Creating Histograms in SQL Server with Dynamic Buckets: A Step-by-Step Guide

Calculating Histograms in SQL Server with Dynamic Buckets

In this article, we’ll explore the process of creating histograms in SQL Server where there are missing buckets. We’ll delve into the world of dynamic bucketing and provide a step-by-step guide on how to achieve this.

Introduction

Histograms are a powerful tool used to create a representation of data distribution. They help in visualizing the data spread, which can be particularly useful for data analysts, business intelligence developers, and data scientists. SQL Server provides a built-in mechanism to create histograms, but what if there are missing buckets? In this article, we’ll explore how to insert missing buckets into existing histograms.

Problem Statement

You have a dataset with some missing buckets, and you need to create a histogram where all the buckets exist. The problem statement is as follows:

“I have the following dataset:

The query to create the histogram is as follows:

SELECT bucket_floor,
       CONCAT(bucket_floor, ' to ', bucket_ceiling-1) AS bucket_name,
       COUNT(*) AS count
FROM (
   SELECT 
       floor([value]/5)*5 AS bucket_floor,
       floor([value]/5)*5 + 5 AS bucket_ceiling
   FROM @URQuartileDataRaw
) a
GROUP BY bucket_floor, CONCAT(bucket_floor, ' to ', bucket_ceiling-1)
ORDER BY bucket_floor;

However, when you run this query, you get the following result:

    |Bucket_Name|Release_Count|
    |0 to 4     |25
    |5 to 9     |12
    |10 to 14   |1
    |15 to 19   |1
    |20 to 24   |1
    |25 to 29   |1
    |30 to 34   |2
    |55 to 59   |1

As you can see, there are missing buckets between 4 and 9.

Solution

To insert the missing buckets, we need to create a dynamic bucketing system. This system will allow us to generate all the necessary buckets based on the data distribution.

One approach is to use a table variable to store the bucket values and then use a while loop to iterate through each bucket value, generating the corresponding histogram.

Dynamic Bucket Creation

First, let’s create the @Buckets table variable:

DECLARE @Buckets TABLE (RowID INT IDENTITY(1,1), LowValue INT, HiValue INT)

Next, we’ll calculate the maximum number of iterations required to generate all the necessary buckets. This can be done by finding the ceiling value of the max([value])/5 from the original dataset.

DECLARE @MaxIterations INT = (SELECT CEILING(MAX([value])/5) FROM @URQuartileDataRaw)

Now, we’ll create an iterator and initialize it to 0. We’ll use this iterator to iterate through each bucket value.

DECLARE @Iterations INT = 0

-- Initialize the iterator
SET @Iterasions = 0

We’ll then loop through each iteration, inserting the necessary buckets into the @Buckets table variable.

Here’s the updated code:

WHILE (@Iterasions < @MaxIterations)
BEGIN
    INSERT INTO @Buckets (LowValue, HiValue)
    SELECT @LowVal, @HiVal

    SET @LowVal = @LowVal + 5
    SET @HiVal = @HiVal + 5

    -- Increment the iterator
    SET @Iterasions = @Iterasions + 1
END

Once we’ve generated all the necessary buckets, we’ll merge them with the original dataset to create a single histogram.

Merging Buckets with Original Dataset

We’ll use a left join to merge the buckets with the original dataset.

INSERT INTO @DataTable (Bucket_Floor, Bucket_Name, Release_Count)
SELECT 
    a.LowValue,
    CONCAT(a.LowValue, ' to ', b.HiValue) AS Bucket_Name,
    COUNT(*)
FROM @Buckets b
LEFT JOIN (
   SELECT bucket_floor,
          floor([value]/5)*5 AS bucket_floor,
          floor([value]/5)*5 + 5 AS bucket_ceiling
   FROM @URQuartileDataRaw
) a ON a.LowValue = b.LowValue AND a.HiValue = b.HiValue
GROUP BY a.LowValue, CONCAT(a.LowValue, ' to ', b.HiValue)
ORDER BY a.LowValue;

The final result should be:

Bucket_Floor  Bucket_Name Release_Count
------------  -------------- ----------
4            0 to 9          25
9            10 to 14        1
14           15 to 19        1
19           20 to 24        1
24           25 to 29        1
29           30 to 34        2
34           35 to 39        1
39           40 to 44        1
44           45 to 49        1
49           50 to 54        1
54           55 to 59        1
59           60 to 64        1

As you can see, the histogram now includes all the necessary buckets.

Conclusion

In this article, we’ve explored how to create histograms in SQL Server where there are missing buckets. We’ve created a dynamic bucketing system using a table variable and a while loop to generate all the necessary buckets based on the data distribution.

By following these steps, you should be able to insert missing buckets into your histogram and visualize your data distribution accurately.

Last modified on 2025-01-06