Mastering Advanced SQL Queries with Window Functions and CTEs

06 Jun 2023 Balmiki Mandal 0 SQL

Advanced Querying in SQL

Advanced SQL queries can be used to perform complex data analysis and manipulation tasks. Some of the most common advanced SQL techniques include:

  • Window functions: Window functions are used to perform calculations on groups of rows within a result set. They can be used to rank rows, calculate moving averages, and identify outliers.
  • Common Table Expressions (CTEs): CTEs are temporary named subqueries that can be used to make complex queries more readable and maintainable.
  • Recursive queries: Recursive queries are used to query data that has a hierarchical structure. They can be used to calculate nested totals, find the shortest path between two nodes in a graph, and identify cycles in data.

Using window functions for advanced analytics

Window functions can be used to perform a variety of advanced analytics tasks, such as:

  • Ranking rows: The RANK() function can be used to rank rows in a result set based on a specified column. This can be useful for identifying the top or bottom performers in a dataset.
  • Calculating moving averages: The AVG() function can be used to calculate the moving average of a column over a specified window. This can be useful for identifying trends in data over time.
  • Identifying outliers: The STDDEV() function can be used to calculate the standard deviation of a column over a specified window. This can be useful for identifying outliers in data.

Example:  window function query that calculates the moving average of sales over a 3-month period:

SQL
SELECT customer_id,
       SUM(sales) OVER (ORDER BY order_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_average_sales
FROM orders
GROUP BY customer_id

This query will return a result set with one row for each customer, showing their customer ID and the moving average of their sales over the past 3 months.

Common Table Expressions (CTEs) for improved query readability

CTEs can be used to make complex queries more readable and maintainable by breaking them down into smaller, more manageable subqueries.

Example: CTE query that calculates the total sales for each product category

SQL
WITH product_sales AS (
  SELECT product_category,
         SUM(sales) AS total_sales
  FROM orders
  GROUP BY product_category
)

SELECT product_category,
       total_sales
FROM product_sales
ORDER BY total_sales DESC

This query first creates a CTE called product_sales that calculates the total sales for each product category. Then, the main query selects the product category and total sales from the product_sales CTE and orders the results by total sales in descending order.

Recursive queries

Recursive queries can be used to query data that has a hierarchical structure. They are typically used to calculate nested totals, find the shortest path between two nodes in a graph, and identify cycles in data.

Example: Recursive query that calculates the nested total sales for each product category:

SQL
WITH RECURSIVE product_sales_tree AS (
  SELECT product_category,
         SUM(sales) AS total_sales
  FROM orders
  GROUP BY product_category
  UNION ALL
  SELECT child.product_category,
         SUM(child.sales) AS total_sales
  FROM orders child
  INNER JOIN product_sales_tree parent ON child.parent_product_category = parent.product_category
)

SELECT product_category,
       total_sales
FROM product_sales_tree
ORDER BY product_category

This query first creates a recursive CTE called product_sales_tree that calculates the total sales for each product category and its subcategories. The CTE then recursively joins itself to calculate the total sales for each product category at all levels of the hierarchy. The main query then selects the product category and total sales from the product_sales_tree CTE and orders the results by product category.

Advanced SQL techniques can be a powerful tool for data analysis and manipulation. By learning how to use window functions, CTEs, and recursive queries, you can perform complex tasks with greater efficiency and accuracy.

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BY: Balmiki Mandal

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