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If you work with large amounts of data or complex calculations, Databricks SQL Window Functions can be your best friends. They provide the means to perform calculations across sets of rows that are related to the current row. This allows you to solve common data analytics problems such as calculating running totals, averages, counts, max, min, and so on in a flexible and clever way.
What are Window Functions?
In simplest terms, a Window Function performs a calculation across a set of table rows that are somehow related to the current row. This is comparable to the type of calculation that an aggregate function performs but differs by retaining the individual row identities and a window frame can be defined for the calculation.
Types of Window Functions
There are three broad categories of window functions:
- Ranking Functions (RANK, DENSE_RANK, ROW_NUMBER etc.)
- Aggregate Functions (SUM, AVG, COUNT etc.)
- Value Functions (LEAD, LAG, FIRST_VALUE, LAST_VALUE etc.)
Using Window Functions: An Example
Let’s suppose we have a table ‘sales’ that contains information about the sales of various products on different dates:
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CREATE TABLE sales ( product VARCHAR(10), saledate DATE, quantity INT ); |
We can calculate the total quantity sold for each product using the SUM() function with an OVER clause to define the window:
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SELECT product, saledate, quantity, SUM(quantity) OVER(PARTITION BY product) as total_quantity FROM sales; |
This will return a result set where for each row, the total_quantity field contains the total number of units sold for that particular product.
The power of Window Functions
While the above example is a simple use case, window functions become really powerful when you start using them with ORDER BY and ROWS BETWEEN within the OVER clause. This allows us for example to calculate running totals:
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SELECT product, saledate, quantity, SUM(quantity) OVER(PARTITION BY product ORDER BY saledate ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as running_total FROM sales; |
Here, for each row, the running_total field contains the total quantity of the product sold up to and including the saledate for that row. This ensures that our total is calculated based on the date sequence, which can be particularly useful for time-based analysis.
Conclusion
Window functions are a powerful tool in any data analyst’s arsenal, allowing you to create flexible and performant queries to analyze your data. So go ahead and start experimenting and you’ll soon discover that they can make your SQL queries more compact, easier to read, and efficient.