With these best practices, you'll increase productivity, optimize database performance, and ensure that your SQL code is secure and maintainable.
We live in a data-driven world, and SQL(Structured Query Language) is essential for managing data. SQL is used to manipulate and interact with data in a relational database. It's used by businesses of all sizes, from startups to large corporations, to extract insights from their data and make informed decisions.
But writing SQL code is not just about knowing the language - it's also about using best practices for coding and development. Poorly written SQL code can lead to performance issues, security vulnerabilities, and difficult-to-maintain databases. On the other hand, well-written code can make your database faster, more secure, and easier to manage.
This article will cover the best practices for SQL coding and development, and provide practical, self-contained tips and techniques to help you write high-quality SQL code. With these best practices, you'll increase productivity, optimize database performance, and ensure that your SQL code is secure and maintainable.
Before diving into this tutorial, here are a few prerequisites you should be familiar with to help you get the most out of this article:
These prerequisites are essential for creating well-designed databases that are optimized for performance and can handle a large amount of data. So, brush up on these prerequisites before diving into advanced SQL coding and development.
Effective Data Modelling
Effective data modelling is crucial for developing a database structure that is functional, maintainable, and scalable when it comes to SQL coding and development. The following are some best practices for efficient SQL data modelling:
1 CREATE TABLE tbl_orders ( 2 fld_order_id INT PRIMARY KEY, 3 fld_customer_id INT, 4 fld_order_date DATE, 5 fld_total_amt DECIMAL(10,2) 6 );
Instead, DO this:
1 CREATE TABLE orders ( 2 order_id INT PRIMARY KEY, 3 customer_id INT, 4 order_date DATE, 5 total DECIMAL(10,2) 6 );
1 CREATE TABLE orders ( 2 order_id INT, 3 customer_name VARCHAR(100), 4 product_name VARCHAR(100), 5 product_description VARCHAR(500), 6 price FLOAT, 7 quantity INT 8 );
Instead, DO this:
1 CREATE TABLE orders ( 2 order_id INT, 3 customer_id INT, 4 product_id INT, 5 order_date DATETIME, 6 quantity INT, 7 CONSTRAINT fk_customer FOREIGN KEY (customer_id) REFERENCES customers(customer_id), 8 CONSTRAINT fk_product FOREIGN KEY (product_id) REFERENCES products(product_id) 9 );
In the first example, there are multiple data fields, such as customer_name and product_description, that are not normalized and can lead to redundancy and data inconsistencies. In the second example, data is organized into separate tables and normalized, resulting in improved data integrity and efficiency.
1 CREATE TABLE ExampleTable ( 2 ID INT, 3 Name VARCHAR, 4 Email VARCHAR NOT NULL UNIQUE, 5 Age INT, 6 Address VARCHAR 7 );
Instead, carefully consider the data you will be storing and choose data types and constraints that are appropriate for the data's size, expected use, and required accuracy. Use appropriate constraints such as NOT NULL and UNIQUE to ensure data integrity. For instance:
1 CREATE TABLE ExampleTable ( 2 ID INT PRIMARY KEY, 3 Name VARCHAR(50) NOT NULL, 4 Email VARCHAR(255) NOT NULL, 5 Age INT, 6 Address VARCHAR(100) 7 );
1 CREATE TABLE Customers ( 2 CustomerID INT, 3 FirstName VARCHAR(50), 4 LastName VARCHAR(50), 5 Email VARCHAR(50), 6 City VARCHAR(50), 7 Country VARCHAR(50) 8 ); 9 10 CREATE TABLE Orders ( 11 OrderID INT, 12 CustomerID INT, 13 OrderDate DATE 14 );
1 CREATE TABLE Customers ( 2 CustomerID INT PRIMARY KEY, 3 FirstName VARCHAR(50) NOT NULL, 4 LastName VARCHAR(50) NOT NULL, 5 Email VARCHAR(50) UNIQUE, 6 City VARCHAR(50) NOT NULL, 7 Country VARCHAR(50) NOT NULL 8 ); 9 10 CREATE TABLE Orders ( 11 OrderID INT PRIMARY KEY, 12 CustomerID INT, 13 OrderDate DATE 14 FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID) 15 );
Poorly written queries and common mistakes can lead to slow response times and poor database performance. Optimizing SQL queries is essential for faster results and improved overall performance of your application.
Here are some best practices for optimizing your queries:
1 SELECT * 2 FROM table1 3 WHERE id IN (SELECT id FROM table2 WHERE condition) AND 4 name LIKE (SELECT name FROM table3 WHERE condition) AND 5 value = (SELECT value FROM table4 WHERE condition) AND 6 ... // more subqueries
1 SELECT t1.* 2 FROM table1 t1 3 JOIN table2 t2 ON t1.id = t2.id 4 JOIN table3 t3 ON t1.name = t3.name 5 JOIN table4 t4 ON t1.value = t4.value 6 WHERE t2.condition AND t3.condition AND t4.condition
1 CREATE INDEX idx_name 2 ON table_name(column_name) 3 WHERE column_name is not null;
1 CREATE INDEX idx_name ON table_name(column_name);
1 SELECT * 2 FROM customers 3 WHERE customer_id = 123
1 SELECT * 2 FROM customers 3 WHERE customer_name = 'John' AND customer_address = '123 Main St'
Any SQL developer must possess the fundamental skill of querying data from a SQL database, and they can use various techniques to create efficient and effective queries. Writing efficient SQL queries is essential for optimizing database performance. Here are some best practices to follow:
1 SELECT * 2 FROM sales 3 WHERE date >= '2022-01-01' AND date <= '2022-12-31'<br> 4 AND customer_id IN ( 5 SELECT id 6 FROM customers 7 WHERE age >= 18 AND age <= 35<br> 8 )<br> 9 AND product_id IN ( 10 SELECT id 11 FROM products 12 WHERE category = 'Electronics' 13 AND price <= 1000<br> 14 );
1 WITH temp_table AS ( 2 SELECT id, SUM(sales) AS total_sales 3 FROM sales 4 GROUP BY id 5 ) 6 SELECT * 7 FROM temp_table 8 WHERE total_sales > 1000;
1 # Unsafe: Directly embedding values into the query 2 import sqlite3 3 4 conn = sqlite3.connect('example.db') 5 c = conn.cursor() 6 7 name = "John" 8 age = 25 9 10 c.execute("SELECT * FROM users WHERE name='%s' AND age='%s'" % (name, age))
1 # Safe: Using query parameters 2 import sqlite3 3 4 conn = sqlite3.connect('example.db') 5 c = conn.cursor() 6 7 name = "John" 8 age = 25 9 10 c.execute("SELECT * FROM users WHERE name=? AND age=?", (name, age))
By following these best practices, you can write efficient and effective SQL queries that will help your database perform optimally.
Additionally, consider using SQL performance monitoring tools(SQL Monitor, SolarWinds Database Performance Analyzer, SQL Sentry, Quest Foglight for SQL Server or Redgate SQL Monitor) to identify slow or inefficient queries and optimize them for better performance.
Formatting and Style
In SQL coding and development, formatting and style are just as important as the code itself. Writing readable code can improve how quickly you can write and debug your code and how easily other team members can understand and collaborate on your code.
Here are some best practices for formatting and style:
1 SELECT * 2 FROM customerTable c
1 SELECT * 2 FROM customers c
1 --get data 2 SELECT * 3 FROM orders o 4 WHERE o.customer_id IN (SELECT customer_id FROM invoices WHERE status = 'pending')
1 --get orders for customers with pending invoices 2 SELECT * 3 FROM orders o 4 WHERE o.customer_id IN (SELECT customer_id FROM invoices WHERE status = 'pending')
1 /* 2 ….. 3 --get orders for customers with pending invoices 4 */ 5 SELECT * 6 FROM orders o 7 WHERE o.customer_id IN (SELECT customer_id FROM invoices WHERE status = 'pending')
1 SELECT * FROM my_table WHERE my_column='value'
1 SELECT * 2 FROM my_table 3 WHERE my_column='value'
So, whether you're working solo or collaborating with a team, mastering the art of readable code is essential for successful SQL coding and development.
The SQL Commander is a feature in DBVisualizer that allows you to write and execute SQL queries. You can use this feature to practice writing queries with complex logic, using the correct syntax and expressions, and optimizing performance with indexes.
Here is a screenshot of the SQL Commander, showing a properly written SQL query and the result: