SQL Query performance tuning

CASE 1: CTEs

SELECT *
FROM orders
WHERE customer_id IN (
  SELECT customer_id
  FROM customers
  WHERE country = 'USA'
) 
INNER JOIN order_items
ON orders.order_id = order_items.order_id;

This query joins the orders and order_items tables using the order_id column, but it first needs to retrieve a list of customer_id values from the customers table that match a certain condition. This is done using a subquery that is executed for each row in the orders table, which can be inefficient and slow down the query.

A better way to write this query is by using a Common Table Expression (CTE) and a good join. Here’s an example:

WITH usa_customers AS (
  SELECT customer_id
  FROM customers
  WHERE country = 'USA'
)
SELECT *
FROM orders
INNER JOIN usa_customers
ON orders.customer_id = usa_customers.customer_id
INNER JOIN order_items
ON orders.order_id = order_items.order_id;

In this query, a CTE named usa_customers is defined that retrieves the customer_id values from the customers table that match the condition country = 'USA'. The main query then joins the orders table with the usa_customers CTE and the order_items table using the customer_id and order_id columns, respectively.

Using a CTE can improve performance because it allows the subquery to be executed only once, and the result set is stored in memory, making it faster to access than repeatedly querying the customers table. Additionally, using a good join can also improve performance by reducing the number of rows that need to be compared and potentially eliminating unnecessary calculations.

Overall, using a CTE and a good join can help optimize a query and improve its performance.

CASE 2: Join Optimization

Here’s an example of a long running query with a bad join:

SELECT *
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
JOIN order_items ON orders.order_id = order_items.order_id
JOIN products ON order_items.product_id = products.product_id
JOIN categories ON products.category_id = categories.category_id
WHERE categories.category_name = 'Electronics';

This query joins the customers, orders, order_items, products, and categories tables, and filters the results by the category_name column in the categories table. However, the joins in this query can lead to performance issues if the tables are large, as the database must perform a large number of comparisons to produce the results.

To improve the performance of this query, we can use a technique called query optimization, which involves restructuring the query to minimize the number of comparisons and operations required. Here’s an example of a good query that achieves the same result as the previous query, but uses better join techniques:

SELECT *
FROM categories
JOIN products ON categories.category_id = products.category_id
JOIN order_items ON products.product_id = order_items.product_id
JOIN orders ON order_items.order_id = orders.order_id
JOIN customers ON orders.customer_id = customers.customer_id
WHERE categories.category_name = 'Electronics';

This query starts by joining the categories and products tables, followed by a join with order_items and orders, and finally a join with customers. By joining the tables in this order, we can minimize the number of comparisons and reduce the overall processing time.

Additionally, by filtering the results by categories.category_name before joining the other tables, we can further optimize the query by reducing the number of rows that need to be compared and potentially eliminating unnecessary calculations.

Overall, by using good join techniques and query optimization, we can significantly improve the performance of long-running queries and help ensure that the database operates efficiently.

CASE 3: Predicate Pushdown

Here’s an example of a bad query that doesn’t use predicate pushdown:

SELECT *
FROM sales
WHERE YEAR(sale_date) = 2022
ORDER BY sale_date DESC;

This query filters the sales table based on the year of the sale_date column, and then sorts the results in descending order based on the sale_date column. However, the filtering operation is applied after the sorting operation, which can be inefficient and slow down the query, especially if the sales table is large.

To improve the performance of this query, we can use predicate pushdown, which involves pushing the filtering operation down to the storage layer, where it can be applied more efficiently. Here’s an example of a corrected query that uses predicate pushdown:

SELECT *
FROM sales
WHERE sale_date >= '2022-01-01' AND sale_date < '2023-01-01'
ORDER BY sale_date DESC;

This query filters the sales table based on a range of dates rather than on the year of the sale_date column. By using a range of dates, we can push the filtering operation down to the storage layer, where it can be applied more efficiently. Additionally, by using a range of dates, we can simplify the query and potentially improve its readability.

Overall, by using predicate pushdown, we can optimize queries and improve their performance, especially when working with large datasets.