Gaps & Islands Problem: Aggregating Adjacent Rows

The problem at hand is to aggregate adjacent rows based on certain conditions. In this case, we want to group by the 2nd column, return the first value from the 3rd column, the last value from the 4th column, and the sum of all values in the 5th column.

Background

The problem presented is a variation of a classic problem known as “gaps & islands.” It involves partitioning rows into groups based on certain conditions and then aggregating values within each group. In this case, we’re dealing with a table that has an unknown number of rows and columns.

To approach this problem, we need to understand the underlying concepts of gaps and islands, row numbering, and aggregation.

Row Numbering

In SQL, we can use the ROW_NUMBER() function to assign a unique number to each row within a partition. The PARTITION BY clause is used to define the boundaries for each partition. In our case, we want to partition by the 2nd column (c2) and then order the rows by the 3rd column (c1).

The first part of our solution uses this concept to create a new column (gp) that represents the group number.

Grouping and Aggregation

Once we have the grouped data, we can use aggregation functions like SUM() to calculate the desired values. In this case, we want to return the sum of all values in the 5th column (c5).

To achieve this, we need to use a combination of window functions and grouping.

Solution Overview

Our solution involves three main steps:

1. Partitioning into groups using row numbering.
2. Aggregating values within each group.
3. Returning the desired results.

Here’s the step-by-step explanation with code examples.

Step 1: Partitioning into Groups

We start by creating a temporary view that partitions our data into groups based on the 2nd column (c2). We use ROW_NUMBER() to assign a unique number to each row within a partition and then subtract this number from an overall sequential number to get the group number.

with g as (
  select *,
    Row_Number() over (order by c1) 
      - Row_Number() over(partition by c2 order by c1) gp
  from t
),
fl as (
  select *,
    First_Value(c3) over(partition by gp order by c1) f, 
    First_Value(c4) over(partition by gp order by c1 desc) l
  from g
)

In this code:

• We create a temporary view g that includes all columns (*) from the original table.
• We use ROW_NUMBER() to assign a unique number to each row within a partition, ordered by the 3rd column (c1). We then subtract this number from an overall sequential number to get the group number (gp).
• The resulting view is then used as input for another temporary view fl, which extracts specific values from the original table.

Step 2: Aggregating Values Within Each Group

Now that we have our grouped data, we can use aggregation functions like SUM() to calculate the desired values. In this case, we want to return the first value from the 3rd column (c3), the last value from the 4th column (c4), and the sum of all values in the 5th column (c5).

select c2, f, l, Sum(c5) s
from fl
group by c2, f, l
order by Min(c1);

In this code:

• We use a SELECT statement to extract specific values from the previous view (fl). In this case, we’re interested in c2, f, l, and s.
• We group the data by c2, f, and l using the GROUP BY clause.
• Finally, we use SUM() to calculate the sum of all values in the 5th column (c5). The results are ordered by the minimum value in the 1st column (c1) for better readability.

Step 3: Final Results

The final output should match the desired aggregation result:

This output shows the desired aggregation result, where A is grouped with its first and last values in columns c3 and c4, respectively, and the sum of all values in column c5. Similarly, D and B are also aggregated according to their respective group rules.

Conclusion

In this article, we explored a classic problem known as “gaps & islands” where adjacent rows need to be aggregated based on certain conditions. We demonstrated how to solve this problem using row numbering, grouping, and aggregation functions in SQL.

The provided solution involves three main steps: partitioning into groups, aggregating values within each group, and returning the desired results. By understanding these concepts and applying them correctly, we can efficiently handle complex data aggregation tasks.

Feel free to ask any questions or provide feedback on this article!

Last modified on 2024-02-04