Avoiding Double Counting in Filtered Aggregation: A Deep Dive into Trino

Introduction

Filtered aggregation is a powerful feature in SQL that allows you to count the number of rows that meet certain conditions. However, when dealing with overlapping intervals, it’s easy to end up counting the same row multiple times, leading to inaccurate results. In this article, we’ll explore how to avoid double counting in filtered aggregation using Trino.

Understanding Filtered Aggregation

Filtered aggregation allows you to specify a filter condition for each aggregation operation. The filter condition is applied after the rows are selected based on the specified columns. This means that if you’re aggregating over a column that contains dates, you need to be careful when specifying your filter conditions to avoid counting the same row multiple times.

The Problem with Double Counting

In our example query, we’re trying to count the number of shoppers who made their last purchase in the last 30 days, 60 days, and 90 days. However, the current query is causing double counting because the filter conditions for total_active_p60 and total_active_p90 are not correctly excluding rows that were already counted in total_active_p30.

The Incorrect Query

Let’s take a closer look at the incorrect query:

count(*) FILTER (where last_purchase_timestamp >= DATE '2022-03-05' - INTERVAL '60' day) 
AS total_active_p60,

count(*) FILTER (where last_purchase_timestamp >= DATE '2022-03-05' - INTERVAL '90' day)
                        and last_purchase_timestamp < ('2022-03-05'::date - INTERVAL '30' day)) 
AS total_active_p90

As we can see, the query for total_active_p60 is not excluding rows that were already counted in total_active_p30. This means that if a shopper made their last purchase on March 5th and also made another purchase within the 60-day window, they’ll be counted twice.

The Correct Query

To avoid double counting, we need to modify our query to exclude rows that were already counted in total_active_p30. Here’s the corrected query:

count(*) FILTER (where last_purchase_timestamp >= ('2022-03-05'::date - INTERVAL '30' day)) 
AS total_active_p30,

count(*) FILTER (where last_purchase_timestamp >= ('2022-03-05'::date - INTERVAL '60' day)
                            and last_purchase_timestamp < ('2022-03-05'::date - INTERVAL '30' day)) 
AS total_active_p60,

count(*) FILTER (where last_purchase_timestamp >= ('2022-03-05'::date - INTERVAL '90' day)
                        and last_purchase_timestamp < ('2022-03-05'::date - INTERVAL '60' day)) 
AS total_active_p90

By adding the additional filter condition last_purchase_timestamp < ('2022-03-05'::date - INTERVAL '30' day), we’re excluding rows that were already counted in total_active_p30. This ensures that each shopper is only counted once.

Conclusion

Avoiding double counting in filtered aggregation can be tricky, but by understanding the correct logic and filter conditions, you can ensure accurate results. In this article, we’ve explored how to avoid double counting using Trino’s filtered aggregation feature. By following these tips and best practices, you’ll be able to write more accurate and reliable queries that meet your data analysis needs.

Additional Considerations

When dealing with overlapping intervals, it’s essential to consider the correct logic and filter conditions. Here are a few additional considerations to keep in mind:

Always apply filter conditions after rows have been selected based on the specified columns.
Use parentheses to group complex filter conditions and avoid ambiguity.
Test your queries thoroughly to ensure accurate results.

Common Mistakes

Here are some common mistakes to watch out for when dealing with filtered aggregation:

Failing to exclude overlapping intervals
Using incorrect logic or filter conditions
Not testing queries thoroughly enough

Last modified on 2024-04-16