Speaker 1: In this video, we're going to deep dive into SQL query execution. We'll cover query processing, optimization techniques, and best practices. Let's get started. Here's the example query we'll use throughout this video. In this query, we find the top 10 customers with at least $1,000 spent on orders since January 1, 2023 by joining the customers and orders table and grouping by customer ID. We display their total orders and amount spent, sorted in descending order by the total spent. Before we dive into the query, let's discuss query execution plans. Database systems create these plans to optimize queries and minimize resource usage. Understanding the plan can help us optimize a query for better performance. Execution plans provide information like estimated costs, chosen join algorithms, and a sequence of operations. First, let's start with the FROM and JOIN clauses. This is where we choose the tables we want to work with and specify how to join them. In our query, we are using the customers table and joining it with the orders table using the common ID and customer ID columns. Using indexes on join columns can significantly improve the performance of the join operation. Make sure we have appropriate indexes in place for faster query execution. Index types, such as B-tree and bitmap indexes, can impact performance based on data distribution and query types. Now we move to the WHERE clause. This filters the combined data by applying a condition. In our case, we are considering orders placed on or after January 1, 2023. It's important to write a soluble query to leverage indexes effectively. Soluble means search, argument, able, and it refers to queries that can use indexes for faster execution. Let's dive deeper into the concept of soluble queries. When we say a query is soluble, it means that the query can efficiently use indexes to speed up the execution process. Writing soluble queries is essential for optimizing database performance. Here's an example to help illustrate soluble vs. non-soluble queries. In a soluble query, we directly compare the order date column to a specific date. This allows the database engine to use an index on the order date column to quickly filter out the records that meet the condition. In contrast, the non-soluble query uses the year function on the order date column. This prevents the database engine from using an index on order date because the function must be applied to every row in the table, even if the index exists. The non-soluble query will be slower because there are a lot more records to scan. To write soluble queries, 1. Avoid using functions or calculations on index columns in the WHERE clause. 2. Use direct comparisons when possible. Do not wrap columns in a function. 3. If we need to use a function on a column, 4. Create a computer column or a function-based index if the database system supports it. Next up are the GROUP BY and HAVING clauses. In our query, we are grouping the records by Customer ID and filtering the groups based on the condition Total Spend greater than or equal to 1000. This query finds those who have spent a significant amount on orders. The SELECT clause comes next. It defines which columns we want in our result set. In this case, we are selecting Customer ID, Total Orders, and Total Spent. Even though SELECT comes first in a SQL query, it is pretty far down in the query processing order. When optimizing SELECT clauses, consider using covering indexes that include all the columns needed for the query, especially in the SELECT, WHERE, and JOIN clauses. This enables the database engine to retrieve data directly from the index. This speeds up query execution. Covering indexes can reduce the need for additional I-O operations and minimize performance overhead. Finally, we have ORDER BY and LIMIT. To optimize the ORDER BY and LIMIT clauses, consider using a smaller result set with filtering and pagination. For large datasets, avoid sorting the entire dataset. It can lead to high memory usage and slow response times. Use appropriate indexes to speed up sorting and reduce the amount of data to be sorted in memory. And that's it. We've visualized a SQL query's execution order. We discussed optimization techniques like indexes, searchable queries, and covering indexes. Remember, the order is FROM, JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, and LIMIT. Armed with this knowledge, we're equipped to tackle more complex SQL queries and optimize them for better performance. If you like our videos, you may like our system design newsletter as well. It covers topics and trends in large-scale system design, trusted by 300,000 readers. Subscribe at blog.bybygo.com.