Common Table Expressions (CTEs) - MySQL

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CTEs (Common Table Expressions) provide a way to temporarily store the result set of a query that can be referenced within another SELECT, INSERT, UPDATE, or DELETE statement. They can be viewed as an alternative to derived tables, subqueries, and views. Here's a comprehensive dive into CTEs in SQL, specifically MySQL:

What is a CTE?

A Common Table Expression (CTE) can be thought of as a temporary result set and is used within a SQL statement. CTEs provide the advantages of improved readability and ease in breaking down complex queries.

Basic Syntax

WITH cte_name (column_name1, column_name2, ...)
AS
(
    SQL query
)
SELECT * FROM cte_name;

Basic Example Using Classicmodels Database:

Consider you want a list of order numbers and the total price for each order.

WITH OrderTotals AS 
(
    SELECT orderNumber, SUM(priceEach * quantityOrdered) AS TotalPrice
    FROM orderdetails
    GROUP BY orderNumber
)
SELECT * FROM OrderTotals;

Applications of CTEs:

1. Substitution for Views: CTEs can be used as a temporary view during the execution of a single SQL statement.

2. Recursive Queries: One of the most powerful features of CTEs is their ability to reference themselves, allowing for recursive queries.

3. Replacement for Derived Tables: Instead of creating a derived table within your query, you can use a CTE for better readability.

4. Sequential Computations: CTEs can be used to compute aggregate values in a sequence.

5. Data Transformation and Data Cleansing: Data can be transformed and cleaned using CTEs before being used in the main query.

Advanced Usage:

1. Recursive CTEs:
   Consider you want to list all the employee hierarchy.

   WITH RECURSIVE EmployeeHierarchy AS
   (
       SELECT employeeNumber, reportsTo, firstName, lastName
       FROM employees
       WHERE reportsTo IS NULL
       UNION ALL
       SELECT e.employeeNumber, e.reportsTo, e.firstName, e.lastName
       FROM employees e
       INNER JOIN EmployeeHierarchy eh ON e.reportsTo = eh.employeeNumber
   )
   SELECT * FROM EmployeeHierarchy;

2. Multiple CTEs in a Single Query:
   Chain multiple CTEs by separating them with a comma.

   WITH CTE1 AS
   (
       -- Some query
   ),
   CTE2 AS
   (
       -- Some query referencing CTE1
   )
   SELECT * FROM CTE2;

CTE Optimization Techniques:

1. Limit the Number of Rows in the CTE: If you are interested in a subset of rows, add a WHERE clause to your CTE to limit the rows it returns.

2. Avoid Using Functions Inside CTE: Calling functions can be resource-intensive. Whenever possible, avoid calling functions within a CTE.

3. Indexes: Ensure that the tables you're querying inside the CTE have appropriate indexes. The performance benefits of indexes carry over into CTEs.

Do's:

1. Use CTEs for Improved Readability: Especially for complex queries.

2. Use CTEs for Recursive Operations: Instead of using stored procedures or cursors.

Don'ts:

1. Avoid Overusing CTEs: While they improve readability, unnecessary CTEs can make your SQL harder to interpret.

2. Don't Assume CTEs Improve Performance: CTEs don’t necessarily optimize or improve the performance of a query.

Interview Questions:

1. What's the difference between a CTE and a temporary table?

   • CTE is a temporary result set for a single query, while a temp table can span multiple queries.

2. Can you update the data inside a CTE directly?

   • No, CTEs are not updatable. They're a temporary result set that you can query.

3. How can you make a recursive CTE stop?

   • Recursive CTEs should have a terminating condition, otherwise they'll go into an infinite loop.

Hands-on Problem:

Problem: Using the classicmodels database, create a CTE that shows the total sales for each customer and then ranks them by sales in descending order.

Solution:

WITH CustomerSales AS
(
    SELECT c.customerNumber, c.customerName, SUM(od.priceEach * od.quantityOrdered) AS TotalSales
    FROM customers c
    JOIN orders o ON c.customerNumber = o.customerNumber
    JOIN orderdetails od ON o.orderNumber = od.orderNumber
    GROUP BY c.customerNumber, c.customerName
)
, RankedCustomers AS
(
    SELECT customerName, TotalSales,
    RANK() OVER(ORDER BY TotalSales DESC) AS SalesRank
    FROM CustomerSales
)
SELECT * FROM RankedCustomers;

In conclusion, CTEs are a powerful tool in SQL that allow for cleaner, more readable code, and complex, recursive operations. Knowing when and how to use them efficiently is key for any data professional.

Let's delve deeper into the intricacies and finer applications of CTEs in MySQL.

Benefits of Using CTEs:

1. Readability: By isolating different parts of a complex query, CTEs allow developers to break down complicated SQL into logical, readable chunks.

2. Maintainability: If a calculation needs to change, it’s often easier to change a CTE than to change an inline subquery.

3. Reusability: Within the scope of a single query, a CTE can be referenced multiple times. This means you can avoid repeating the same subquery.

Limitations & Quirks:

1. Performance: CTEs aren't always optimized. MySQL's optimizer doesn't always materialize CTEs. This means that, in some cases, it might execute a CTE multiple times if it's referenced multiple times in the main query.

2. Scope: The scope of a CTE is only the query in which it's defined. You cannot use a CTE across multiple queries in the same batch.

Advanced Examples:

1. Data Transformation and Data Cleansing:

Suppose there's a requirement to fetch customer details but the names in the database have irregular capitalizations. Using CTEs, this can be managed in a cleaner way.

WITH CleanedData AS
(
    SELECT customerNumber,
           CONCAT(UPPER(SUBSTRING(customerName, 1, 1)),
                  LOWER(SUBSTRING(customerName FROM 2))) AS formattedName
    FROM customers
)
SELECT * FROM CleanedData;

2. Chain Multiple CTEs for Complex Analytics:

Suppose you want to determine the product which has the highest sales in the highest sales month of the year.

WITH MonthlySales AS 
(
    SELECT MONTH(o.orderDate) AS SaleMonth, 
           od.productCode,
           SUM(od.priceEach * od.quantityOrdered) AS MonthlyProductSales
    FROM orders o
    JOIN orderdetails od ON o.orderNumber = od.orderNumber
    GROUP BY MONTH(o.orderDate), od.productCode
),

MaxSalesMonth AS 
(
    SELECT SaleMonth, SUM(MonthlyProductSales) as TotalSales
    FROM MonthlySales
    GROUP BY SaleMonth
    ORDER BY TotalSales DESC
    LIMIT 1
)

SELECT od.productCode, p.productName, ms.SaleMonth, MAX(ms.MonthlyProductSales) as HighestSales
FROM MonthlySales ms
JOIN products p ON ms.productCode = p.productCode
WHERE ms.SaleMonth = (SELECT SaleMonth FROM MaxSalesMonth)
GROUP BY ms.SaleMonth, od.productCode, p.productName
ORDER BY HighestSales DESC
LIMIT 1;

3. Recursive CTEs with Limitation:

Recursive CTEs can go indefinitely, so setting a limit can be critical. For instance, if you are trying to find managerial hierarchy and limit to 3 levels:

WITH RECURSIVE EmployeeHierarchy AS
(
    SELECT employeeNumber, reportsTo, firstName, lastName, 1 as Level
    FROM employees
    WHERE reportsTo IS NULL
    UNION ALL
    SELECT e.employeeNumber, e.reportsTo, e.firstName, e.lastName, eh.Level + 1
    FROM employees e
    JOIN EmployeeHierarchy eh ON e.reportsTo = eh.employeeNumber
    WHERE eh.Level < 3
)
SELECT * FROM EmployeeHierarchy;

CTE Best Practices:

1. Avoid Large CTEs: If a CTE gets too large, it's generally better to persist the data in a temp table, especially if you're going to do multiple different operations on it.

2. Document: Comment the intent and purpose of each CTE, especially when writing recursive CTEs or chaining multiple CTEs.

3. Testing: When building complex queries using multiple CTEs, write and test each CTE separately to ensure they return the expected results.

Nuanced Scenarios:

1. Hierarchical Data Processing: Besides the employee hierarchy example, recursive CTEs are great for processing data stored in a hierarchical format, such as organizational charts, folder structures, or multi-level bill of materials.

2. Time Series Analysis: Using CTEs, one can easily compute running totals, moving averages, and other analytics over a time series.

3. Graph Analysis: You can use recursive CTEs to traverse graphs stored in databases, e.g., to determine shortest paths or trace routes.

In conclusion, while CTEs are a powerful tool and can simplify complex query writing significantly, it's essential to understand their quirks and nuances in MySQL. Properly used, they can lead to highly readable and maintainable SQL code.

Let's delve into the complex applications of CTEs and some tough interview questions.

Complex Applications:

1. Pathfinding in Graph Structures:

Imagine we have a table routes that represents possible travel routes between cities. It has columns from_city, to_city and cost.

We can use a recursive CTE to find the path and total cost from one city to another.

WITH RECURSIVE PathFinder AS (
    SELECT from_city, to_city, CAST(from_city AS CHAR(1000)) AS route, cost
    FROM routes
    WHERE from_city = 'CityA' 
    UNION ALL
    SELECT pf.from_city, r.to_city, CONCAT(pf.route, '->', r.to_city), pf.cost + r.cost
    FROM PathFinder pf
    JOIN routes r ON pf.to_city = r.from_city
    WHERE NOT FIND_IN_SET(r.to_city, pf.route)
)
SELECT * FROM PathFinder
WHERE to_city = 'CityZ'
ORDER BY cost ASC
LIMIT 1;

This would provide the path from CityA to CityZ with the least cost.

2. Gap Analysis in Time Series Data:

Suppose you have a table sales with daily sales data and you want to identify gaps (missing dates).

WITH RECURSIVE DateSeries AS (
    SELECT MIN(sale_date) AS date
    FROM sales
    UNION
    SELECT DATE_ADD(date, INTERVAL 1 DAY)
    FROM DateSeries
    WHERE date < (SELECT MAX(sale_date) FROM sales)
)
SELECT ds.date 
FROM DateSeries ds
LEFT JOIN sales s ON ds.date = s.sale_date
WHERE s.sale_date IS NULL;

This would list all missing dates in your sales data.

Complex Interview Questions:

1. Self-Join with CTE:
   Question: Imagine you have a table of employees with columns employee_id, manager_id, and name. Use a CTE to find employees who share the same manager and list them together.

   Answer:

   WITH ManagerCTE AS (
       SELECT manager_id, GROUP_CONCAT(name) AS employees_under_manager
       FROM employees
       WHERE manager_id IS NOT NULL
       GROUP BY manager_id
   )
   SELECT m.manager_id, e.name AS manager_name, m.employees_under_manager
   FROM ManagerCTE m
   JOIN employees e ON m.manager_id = e.employee_id;

2. CTEs with Windows Functions:
   Question: You have a table orders with order_id, customer_id, order_date, and total_amount. How would you use a CTE to determine the month-over-month growth rate for your sales?

   Answer:

   WITH MonthlySales AS (
       SELECT MONTH(order_date) AS month, YEAR(order_date) AS year, SUM(total_amount) AS total
       FROM orders
       GROUP BY year, month
   ),
   SalesGrowth AS (
       SELECT month, year, total,
       LAG(total, 1, 0) OVER (ORDER BY year, month) AS prev_month_total
       FROM MonthlySales
   )
   SELECT month, year, total,
   ((total - prev_month_total) / prev_month_total) * 100 AS growth_rate
   FROM SalesGrowth;

3. Recursive CTE Limits:
   Question: Recursive CTEs can sometimes result in infinite loops. How would you ensure that a recursive CTE stops after a certain number of recursions, say 10 recursions?

   Answer: Add a counter in your recursive CTE, and use a WHERE clause to limit the number of recursions.

   WITH RECURSIVE CTE AS (
       SELECT ...
       UNION ALL
       SELECT ...
       WHERE counter < 10
   )
   ...

Such scenarios and questions test the depth of knowledge and understanding of the applicant in handling advanced SQL operations using CTEs.

Complex Applications (continued):

3. Finding Islands of Continuous Data:

Consider a logins table that has a login_date column. We want to identify stretches of continuous daily logins. That is, if a user logged in for continuous days, how do we segment these?

WITH RankedLogins AS (
    SELECT login_date,
           DATEDIFF(login_date, ROW_NUMBER() OVER (ORDER BY login_date)) as diff
    FROM logins
)

SELECT MIN(login_date) AS start_date, MAX(login_date) AS end_date
FROM RankedLogins
GROUP BY diff;

The concept here is to use a ranking function to assign a row number to each row. By taking the difference between the date and the row number, continuous dates will end up with the same difference value, which we use for grouping.

Complex Interview Questions (continued):

4. Fibonacci with Recursive CTE:
   Question: Can you generate the Fibonacci series for the first 10 numbers using a recursive CTE?

   Answer:

   WITH RECURSIVE Fibonacci AS (
       SELECT 0 AS prev_value, 1 AS curr_value, 1 AS counter
       UNION ALL
       SELECT curr_value, prev_value + curr_value, counter + 1
       FROM Fibonacci
       WHERE counter < 10
   )
   SELECT curr_value AS Fibonacci_Number FROM Fibonacci;

5. Nested Sets with CTE:
   Question: Given a table representing a hierarchical structure with columns id, parent_id, and name, can you use a recursive CTE to create a nested set representation with left and right values?

   Answer:

   WITH RECURSIVE NestedSet AS (
       SELECT id, parent_id, name, 
              ROW_NUMBER() OVER (ORDER BY name) * 2 - 1 AS lft,
              ROW_NUMBER() OVER (ORDER BY name) * 2 AS rgt
       FROM hierarchy
       WHERE parent_id IS NULL
       UNION ALL
       SELECT h.id, h.parent_id, h.name,
              ns.rgt + ROW_NUMBER() OVER (ORDER BY h.name) * 2 - 1,
              ns.rgt + ROW_NUMBER() OVER (ORDER BY h.name) * 2
       FROM hierarchy h
       JOIN NestedSet ns ON h.parent_id = ns.id
   )
   SELECT * FROM NestedSet ORDER BY id;

6. Path Resolution with Recursive CTE:
   Question: You are given a table named paths with columns id and parent_id, representing a file structure. How would you use CTE to find the full path for each id?

   Answer:

   WITH RECURSIVE PathResolver AS (
       SELECT id, CAST(id AS CHAR(255)) AS path
       FROM paths
       WHERE parent_id IS NULL
       UNION ALL
       SELECT p.id, CONCAT(pr.path, '/', p.id)
       FROM paths p
       JOIN PathResolver pr ON p.parent_id = pr.id
   )
   SELECT * FROM PathResolver;

These examples and questions help demonstrate the versatility of CTEs in SQL. When interviewing for advanced roles, such topics can truly challenge the depth of one's understanding and experience.

Compound Applications of CTEs:

CTEs can be combined with other MySQL features to tackle intricate scenarios. Below are complex scenarios that mix CTEs with various MySQL concepts:

Combining CTEs with Partitioning, Window Functions and Joins:

1. Determine Monthly Top 3 Customers by Sales:

Imagine a sales table with date, customer_id, and amount. Find the top 3 customers every month based on sales.

WITH MonthlySales AS (
    SELECT 
           DATE_FORMAT(date, '%Y-%m') AS month,
           customer_id, 
           SUM(amount) as total_sales,
           ROW_NUMBER() OVER (PARTITION BY DATE_FORMAT(date, '%Y-%m') ORDER BY SUM(amount) DESC) as rn
    FROM sales
    GROUP BY month, customer_id
)
SELECT month, customer_id, total_sales
FROM MonthlySales
WHERE rn <= 3;

2. Employee Salary Increase Compared to Average:

If you have an employee_salaries table with columns employee_id, year, salary, find employees whose salary increases were above the average increase that year.

WITH SalaryDifferences AS (
    SELECT 
           employee_id, 
           year, 
           salary - LAG(salary) OVER (PARTITION BY employee_id ORDER BY year) AS diff
    FROM employee_salaries
),
AvgDifference AS (
    SELECT year, AVG(diff) as avg_diff
    FROM SalaryDifferences
    GROUP BY year
)
SELECT sd.employee_id, sd.year, sd.diff
FROM SalaryDifferences sd
JOIN AvgDifference ad ON sd.year = ad.year
WHERE sd.diff > ad.avg_diff;

Combining CTEs with Subqueries and Aggregate Functions:

3. Categories with Above Average Products:

Given a products table with product_id, category_id, and price, find categories with an average product price greater than the overall average price.

WITH CategoryAverage AS (
    SELECT category_id, AVG(price) as avg_price
    FROM products
    GROUP BY category_id
),
OverallAverage AS (
    SELECT AVG(price) as overall_avg
    FROM products
)
SELECT ca.category_id, ca.avg_price
FROM CategoryAverage ca, OverallAverage oa
WHERE ca.avg_price > oa.overall_avg;

Combining CTEs with Temporary Tables and UNION:

4. Combine Active and Past Employees' Salaries:

Given two tables: active_employees and past_employees with similar structure (employee_id, name, and salary), find the top 10 earners ever.

WITH CombinedSalaries AS (
    SELECT employee_id, name, salary FROM active_employees
    UNION ALL
    SELECT employee_id, name, salary FROM past_employees
)
SELECT employee_id, name, salary
FROM CombinedSalaries
ORDER BY salary DESC
LIMIT 10;

Combining CTEs with CASE WHEN:

5. Tagging Sales Performance:

Using the sales table with salesperson_id and amount, categorize each salesperson's total sales as 'High', 'Medium', or 'Low'.

WITH SalesAggregates AS (
    SELECT salesperson_id, SUM(amount) as total_sales
    FROM sales
    GROUP BY salesperson_id
),
SalesBounds AS (
    SELECT 
        AVG(total_sales) as avg_sales, 
        STDDEV_POP(total_sales) as std_sales
    FROM SalesAggregates
)
SELECT 
    sa.salesperson_id, 
    sa.total_sales, 
    CASE 
        WHEN total_sales > (sb.avg_sales + sb.std_sales) THEN 'High'
        WHEN total_sales < (sb.avg_sales - sb.std_sales) THEN 'Low'
        ELSE 'Medium'
    END as Performance
FROM SalesAggregates sa, SalesBounds sb;

These are advanced use cases combining CTEs with other advanced SQL concepts. They can be useful for analytics, reporting, or data transformation tasks. Using these patterns as a base, one can further modify and adapt them to various requirements and scenarios.

Let's try to create some more sophisticated scenarios that combine multiple SQL concepts with CTEs:

Hierarchical Data Aggregation with Window Functions:

1. Employee Hierarchy and Cumulative Salary:

Imagine an employees table with columns employee_id, manager_id, name, and salary. We want to get the cumulative salary for each manager considering the salaries of all their direct and indirect subordinates.

WITH RECURSIVE EmployeeHierarchy AS (
    SELECT employee_id, manager_id, name, salary, 
           CAST(employee_id AS CHAR(255)) AS hierarchy
    FROM employees
    WHERE manager_id IS NULL
    UNION ALL
    SELECT e.employee_id, e.manager_id, e.name, e.salary,
           CONCAT(eh.hierarchy, '->', e.employee_id)
    FROM employees e
    JOIN EmployeeHierarchy eh ON e.manager_id = eh.employee_id
),
RankedHierarchy AS (
    SELECT employee_id, name, salary, hierarchy,
           ROW_NUMBER() OVER (ORDER BY hierarchy) as rank
    FROM EmployeeHierarchy
)
SELECT a.employee_id, a.name, a.salary, SUM(b.salary) as cumulative_salary
FROM RankedHierarchy a
JOIN RankedHierarchy b ON b.rank <= a.rank
WHERE LOCATE(b.employee_id, a.hierarchy) > 0
GROUP BY a.employee_id, a.name, a.salary;

Complex Time-Series Analysis:

2. User Activity Streaks:

From a user_activity table having a single row per activity with columns user_id and activity_date, identify users who've been active for 7 consecutive days more than once.

WITH RECURSIVE DateRanges AS (
    SELECT MIN(activity_date) as start_date, 
           DATE_ADD(MIN(activity_date), INTERVAL 6 DAY) as end_date
    FROM user_activity
    UNION ALL
    SELECT DATE_ADD(start_date, INTERVAL 1 DAY), 
           DATE_ADD(end_date, INTERVAL 1 DAY)
    FROM DateRanges
    WHERE end_date < (SELECT MAX(activity_date) FROM user_activity)
),
UserStreaks AS (
    SELECT dr.start_date, dr.end_date, ua.user_id,
           COUNT(DISTINCT ua.activity_date) as active_days
    FROM DateRanges dr
    JOIN user_activity ua ON ua.activity_date BETWEEN dr.start_date AND dr.end_date
    GROUP BY dr.start_date, dr.end_date, ua.user_id
    HAVING active_days = 7
)
SELECT user_id
FROM UserStreaks
GROUP BY user_id
HAVING COUNT(DISTINCT start_date) > 1;

Data Normalization using CTEs and Subqueries:

3. Converting Non-Normalized Data into 3NF:

Imagine having a table orders with columns: order_id, products_purchased (comma-separated product IDs), and total_price. The task is to convert this non-normalized data into Third Normal Form (3NF).

WITH RECURSIVE NormalizedOrders AS (
    SELECT order_id, 
           SUBSTRING_INDEX(products_purchased, ',', 1) as product,
           SUBSTRING(SUBSTRING_INDEX(products_purchased, ',', 2), LENGTH(SUBSTRING_INDEX(products_purchased, ',', 2 - 1)) + 1) AS remaining_products,
           total_price
    FROM orders
    WHERE products_purchased IS NOT NULL
    UNION ALL
    SELECT order_id,
           SUBSTRING_INDEX(remaining_products, ',', 1),
           SUBSTRING(SUBSTRING_INDEX(remaining_products, ',', 2), LENGTH(SUBSTRING_INDEX(remaining_products, ',', 2 - 1)) + 1),
           total_price
    FROM NormalizedOrders
    WHERE remaining_products IS NOT NULL
)
SELECT order_id, product, total_price
FROM NormalizedOrders;

These examples showcase advanced use cases combining multiple SQL concepts with CTEs. Crafting them requires not only knowledge of SQL syntax but also an understanding of the business logic and requirements.

Let's continue our deeper dive.

Using CTEs with JSON Functions and Temporary Tables:

MySQL introduced JSON functions from version 5.7 onwards. This has allowed developers to deal with semi-structured data directly within relational databases. When combined with CTEs, some powerful transformations are possible.

1. Extracting Nested JSON Data:

Suppose you have a table user_data with columns user_id and data where data is a JSON field containing user activities like:

{
    "purchases": [
        {"product_id": 1, "timestamp": "2022-01-01 12:00:00"},
        {"product_id": 2, "timestamp": "2022-01-05 12:00:00"}
    ],
    "views": [
        {"product_id": 3, "timestamp": "2022-01-02 12:00:00"},
        {"product_id": 4, "timestamp": "2022-01-03 12:00:00"}
    ]
}

You want to extract this data into a structured format.

WITH RECURSIVE JsonExtractor AS (
    SELECT user_id,
           JSON_UNQUOTE(JSON_EXTRACT(data, '$.purchases[0].product_id')) as product_id,
           JSON_UNQUOTE(JSON_EXTRACT(data, '$.purchases[0].timestamp')) as activity_time,
           'purchase' as activity_type,
           JSON_REMOVE(data, '$.purchases[0]') as remaining_data,
           1 as activity_index
    FROM user_data
    WHERE JSON_LENGTH(data, '$.purchases') > 0
    UNION ALL
    SELECT user_id,
           JSON_UNQUOTE(JSON_EXTRACT(remaining_data, CONCAT('$.purchases[', activity_index, '].product_id'))),
           JSON_UNQUOTE(JSON_EXTRACT(remaining_data, CONCAT('$.purchases[', activity_index, '].timestamp'))),
           'purchase',
           JSON_REMOVE(remaining_data, CONCAT('$.purchases[', activity_index, ']')),
           activity_index + 1
    FROM JsonExtractor
    WHERE JSON_LENGTH(remaining_data, '$.purchases') > activity_index
)
SELECT user_id, product_id, activity_time, activity_type
FROM JsonExtractor;

This CTE will handle the purchases array. You would need a similar CTE for the views array, and then combine the results using UNION ALL.

Combining CTEs with Full-Text Search:

Full-text search is essential for building search engines or implementing search functionality within a large dataset. When combined with CTEs, more complex search functionalities are feasible.

2. Fetching Related Articles:

Imagine a table articles with columns article_id, title, content, and tags. Now, you want to find articles related to a given article by its article_id.

WITH ArticleTags AS (
    SELECT article_id, 
           MATCH(tags) AGAINST ((SELECT tags FROM articles WHERE article_id = ?) IN NATURAL LANGUAGE MODE) AS relevance
    FROM articles
    WHERE article_id <> ?
)
SELECT article_id, relevance
FROM ArticleTags
WHERE relevance > 0.5
ORDER BY relevance DESC
LIMIT 5;

In this scenario, you're using a CTE to compute the relevance of each article's tags compared to the tags of the provided article_id. Then, the main query fetches the top 5 related articles based on this relevance score.

Using CTEs for Data Cleaning:

Data cleaning is an essential part of any data analysis or machine learning workflow. With CTEs, you can spot inconsistencies in your data and rectify them.

3. Identifying and Correcting Outliers:

Suppose you have a temperature_readings table with reading_date and temperature. You want to spot days where the temperature is more than 3 standard deviations away from the mean and replace it with the average of its neighbors.

WITH MeanAndStdDev AS (
    SELECT AVG(temperature) as avg_temp, STDDEV(temperature) as std_temp
    FROM temperature_readings
),
Outliers AS (
    SELECT reading_date
    FROM temperature_readings, MeanAndStdDev
    WHERE ABS(temperature - avg_temp) > 3 * std_temp
)
UPDATE temperature_readings tr1
JOIN Outliers o ON tr1.reading_date = o.reading_date
SET tr1.temperature = (
    (SELECT temperature FROM temperature_readings WHERE reading_date = DATE_SUB(tr1.reading_date, INTERVAL 1 DAY)) +
    (SELECT temperature FROM temperature_readings WHERE reading_date = DATE_ADD(tr1.reading_date, INTERVAL 1 DAY))
) / 2;

In this example, we first calculate the mean and standard deviation of the temperature readings. Then, using another CTE, we identify the outliers. Finally, the UPDATE statement modifies these outlier values to be the average of their neighbouring days.

Combining CTEs with other MySQL functionalities enables a more robust, clear, and maintainable SQL codebase, particularly for complex operations. The examples provided illustrate the depth and flexibility that SQL provides for data manipulation, especially when combined with CTEs.

Let's continue by blending CTEs with other advanced SQL functionalities, like partitioning and spatial functions.

Using CTEs with Partitioning:

Partitioning allows databases to enhance the performance of certain types of queries by segmenting the data into smaller, more manageable pieces.

1. Finding the Highest Selling Product for Each Year:

Imagine you have an orders table partitioned by year and a related order_details table containing products and quantities. You want to find the highest-selling product for each year.

WITH YearlyTopProduct AS (
    SELECT DATE_FORMAT(order_date, '%Y') as year,
           product_id,
           SUM(quantity) as total_quantity
    FROM orders o
    JOIN order_details od ON o.order_id = od.order_id
    GROUP BY DATE_FORMAT(order_date, '%Y'), product_id
),
RankedProducts AS (
    SELECT year, product_id, total_quantity,
           RANK() OVER (PARTITION BY year ORDER BY total_quantity DESC) as rnk
    FROM YearlyTopProduct
)
SELECT year, product_id, total_quantity
FROM RankedProducts
WHERE rnk = 1;

Using CTEs with Spatial Functions:

Spatial functions allow for operations on geometric values. This is especially useful for geographic data.

2. Finding Nearest Stores to a Given Location:

Assuming you have a stores table with a location column of type POINT indicating the store's location, and you want to find the nearest 3 stores to a given point.

WITH DistanceCalculation AS (
    SELECT store_id, store_name,
           ST_Distance_Sphere(location, ST_GeomFromText('POINT(latitude longitude)')) as distance
    FROM stores
)
SELECT store_id, store_name, distance
FROM DistanceCalculation
ORDER BY distance ASC
LIMIT 3;

Replace latitude and longitude with the actual coordinates of the given point.

Combining CTEs with Advanced Joins:

Using CTEs with advanced joins can create powerful combinations, especially for comparing data or creating complex aggregations.

3. Comparing Quarterly Sales of Two Consecutive Years:

Given an sales table with columns date and amount, you want to compare the quarterly sales of two consecutive years.

WITH QuarterlySales AS (
    SELECT YEAR(date) as year,
           QUARTER(date) as quarter,
           SUM(amount) as total_sales
    FROM sales
    GROUP BY YEAR(date), QUARTER(date)
),
YearlyComparison AS (
    SELECT q1.year as year1, q1.quarter as quarter1, q1.total_sales as sales1,
           q2.year as year2, q2.quarter as quarter2, q2.total_sales as sales2
    FROM QuarterlySales q1
    JOIN QuarterlySales q2 ON q1.quarter = q2.quarter AND q1.year = q2.year - 1
)
SELECT year1, quarter1, sales1, year2, quarter2, sales2,
       CASE WHEN sales1 > sales2 THEN 'Increase' 
            WHEN sales1 < sales2 THEN 'Decrease' 
            ELSE 'No Change' END as comparison
FROM YearlyComparison;

This example compares quarterly sales of two consecutive years, and also provides a verdict (Increase, Decrease, or No Change) based on the comparison.

By using CTEs in conjunction with other advanced SQL features, it's possible to construct powerful queries that can handle complex scenarios, transforming data as needed and producing detailed insights.

Let's delve further and combine CTEs with subqueries, pivoting, and sequence generation to handle more intricate scenarios.

CTEs with Subqueries and Pivoting:

1. Monthly Sales Report for Products:

Given a sales table with date, product_id, and amount, create a monthly report that shows the sales of each product, pivoting the months.

WITH MonthlySales AS (
    SELECT 
           MONTH(date) as month,
           product_id,
           SUM(amount) as total_sales
    FROM sales
    GROUP BY MONTH(date), product_id
)
SELECT 
    product_id,
    COALESCE(SUM(CASE WHEN month = 1 THEN total_sales ELSE 0 END), 0) as January,
    COALESCE(SUM(CASE WHEN month = 2 THEN total_sales ELSE 0 END), 0) as February,
    ... -- Repeat for all months
FROM MonthlySales
GROUP BY product_id;

CTEs with Sequence Generation:

2. Generating a Sequence of Dates:

Say you want to fill in missing dates in your sales data. First, you'd generate a sequence of dates, and then left join on your sales data to fill in the gaps.

WITH RECURSIVE DateSequence AS (
    SELECT '2022-01-01' as date
    UNION ALL
    SELECT DATE_ADD(date, INTERVAL 1 DAY)
    FROM DateSequence
    WHERE date <= '2022-12-31'
)
SELECT ds.date, IFNULL(s.total_sales, 0) as sales
FROM DateSequence ds
LEFT JOIN (
    SELECT date, SUM(amount) as total_sales
    FROM sales
    GROUP BY date
) s ON ds.date = s.date;

Combining CTEs for Multi-level Analysis:

3. Top 5 Customers Who Bought the Top 5 Products:

Using a sales database with a sales table (containing customer_id and product_id), you want to find out who the top customers are for the best selling products.

WITH TopProducts AS (
    SELECT product_id,
           SUM(amount) as total_sales
    FROM sales
    GROUP BY product_id
    ORDER BY total_sales DESC
    LIMIT 5
),
TopCustomers AS (
    SELECT customer_id,
           SUM(amount) as total_spent
    FROM sales
    WHERE product_id IN (SELECT product_id FROM TopProducts)
    GROUP BY customer_id
    ORDER BY total_spent DESC
    LIMIT 5
)
SELECT customer_id, total_spent
FROM TopCustomers;

CTEs with Hierarchical Data:

4. Fetching All Subordinates of an Employee:

Assuming you have an employees table with columns employee_id, name, and manager_id. Using CTEs, fetch all direct and indirect subordinates of a given employee.

WITH RECURSIVE Subordinates AS (
    SELECT employee_id, name, manager_id
    FROM employees
    WHERE manager_id = ? -- Given employee's ID
    UNION ALL
    SELECT e.employee_id, e.name, e.manager_id
    FROM employees e
    JOIN Subordinates s ON e.manager_id = s.employee_id
)
SELECT employee_id, name
FROM Subordinates;

The power of CTEs shines when combining them with various SQL functionalities, turning complex requirements into manageable, modular, and understandable SQL scripts. This also aids in performance optimization and data exploration, especially in large datasets where the interplay between different entities and metrics becomes intricate.

Let’s push further with additional advanced combinations using CTEs:

CTEs with Recursive Joins for Path Analysis:

1. Building a Path in a Referral System:

Assume you have a users table with columns user_id and referred_by, indicating who referred them. A challenge here is to build a referral path for a specific user.

WITH RECURSIVE ReferralPath AS (
    SELECT user_id, referred_by, CAST(user_id AS CHAR(255)) AS path
    FROM users
    WHERE user_id = ? -- Given user's ID
    UNION ALL
    SELECT u.user_id, u.referred_by, CONCAT(rp.path, '->', u.user_id)
    FROM users u
    JOIN ReferralPath rp ON u.user_id = rp.referred_by
)
SELECT path 
FROM ReferralPath 
WHERE referred_by IS NULL;

This would provide the chain of referrals for a user, which can be especially useful for understanding user acquisition paths or calculating referral bonuses.

CTEs with Aggregated Window Functions:

2. Calculating Rolling Averages with Rankings:

Given a sales table containing daily sales, calculate a 7-day rolling average, but only for dates that rank in the top 10 for sales.

WITH RankedSales AS (
    SELECT date, 
           SUM(amount) as daily_sales,
           RANK() OVER (ORDER BY SUM(amount) DESC) as sales_rank
    FROM sales
    GROUP BY date
),
RollingAverages AS (
    SELECT date, 
           daily_sales,
           AVG(daily_sales) OVER (ORDER BY date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as rolling_avg
    FROM RankedSales
    WHERE sales_rank <= 10
)
SELECT date, rolling_avg 
FROM RollingAverages;

CTEs with Conditional Aggregations:

3. Monthly Active/Inactive User Report:

For an activity_log table with user_id and activity_date, generate a report of active and inactive users by month. An active user is someone who logged activity more than 10 times in a month.

WITH MonthlyActivity AS (
    SELECT DATE_FORMAT(activity_date, '%Y-%m') as month,
           user_id,
           COUNT(*) as activity_count
    FROM activity_log
    GROUP BY DATE_FORMAT(activity_date, '%Y-%m'), user_id
)
SELECT month, 
       SUM(CASE WHEN activity_count > 10 THEN 1 ELSE 0 END) as active_users,
       SUM(CASE WHEN activity_count <= 10 THEN 1 ELSE 0 END) as inactive_users
FROM MonthlyActivity
GROUP BY month;

CTEs in Conjunction with Set Operations:

4. Find Products Sold Last Year But Not This Year:

From a sales table with product_id and date, determine which products haven't been sold this year but were sold the previous year.

WITH LastYearSales AS (
    SELECT DISTINCT product_id
    FROM sales
    WHERE YEAR(date) = YEAR(CURRENT_DATE) - 1
),
ThisYearSales AS (
    SELECT DISTINCT product_id
    FROM sales
    WHERE YEAR(date) = YEAR(CURRENT_DATE)
)
SELECT product_id 
FROM LastYearSales
EXCEPT
SELECT product_id 
FROM ThisYearSales;

By blending CTEs with a multitude of SQL techniques and functions, you can address multifaceted analytical challenges and data transformations, maintaining clarity and modularity in your SQL code.

Let's dive even deeper into challenging and complex scenarios where CTEs can be of great use.

CTEs with Lag/Lead Functions:

1. Finding Consecutive Days of Decline in Sales:

From a daily_sales table containing date and amount, determine periods where there were three or more consecutive days of declining sales.

WITH SalesWithLag AS (
    SELECT date, 
           amount,
           LAG(amount, 1) OVER (ORDER BY date) as prev_day_amount,
           LAG(amount, 2) OVER (ORDER BY date) as prev_2_day_amount
    FROM daily_sales
)
SELECT date 
FROM SalesWithLag
WHERE amount < prev_day_amount AND prev_day_amount < prev_2_day_amount;

CTEs with Custom Variables and Computation:

2. Calculating Cumulative Sales with Target Achievement Dates:

From a sales table containing date and amount, calculate cumulative sales and determine the dates when the cumulative sales exceeded certain milestones.

WITH CumulativeSales AS (
    SELECT date,
           amount,
           @cumulative := @cumulative + amount as cumulative_amount
    FROM sales,
    (SELECT @cumulative := 0) as init
    ORDER BY date
)
SELECT MIN(date) as achievement_date
FROM CumulativeSales
WHERE cumulative_amount > 100000;  -- Or whatever target value you're aiming for

CTEs with Handling Missing Data:

3. Finding Missing Dates in a Time Series:

In a logins table containing login_date, determine if there are any missing dates in a sequence.

WITH DateSeries AS (
    SELECT CURDATE() - INTERVAL (a.a + (10 * b.a) + (100 * c.a)) DAY as calendar_date
    FROM (SELECT 0 AS a UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9) as a
    CROSS JOIN (SELECT 0 AS a UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9) as b
    CROSS JOIN (SELECT 0 AS a UNION ALL SELECT 1 UNION ALL SELECT 2 UNION ALL SELECT 3 UNION ALL SELECT 4 UNION ALL SELECT 5 UNION ALL SELECT 6 UNION ALL SELECT 7 UNION ALL SELECT 8 UNION ALL SELECT 9) as c
)
SELECT calendar_date
FROM DateSeries
WHERE calendar_date BETWEEN (SELECT MIN(login_date) FROM logins) AND (SELECT MAX(login_date) FROM logins)
AND calendar_date NOT IN (SELECT login_date FROM logins);

CTEs with Complex Data Transformation:

4. Building a Product Recommendation Engine:

Imagine a purchases table containing user_id, product_id, and purchase_date. Using this table, you want to find products that are commonly bought together to provide product recommendations.

WITH ProductPairs AS (
    SELECT p1.user_id,
           p1.product_id as product_A,
           p2.product_id as product_B
    FROM purchases p1
    JOIN purchases p2 ON p1.user_id = p2.user_id AND p1.product_id < p2.product_id
)
SELECT product_A, product_B, COUNT(*) as frequency
FROM ProductPairs
GROUP BY product_A, product_B
HAVING frequency > 5   -- Or any threshold that seems fit
ORDER BY frequency DESC;

By intensively combining CTEs with a myriad of SQL concepts, one can construct multi-faceted, organized, and optimized SQL queries to solve intricate data tasks that often emerge in complex business requirements or data analytics challenges.

Let's further dissect some more scenarios that test the bounds of common SQL and CTE applications.

CTEs with Time Series Analysis:

1. Calculating Month-over-Month Growth Rate:

Suppose you have a sales table with columns date and amount. The task is to calculate the month-over-month growth rate.

WITH MonthlySales AS (
    SELECT DATE_FORMAT(date, '%Y-%m') as month,
           SUM(amount) as total_sales
    FROM sales
    GROUP BY DATE_FORMAT(date, '%Y-%m')
),
SalesWithLag AS (
    SELECT month, 
           total_sales,
           LAG(total_sales, 1) OVER (ORDER BY month) as prev_month_sales
    FROM MonthlySales
)
SELECT month,
       ((total_sales - prev_month_sales) / prev_month_sales) * 100 as growth_rate
FROM SalesWithLag
WHERE prev_month_sales IS NOT NULL;

CTEs with Event Sequencing:

2. Finding Users who Purchased Product A then Product B within 30 days:

Given a purchases table with user_id, product_id, and purchase_date, detect sequences of user behaviors.

WITH ProductA_Purchases AS (
    SELECT user_id, purchase_date AS first_purchase_date
    FROM purchases
    WHERE product_id = 'A'
),
ProductB_Purchases AS (
    SELECT user_id, purchase_date AS second_purchase_date
    FROM purchases
    WHERE product_id = 'B'
)
SELECT a.user_id, a.first_purchase_date, b.second_purchase_date
FROM ProductA_Purchases a
JOIN ProductB_Purchases b ON a.user_id = b.user_id
WHERE DATEDIFF(b.second_purchase_date, a.first_purchase_date) BETWEEN 1 AND 30;

CTEs with Filtering and Segmentation:

3. Segmenting Users Based on Purchase Frequency within a Time Period:

From a purchases table containing user_id and purchase_date, segment users based on their purchase frequency within the last year.

WITH YearlyPurchaseCount AS (
    SELECT user_id,
           COUNT(*) as purchase_count
    FROM purchases
    WHERE purchase_date BETWEEN DATE_SUB(CURDATE(), INTERVAL 1 YEAR) AND CURDATE()
    GROUP BY user_id
)
SELECT user_id, 
       CASE 
           WHEN purchase_count >= 10 THEN 'High Frequency'
           WHEN purchase_count BETWEEN 5 AND 9 THEN 'Medium Frequency'
           ELSE 'Low Frequency'
       END as segment
FROM YearlyPurchaseCount;

CTEs with Hierarchical Data and Aggregations:

4. Calculate Total Sales per Manager:

Imagine an employees table with employee_id, manager_id and an orders table with employee_id, order_id, and order_value. Here we aim to attribute sales values up the management chain.

WITH RECURSIVE ManagerSales AS (
    SELECT e.employee_id, e.manager_id, IFNULL(SUM(o.order_value), 0) as direct_sales
    FROM employees e
    LEFT JOIN orders o ON e.employee_id = o.employee_id
    GROUP BY e.employee_id, e.manager_id
    UNION ALL
    SELECT e.employee_id, e.manager_id, ms.direct_sales
    FROM employees e
    JOIN ManagerSales ms ON e.employee_id = ms.manager_id
)
SELECT manager_id, SUM(direct_sales) as total_sales
FROM ManagerSales
GROUP BY manager_id;

Leveraging CTEs with advanced SQL techniques can empower data engineers and analysts to tackle convoluted analytical challenges, making the solutions both modular and understandable. They prove invaluable in presenting layered data, sequential events, or aggregated results from intricate data models.

Let's dive into even more intricate scenarios with CTEs, pushing their integration with other SQL features:

CTEs with Dynamic Columns & Pivoting:

1. Pivoting Sales Data by Month:

Given a sales table with columns date and amount, convert it to display months as columns, with sales totals for each month.

SET SESSION group_concat_max_len = 1000000;

SET @sql = NULL;

SELECT GROUP_CONCAT(DISTINCT
    CONCAT(
      'SUM(CASE WHEN DATE_FORMAT(date, "%Y-%m") = "', 
      DATE_FORMAT(date, '%Y-%m'), 
      '" THEN amount ELSE 0 END) AS `', 
      DATE_FORMAT(date, '%Y-%m'), '`'
    )
  ) INTO @sql
FROM sales;

SET @sql = CONCAT('SELECT ', @sql, ' FROM sales GROUP BY DATE_FORMAT(date, "%Y-%m")');

PREPARE stmt FROM @sql;
EXECUTE stmt;
DEALLOCATE PREPARE stmt;

CTEs with Advanced Pattern Matching:

2. Find Patterns of A-B-A Purchases:

For a purchases table containing user_id, product_id, and purchase_date, find all sequences of purchases by users in an A-B-A pattern.

WITH RankedPurchases AS (
    SELECT user_id, product_id, purchase_date,
           ROW_NUMBER() OVER(PARTITION BY user_id ORDER BY purchase_date) as row_num
    FROM purchases
)
SELECT a.user_id,
       a.product_id AS first_product,
       b.product_id AS second_product,
       a.product_id AS third_product
FROM RankedPurchases a
JOIN RankedPurchases b ON a.user_id = b.user_id AND a.row_num = b.row_num - 1
JOIN RankedPurchases c ON a.user_id = c.user_id AND a.row_num = c.row_num - 2
WHERE a.product_id = 'A' AND b.product_id = 'B' AND c.product_id = 'A';

CTEs with Geospatial Data:

3. Find Nearest Stores:

Given a stores table with store_id, latitude, and longitude, for a given user's lat-long, find the nearest store.

WITH DistanceCalc AS (
    SELECT store_id,
           ( 6371 * acos( cos( radians(?UserLat) ) * cos( radians( latitude ) ) 
           * cos( radians( longitude ) - radians(?UserLong) ) 
           + sin( radians(?UserLat) ) * sin(radians(latitude)) ) ) AS distance
    FROM stores
)
SELECT store_id
FROM DistanceCalc
ORDER BY distance
LIMIT 1;

CTEs with JSON Parsing:

4. Extracting JSON Array Elements:

Given a user_data table containing a column user_preferences storing JSON arrays, fetch all unique elements from these arrays across all records.

WITH JSONValues AS (
    SELECT JSON_UNQUOTE(JSON_EXTRACT(user_preferences, '$[''0'']')) AS pref_value
    FROM user_data
    WHERE JSON_EXTRACT(user_preferences, '$[''0'']') IS NOT NULL
    UNION ALL
    SELECT JSON_UNQUOTE(JSON_EXTRACT(user_preferences, '$[''1'']'))
    FROM user_data
    WHERE JSON_EXTRACT(user_preferences, '$[''1'']') IS NOT NULL
    -- This can continue depending on the maximum expected length of JSON arrays
)
SELECT DISTINCT pref_value
FROM JSONValues;

Merging CTEs with these diverse SQL capabilities underscores the flexibility and power of the structured query language. The CTEs allow for modular, readable queries, even when the underlying operations are intrinsically complex.