Aggregate Data with Date Ranges

In this article, we will explore how to aggregate data with date ranges. This involves combining overlapping time periods into a single range for the same values of weight and factor.

Understanding the Problem

The problem statement presents a table #CategoryWeight with columns CategoryId, weight, factor, startYear, and endYear. The task is to aggregate this data by combining consecutive date ranges for each category, weight, and factor value.

Why it’s Challenging

The challenge lies in the fact that the same year can have different values on different rows. This means that any assumption about the data becomes troublesome, making it difficult to determine how to handle overlapping time periods.

Solution Overview

To address this issue, we will use a recursive Common Table Expression (CTE) and a gaps-and-islands trick to expand out the data and then recombine it into chunks where the values are the same.

Recursive CTE for Data Expansion

We start by creating a recursive CTE that expands out the data. The idea is to add one year to each existing row and repeat this process until there are no more years to add.

with cte as (
      select categoryid, weight, factor,
             convert(int, startyear) as year, convert(int, endyear) as endyear
      from categoryweight
      union all
      select categoryid, weight, factor,
             year + 1, endyear
      from cte
      where year < endyear
     )

In this CTE, we first select the distinct rows from the original table and then add one year to each existing row using a recursive join. We repeat this process until there are no more years to add, which is ensured by the where year < endyear clause.

Gaps-and-Islands Trick for Data Aggregation

Next, we use a gaps-and-islands trick to recombine the data into chunks where the values are the same. The idea is to identify the gaps in the data (i.e., the years where there is no corresponding row) and then group the remaining rows by these gaps.

select categoryid, weight, factor, min(year), max(year)
from (
  select categoryid, weight, factor, year,
         row_number() over (partition by categoryid, weight, factor order by year) as seqnum
  from (select distinct categoryid, weight, factor, year from cte) cte
) cte
group by categoryid, weight, factor, (year - seqnum)
order by categoryid, min(year)
option (maxrecursion 0);

In this query, we first use the row_number() function to assign a unique sequence number to each row within each group. We then group the rows by these sequence numbers and calculate the minimum and maximum years for each group.

Example Walkthrough

Let’s walk through an example using the provided table data:

The recursive CTE expands out the data, and we get:

The gaps-and-islands trick groups the rows by the years where there is no corresponding row. We get:

The final result is a table with the category ID, weight, factor, minimum year, and maximum year for each group.

Conclusion

In this article, we explored how to aggregate data with date ranges. We used a recursive Common Table Expression (CTE) and a gaps-and-islands trick to expand out the data and then recombine it into chunks where the values are the same. The resulting table provides a simplified view of the original data by combining overlapping time periods for each category, weight, and factor value.

Additional Tips

• Always use proper data types (e.g., decimal or numeric) instead of float to avoid precision issues.
• Ensure that your database schema accurately reflects the structure of your data.

Last modified on 2024-04-01