Determining Optimal Histogram Bin Sizes

In data analysis, histograms are valuable tools for visually representing the distribution of data. While it's possible to generate histograms using scripting languages, can this process be accomplished directly within SQL? The answer is yes, and the following question delves into this topic.

The main challenge lies in defining the sizes of the histogram bins. In most cases, the goal is to group data into predefined ranges to obtain a more informative and comprehensive representation. The question presented provides an SQL query that groups data by an integer column called "total," but it also notes that the resulting rows are too numerous, making visualizing the distribution difficult.

The solution lies in bucketing the data into larger bins. The original SQL query can be modified to achieve this:

SELECT ROUND(total, -2) AS bucket,
       COUNT(*) AS count
FROM faults
GROUP BY bucket;

The ROUND function, with a negative argument, rounds the "total" values to the nearest predefined interval. In this case, the interval is set to -2, which means rounding to the nearest 100 (-2). This creates bins with ranges of [0-99], [100-199], and so on.

Grouping the data by the "bucket" column effectively combines the counts for values falling within each interval, resulting in a more concise and meaningful histogram. The output would resemble the example provided in the question:

 ------------ --------------- 
| total      | count(total)  |
 ------------ --------------- 
|    30 - 40 |            23 | 
|    40 - 50 |            15 | 
|    50 - 60 |            51 | 
|    60 - 70 |            45 | 
------------------------------

This technique provides a straightforward method for creating histograms in SQL, even when dealing with numeric data. By specifying appropriate bin sizes, analysts can obtain a clearer understanding of the data distribution and make more informed decisions.