Database Refactoring Strategies

Database refactoring is the process of evolving a database schema to improve its design without changing its functionality or introducing defects. It involves making small, incremental changes to a database to enhance performance, scalability, or maintainability while ensuring that applications interacting with the database continue to work seamlessly.This chapter covers everything from the basics of database refactoring to advanced strategies, explained in an easy-to-understand manner with examples.

Introduction to Database Refactoring

What is Database Refactoring?

Database refactoring involves modifying the structure of a database (schema, relationships, queries) to:

Improve readability.
Enhance performance.
Enable scalability.
Simplify maintenance.

Why is it Important?

Databases grow complex over time, making them harder to maintain.
Poorly designed schemas impact application performance and developer productivity.
Refactoring helps modernize the database, ensuring it meets evolving business requirements.

Principles of Database Refactoring

Key Principles

Incremental Changes: Small, manageable changes reduce risk.
Backward Compatibility: Ensure existing applications work after refactoring.
Comprehensive Testing: Validate that changes do not break functionality.
Automated Migration: Use scripts to apply schema changes across environments.

Types of Database Refactoring

Structural Refactoring: Changing the schema design (e.g., renaming columns, normalizing data).
Data Quality Refactoring: Cleaning or improving data consistency.
Performance Refactoring: Optimizing indexes, queries, and storage strategies.
Architectural Refactoring: Changing database deployment, such as moving to a cloud-native setup.

Planning Database Refactoring

Assessing the Current Database

Schema Analysis: Identify redundant tables, columns, and indexes.
Query Performance Analysis: Identify slow or frequently executed queries.
Dependency Mapping: Understand how applications interact with the database.

Example: Identify Redundant Columns in MySQL

				
					SELECT TABLE_NAME, COLUMN_NAME 
FROM INFORMATION_SCHEMA.COLUMNS
WHERE COLUMN_NAME LIKE '%redundant%';

Setting Objectives

Define clear goals, such as:

Improving query performance by 20%.
Reducing storage usage by 10%.
Simplifying relationships for better readability.

Structural Refactoring Techniques

Renaming Tables or Columns

Rename poorly named tables or columns to improve clarity.

Example: Renaming a Column in SQL Server

				
					EXEC sp_rename 'orders.cust_id', 'customer_id', 'COLUMN';

Splitting Tables

Split a table into smaller tables for normalization and performance.

Example: Splitting a Table

Before Refactoring

				
					CREATE TABLE orders (
    order_id INT,
    customer_name VARCHAR(100),
    product_name VARCHAR(100)
);

After Refactoring

				
					CREATE TABLE customers (
    customer_id INT PRIMARY KEY,
    customer_name VARCHAR(100)
);

CREATE TABLE products (
    product_id INT PRIMARY KEY,
    product_name VARCHAR(100)
);

CREATE TABLE orders (
    order_id INT,
    customer_id INT,
    product_id INT,
    FOREIGN KEY (customer_id) REFERENCES customers(customer_id),
    FOREIGN KEY (product_id) REFERENCES products(product_id)
);

Merging Tables

Combine tables to reduce redundancy or simplify queries.

Example: Merging Two Tables

				
					INSERT INTO merged_table (col1, col2)
SELECT col1, col2 FROM table1
UNION ALL
SELECT col1, col2 FROM table2;

Performance Refactoring Strategies

Adding or Modifying Indexes

Optimize query execution with indexes.

Example: Adding an Index in MySQL

				
					CREATE INDEX idx_customer_id ON orders(customer_id);

Optimizing Query Execution Plans

Review and optimize execution plans to avoid bottlenecks.

Example: Viewing an Execution Plan in PostgreSQL

				
					EXPLAIN ANALYZE SELECT * FROM orders WHERE customer_id = 1;

Data Quality Refactoring

Removing Duplicates

Identify and remove duplicate data.

Example: Deleting Duplicate Rows in MySQL

				
					DELETE FROM orders
WHERE id NOT IN (
    SELECT MIN(id)
    FROM orders
    GROUP BY customer_id, product_id
);

Standardizing Data

Ensure consistent formats for fields like dates or phone numbers.

Example: Standardizing Phone Number

				
					UPDATE customers
SET phone_number = REPLACE(phone_number, '-', '');

Architectural Refactoring

Migrating to a Partitioned Database

Partition large tables for better performance.

Example: Partitioning by Date in PostgreSQL

				
					CREATE TABLE orders_partitioned (
    order_id INT,
    order_date DATE
) PARTITION BY RANGE (order_date);

CREATE TABLE orders_2024 PARTITION OF orders_partitioned
FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');

Moving to a Cloud-Native Database

Refactor on-premises databases to leverage cloud scalability and reliability.

Automation and Tooling

Database Migration Tools

Flyway: Version control for database migrations.
Liquibase: Automates schema changes across environments.

Using Version Control

Track schema changes with Git for rollbacks and auditing.

Testing and Deployment

Writing Database Tests

Schema Tests: Validate structure and constraints.
Data Integrity Tests: Check for data consistency.

Example: Testing Constraints in MySQL

				
					SELECT * FROM orders WHERE customer_id NOT IN (SELECT id FROM customers);

Deployment Strategies

Blue-Green Deployment: Minimize downtime during migrations.
Rolling Updates: Gradually apply changes across servers.

Database refactoring is a critical process for improving the performance, scalability, and maintainability of databases. By following structured strategies and leveraging appropriate tools, you can transform outdated, inefficient databases into robust systems capable of handling modern workloads. With the insights and techniques shared in this chapter, you now have a comprehensive roadmap to tackle any database refactoring challenge. Happy coding !❤️