Window functions for advanced analytics in MYSQL

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Window functions, also known as analytical functions or windowed aggregates, are a powerful feature in SQL that allow you to perform advanced analytics and calculations on sets of rows related to the current row within a query result set. These functions operate over a "window" of rows defined by an OVER() clause and are commonly used for tasks like ranking, aggregating, and calculating running totals.

MySQL introduced support for window functions starting with version 8.0.2. Here are some common window functions and how to use them in MySQL:

ROW_NUMBER(): Assigns a unique integer value to each row within a result set, typically used for ranking.

sql code

SELECT

    customer_id,

    order_id,

    ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS row_num

FROM orders;

RANK() and DENSE_RANK(): Assigns a rank to each row within a result set, with optional handling of tied values.

sql code

SELECT

    employee_id,

    salary,

    RANK() OVER (ORDER BY salary DESC) AS rank,

    DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rank

FROM employees;

NTILE(): Divides the result set into specified numbers of roughly equal parts (tiles) and assigns a bucket or tile number to each row.

sql code

SELECT

    employee_id,

    salary,

    NTILE(4) OVER (ORDER BY salary) AS salary_quartile

FROM employees;

LEAD() and LAG(): Access data from subsequent or previous rows within the result set.

sql code

SELECT

    order_date,

    sales_amount,

    LAG(sales_amount) OVER (ORDER BY order_date) AS prev_sales,

    LEAD(sales_amount) OVER (ORDER BY order_date) AS next_sales

FROM sales;

SUM(), AVG(), MIN(), MAX() OVER(): Calculate aggregate values over a window of rows.

sql code

SELECT

    department,

    salary,

    AVG(salary) OVER (PARTITION BY department) AS avg_salary,

    MAX(salary) OVER (PARTITION BY department) AS max_salary

FROM employees;

FIRST_VALUE() and LAST_VALUE(): Retrieve the first or last value within a window.

sql code

SELECT

    product_id,

    sale_date,

    sale_price,

    FIRST_VALUE(sale_price) OVER (PARTITION BY product_id ORDER BY sale_date) AS first_sale_price,

    LAST_VALUE(sale_price) OVER (PARTITION BY product_id ORDER BY sale_date) AS last_sale_price

FROM sales;

PERCENT_RANK(): Calculates the relative rank of a row within a result set as a percentage.

sql code

SELECT

    student_id,

    score,

    PERCENT_RANK() OVER (ORDER BY score DESC) AS percentile_rank

FROM exam_scores;

These are just a few examples of how you can use window functions in MySQL for advanced analytics. Window functions can significantly simplify complex queries and provide insights into your data that would be challenging to achieve otherwise. Always consult the MySQL documentation and consider your specific analytical requirements when using these functions.

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