SQL: Running Total Count of Distinct Values in Your Queries

Learn how to calculate the running total of unique values in your SQL queries using BigQuery for insightful data analysis.
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Mastering Running Total Count of Distinct Values in SQL

When working with SQL, particularly in a data warehouse environment like Google BigQuery, you might come across a requirement to analyze unique items bought by a customer over time. Imagine you're tracking customer purchases and you want to see how many unique products a customer has bought up to specific order dates. This need brings us to a common yet intriguing challenge in SQL data manipulation — calculating a running total count of distinct values.

The Problem

Let’s consider we have a table named example_table that records user purchases. The structure looks something like this:

user_idorder_dateproduct12021-01-01A12021-01-01B12021-01-04A12021-01-07C12021-01-09C12021-01-20AOur goal is to generate a list that not only shows each purchase but also indicates how many unique products the user has bought at any given date. For instance, by 2021-01-04, we want to see that the user has purchased 2 distinct products (A and B), and by 2021-01-07, the count should reflect 3 distinct products (A, B, C).

The Solution

To achieve this running total of unique products, we can leverage analytical SQL functions effectively. The following SQL query illustrates how to compute this distinct count:

[[See Video to Reveal this Text or Code Snippet]]

Breaking Down the Query

Subquery Creation:

We begin with a subquery that selects all data from the example_table.

We introduce a new computed column seqnum using the ROW_NUMBER() function. This column uniquely ranks the entries of each product bought by a user based on the order date.

[[See Video to Reveal this Text or Code Snippet]]

Main Query Analysis:

In the main query, the outer select uses the results of the subquery.

The COUNTIF(seqnum=1) function is employed to count occurrences where the sequence number is equal to 1 for each user, effectively giving us the running total of distinct products.

Ordering and Partitioning:

We utilize the PARTITION BY user_id ORDER BY order_date clause to ensure our running count resets for each unique user and orders the results by purchase dates.

Understanding the Output

The result of this query will generate an extended version of your original table, enriched with the unique running total count of products:

user_idorder_dateproductrunning_distinct_count12021-01-01A112021-01-01B212021-01-04A212021-01-07C312021-01-09C312021-01-20A3Through this outcome, you clear past confusion by accurately assessing unique product purchases over time — essential for detailed customer behavior analysis.

Wrapping Up

Calculating a running total count of distinct values in SQL may seem complex at first, but with analytical functions and a structured approach, you can unlock powerful insights from your data. Using the above methodology in Google BigQuery will aid you in effectively tracking unique items across dates, helping you make informed business decisions. Happy querying!

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