  • Query performance review cycles
  • A/B testing for query changes
  • Load testing before deployments
  • Performance regression testing
  • Documentation of optimizations

✅ Baseline Metrics

Establish performance baselines before optimization

✅ Gradual Changes

Implement optimizations gradually and monitor impact

✅ Test Thoroughly

Test optimizations with production-like data volumes

✅ Document Everything

Maintain optimization log with before/after metrics

Ready to Optimize Your Database Performance?

Join database performance engineers optimizing mission-critical systems with advanced SQL tuning techniques.

Start Optimizing Now
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