In SQL, the LAST_VALUE function is commonly used to return the last value in an ordered set of values. However, when used in conjunction with aggregate functions like SUM, COUNT, or AVG, the behavior of LAST_VALUE may not be intuitive. In this blog post, we will explore how to use LAST_VALUE with aggregate functions and discuss some important considerations.

Syntax

The syntax for using LAST_VALUE with aggregate functions is as follows:

SELECT 
   aggregate_function(LAST_VALUE(column) 
   OVER (ORDER BY column 
   ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)) 
FROM 
   table_name

Example

Let’s consider a sample table called sales with the following columns: product_name, date, and quantity_sold. We want to calculate the total quantity sold for each product, but also display the last quantity sold for each product.

SELECT
   product_name,
   SUM(quantity_sold) AS total_quantity_sold,
   LAST_VALUE(quantity_sold) 
      OVER (PARTITION BY product_name ORDER BY date 
      ROWS BETWEEN UNBOUNDED PRECEDING AND 
      UNBOUNDED FOLLOWING) AS last_quantity_sold
FROM
   sales
GROUP BY
   product_name;

In the above example, we use the PARTITION BY clause to partition the data by the product_name column. This ensures that the LAST_VALUE function is applied separately for each product. We then use the ORDER BY clause to sort the rows within each partition by the date column. Finally, we calculate the SUM of the quantity_sold column and select the LAST_VALUE of quantity_sold for each product.

Considerations

When using LAST_VALUE with aggregate functions, it is important to consider the window frame clause. In the example above, we used the clause ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING, which includes all rows within the partition. However, you can customize the window frame to include only a subset of rows if desired.

Another consideration is the performance impact of using LAST_VALUE with aggregate functions. Depending on the size of the dataset and the complexity of the query, it may have an impact on query execution time. Therefore, it is important to evaluate the performance of your query and consider indexing or other optimization techniques if necessary.

#SQL #AggregateFunctions