Advanced SQL - The next frontier

Advanced SQL is a powerful tool that allows you to retrieve, analyze, and manipulate large amounts of data in a structured and efficient way

Advanced SQL is a powerful tool that allows you to retrieve, analyze, and manipulate large amounts of data in a structured and efficient way. It is widely used in data analysis and business intelligence, as well as in many other fields such as software development, finance, and marketing.

Learning advanced SQL can help you to:

  • Retrieve and analyze large amounts of data from databases

  • Create complex reports and visualizations to gain insights from your data

  • Write efficient queries to improve the performance of your database

  • Use advanced features such as window functions, common table expressions, and recursive queries

  • Understand and optimize the performance of your database

  • Be able to explore, analyze, and gain insights from data more effectively

  • Provide data-driven insights and make decisions in an evidence-based manner.

With the ability to handle big data and make sense of it, advanced SQL skills are becoming increasingly important in today's data-driven world. The knowledge of advanced SQL can make you a valuable asset to any organization that deals with large amounts of data.

Here are a few examples of advanced SQL queries that demonstrate the use of some complex and powerful features of the SQL language:

Using subqueries in the SELECT clause:

  (SELECT SUM(amount) FROM orders WHERE orders.customer_id = as total_spent
FROM customers
ORDER BY total_spent DESC;

This query uses a subquery in the SELECT clause to calculate the total amount spent by each customer, and then returns a list of customers along with their total spending, ordered by descending spending.

Using the WITH clause for common table expressions:

  top_customers AS (SELECT customer_id, SUM(amount) as total_spent FROM orders GROUP BY customer_id ORDER BY total_spent DESC LIMIT 10),
  customer_info AS (SELECT id, name, email FROM customers)
  JOIN customer_info ON top_customers.customer_id =;

This query uses the WITH clause to define two common table expressions (CTEs) "top_customers" and "customer_info", which are used to simplify and modularize the query. The first CTE selects the top 10 customers based on their total spending, and the second CTE selects customer name, email and id . And then it join the two CTE to get the final result.

Using window functions to calculate running totals:

  SUM(amount) OVER (PARTITION BY name ORDER BY date) as running_total
  name, date;

This query uses a window function, SUM(amount) OVER (PARTITION BY name ORDER BY date), to calculate the running total of transactions for each name. It returns all transactions along with the running total for each name, ordered by name and date.

Using Self Join:

SELECT as employee, as manager
  employees e1 
  JOIN employees e2 ON e1.manager_id =;

This query uses a self-join to join a table to itself to show the relationship between employees and their managers. It returns a list of all employees and their corresponding managers.


  SUM(order_items.quantity) as product_sold,
  JOIN order_items ON = order_items.order_id
  JOIN products ON = order_items.product_id
  SUM(order_items.quantity) > 100;

This query uses join to combine the orders and order_items tables on the order_id column, and join with the product table on the product_id column, then it uses the GROUP BY clause to group the results by product_id, and the HAVING clause to filter out only the products that have sold more than 100 units. The SELECT clause lists the product_id, the total quantity sold, and the product name.

Using COUNT() and GROUP BY :

  COUNT(employee_id) as total_employees
  total_employees DESC;

This query uses the COUNT() function to count the number of employees in each department, and the GROUP BY clause to group the results by department. The SELECT clause lists the department name and the total number of employees, and the query is ordered by total number of employees in descending order.


(SELECT id, name, 'customer' as type FROM customers)
(SELECT id, name, 'employee' as type FROM employees)
ORDER BY name;

This query uses the UNION operator to combine the results of two separate SELECT statements, one for customers and one for employees, and orders the final result set by name. UNION operator will remove duplicates if present.

Recursive Queries:

A recursive query is a type of query that uses a self-referencing mechanism to perform a task. One common use case for a recursive query is to traverse a hierarchical data structure, such as a tree or a graph.

Here is an example of a recursive query that is used to retrieve all the ancestors of a particular node in a tree-like structure:

WITH RECURSIVE ancestors (id, parent_id, name) AS (
    -- Anchor query to select the starting node
    SELECT id, parent_id, name FROM nodes WHERE id = 5
    -- Recursive query to select the parent of each node
    SELECT, nodes.parent_id, FROM nodes
    JOIN ancestors ON = ancestors.parent_id
SELECT * FROM ancestors;

The query uses a common table expression (CTE) called "ancestors" to define the recursive query. The CTE has three columns: id, parent_id, and name. The anchor query selects the starting node for the recursive query, which in this case is the node with an id of 5. The recursive query then selects the parent of each node in the "ancestors" CTE, and joins it with the "ancestors" CTE on the parent_id column. This process is repeated until it reaches the root of the tree or until the maximum recursion level is reached. The final query selects all the ancestors that have been found.

It's important to note that recursive queries can be very powerful, but they can also be very resource-intensive and should be used carefully to avoid performance issues. Make sure you stop recursion in an appropriate place and take into account the maximum recursion level allowed in your DBMS.

Also, it's worth noting that not all SQL implementations support recursion, but most of the major RDBMS systems like PostgreSQL, Oracle, SQL Server and SQLite provide support for recursive queries using the WITH RECURSIVE keyword.

These are just a few examples of the many powerful features of SQL, and the types of queries that you can create using them. Of course, the specific details of the queries will depend on the structure of your database and the information you are trying to retrieve, but these examples should give you an idea of what is possible.


Kaggle - Advanced SQL

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