MySQL: How to optimize JOIN operations on large tables with compound indexes?
I'm trying to debug Can someone help me understand I'm getting frustrated with I'm stuck on something that should probably be simple. I am currently working with a MySQL 8.0 database where I have two large tables, `orders` and `customers`, each containing millions of rows. I need to frequently run JOIN queries to retrieve customer details along with their respective orders. However, I've been experiencing important performance optimization with these queries, particularly as the dataset grows. For example, the following query takes a considerable amount of time to execute: ```sql SELECT c.customer_id, c.name, o.order_id, o.total_amount FROM customers c JOIN orders o ON c.customer_id = o.customer_id WHERE o.order_date BETWEEN '2023-01-01' AND '2023-12-31'; ``` I have tried adding indexes to both `customer_id` columns in both tables: ```sql CREATE INDEX idx_customer_id ON customers(customer_id); CREATE INDEX idx_order_customer_id ON orders(customer_id); ``` However, the performance improvement has been minimal. I also checked the execution plan using `EXPLAIN` and noticed that it still performs a full table scan on the `orders` table. I am unsure if I am missing something in terms of indexing strategy or if there's a better approach to optimize these JOIN operations. Could anyone suggest best practices or specific configurations that could help with improving the query performance? Additionally, are there any drawbacks to using compound indexes in this scenario? For context: I'm using Sql on Windows. Am I missing something obvious? For context: I'm using Sql on Windows. For reference, this is a production REST API. I'm working with Sql in a Docker container on macOS. What's the correct way to implement this?