For an instructor lead, in-depth look at learning SQL click below.
Creating speedy, efficient SQL queries is not only essential for a smooth user experience, but it also optimizes your resources and keeps operational costs low. SQL performance optimization can seem daunting initially, especially with Databricks’ vast array of capabilities and functionalities. In this post, we dive into some simple tips and techniques to optimize your databricks SQL performance and ensure your queries are running as efficiently as possible.
1. Minimize Amount of Data Retrieved
One principle of writing efficient SQL queries is minimizing the amount of data a query retrieves. Instead of using the asterisk (*) to select all columns, list the columns you need specifically. This not only reduces the amount of data loaded into memory but also improves network performance by reducing the volume of data that needs to travel between your server and Databricks.
|
1 2 3 4 5 6 7 |
/* Instead of this */ SELECT * FROM Orders; /* Use this */ SELECT OrderId, CustomerId, OrderDate FROM Orders; |
2. Use WHERE Clause to Filter Rows
The WHERE clause helps filter data so Databricks only processes the minimum amount of data required. It’s efficient to apply these filters as early as possible to minimize the amount of data that must be processed subsequently.
|
1 2 3 4 5 6 7 8 |
/* Instead of this */ SELECT OrderId, CustomerId, OrderDate FROM Orders; SELECT * FROM Customers WHERE Country = 'USA'; /* Use this */ SELECT OrderId, CustomerId, OrderDate FROM Orders WHERE CustomerId IN (SELECT CustomerId FROM Customers WHERE Country = 'USA'); |
3. Optimize Joins
Join operations can be costly in terms of performance. When joining tables, always ensure that you join them on indexed columns and join smaller tables to larger ones for best performance.
|
1 2 3 4 5 6 7 8 9 10 11 12 13 |
/* Instead of this */ SELECT Orders.OrderId, Customers.CustomerName FROM Orders JOIN Customers ON Orders.CustomerId = Customers.CustomerId; /* Use this */ SELECT Orders.OrderId, Customers.CustomerName FROM Customers JOIN Orders ON Customers.CustomerId = Orders.CustomerId; |
4. Limit the Use of Functions in Predicates
Using functions in WHERE clause can prevent the engine from using indexes effectively, leading to full table scans. You can generally rewrite the query to avoid function calls in the WHERE clause.
|
1 2 3 4 5 6 7 |
/* Instead of this */ SELECT * FROM Orders WHERE MONTH(OrderDate) = 7; /* Use this */ SELECT * FROM Orders WHERE OrderDate >= '2021-07-01' AND OrderDate < '2021-08-01'; |
With these tips and techniques, optimizing your SQL performance for Databricks can significantly aid in the overall efficiency and speed of your operations. Always remember to analyze your queries and take time to understand what each statement does to ensure optimal performance.