Interview Segment: Rolling Average in SQL

Interviewer: Let's discuss the concept of a rolling average in SQL. Can you explain what it is and how it might be used?

You: Certainly! A rolling average, also known as a moving average, is a statistical calculation that averages a number of data points from a larger dataset over a specified window of time. This technique is commonly used in time series data to smooth out short-term fluctuations and highlight longer-term trends or cycles.

For example, if you have daily sales data and you want to calculate a 7-day rolling average, you would take the average of the sales for each day and the previous six days.

Interviewer: That makes sense. Can you demonstrate how to calculate a rolling average in SQL?

You: Sure! Let's consider a simple example. Suppose we have a table called Sales that contains daily sales data:

CREATE TABLE Sales ( SaleDate DATE, SalesAmount DECIMAL(10, 2) ); 
INSERT INTO Sales (SaleDate, SalesAmount) 
VALUES ('2023-08-01', 100.00), ('2023-08-02', 150.00), ('2023-08-03', 200.00), ('2023-08-04', 250.00), ('2023-08-05', 300.00), ('2023-08-06', 350.00), ('2023-08-07', 400.00), ('2023-08-08', 450.00);        

Now, let’s calculate the 3-day rolling average of the SalesAmount:

SELECT SaleDate, SalesAmount, AVG(SalesAmount) 
OVER ( ORDER BY SaleDate ROWS BETWEEN 2 PRECEDING AND CURRENT ROW )
 AS RollingAverage FROM Sales;        

Interviewer: Could you explain how that query works?

You: Of course! Here’s a breakdown of the query:

• AVG(SalesAmount) OVER (...): This calculates the average of the SalesAmount column. The OVER clause specifies how the calculation should be performed across the rows.
• ORDER BY SaleDate: This orders the rows by SaleDate, which is essential for calculating the rolling average in the correct sequence.
• ROWS BETWEEN 2 PRECEDING AND CURRENT ROW: This defines the window frame. It specifies that the average should be calculated for the current row and the two preceding rows. So, for each day, the rolling average will consider that day and the previous two days.

Interviewer: What would the output look like?

You: The output will display the SaleDate, SalesAmount, and the calculated RollingAverage for each date. For the first two rows, where there aren’t enough preceding rows to complete the 3-day window, the average will be calculated based on the available data.

The RollingAverage for August 3rd, for instance, is (100 + 150 + 200) / 3 = 150.00.

Interviewer: That’s a great explanation. Can you briefly discuss when and why you would use a rolling average in a real-world scenario?

You: Rolling averages are particularly useful in scenarios where you want to identify trends or smooth out data fluctuations. For example:

• Sales Analysis: To understand sales performance over time, smoothing out daily fluctuations.
• Stock Market Analysis: To analyze stock price trends by calculating moving averages over various time periods.
• Quality Control: To monitor processes in manufacturing, where you might want to observe trends in defect rates over time.

Using rolling averages helps to reduce noise in the data and focus on the underlying trend.

Interviewer: Excellent, thank you for the explanation!

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