SQL Blunders: The Disastrous Approach of Ignoring Window Functions for Basic Aggregations

First of each Group

Problem Statement:

Cats are vain. Each cat would like to pretend it has the lowest weight for its color.

Print cat name, color and the minimum weight of cats with that color.

Return: name, color, lowest_weight_by_color
Order by: color, name

Table Schema - Cats:

Table Data: Cats

Desired Output:

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Solution 1: Using CTE (Common Table Expression)

WITH lowest_weight_by_color AS (
  SELECT
    color,
    MIN(weight) AS lowest_weight_by_color
  FROM
    cats
  GROUP BY
    color
)
SELECT
  c.name,
  c.color,
  lw.lowest_weight_by_color
FROM
  cats c
JOIN
  lowest_weight_by_color lw ON c.color = lw.color
ORDER BY
  c.color,
  c.name;

• Approach: This solution uses a CTE to first find the minimum weight for each color. It then joins this result back with the original cats table to select the cat names, their color, and the corresponding minimum weight for that color.
• Correctness: This solution correctly addresses the requirement to display the cat name, color, and the lowest weight for cats of that color. By joining the CTE with the original table, it ensures that each cat's name is shown next to the minimum weight of its color category.
• Efficiency: While this solution is logically sound, it might not be the most efficient due to the join operation. Depending on the size of the dataset, joining tables can be more resource-intensive than necessary for this task.

Solution 2: Using Window Function

SELECT DISTINCT
  name,
  color,
  FIRST_VALUE (weight) OVER (
    PARTITION BY
      color
    ORDER BY
      weight
  ) AS lowest_weight_by_color
FROM
  cats
ORDER BY
  color,
  name;

• Approach: This solution uses a window function (FIRST_VALUE over a partition by color) to directly compute the lowest weight for each color alongside each cat's name and color without needing a join.
• Correctness: This solution might seem to directly address the question by displaying each cat's name, color, and a weight value that represents the lowest weight within that color group. However, it incorrectly associates the lowest weight with every cat in the color group, potentially leading to misunderstanding since it doesn't explicitly show the "lowest weight by color" but rather repeats the lowest weight for every entry in that color.
• Efficiency: Window functions are generally efficient for partitioned data operations, and this approach avoids the need for a join, which could make it faster on large datasets. However, the output might not be as clear in conveying that the weight shown is the minimum for the color group, not necessarily the weight of the cat named in the row.

Both solutions aim to achieve the same result but take different approaches. Let's compare their efficiency and correctness based on the given task.

Best Solution

The best solution depends on the interpretation of the requirements:

• If the requirement is to associate each cat with the minimum weight of its color (implying all cats of the same color will have the same weight displayed), Solution 2 is more efficient as it directly achieves this with less computational effort.
• If the intention is to clearly display the minimum weight alongside each cat's actual weight, making it clear that the weight shown is a grouped minimum and not the individual's actual weight, Solution 1 is more accurate and transparent about what the data represents, despite being potentially less efficient.

Given the question's wording, Solution 1 is preferable for clarity and correctness, as it accurately reflects the intention to show the minimum weight per color alongside each cat, ensuring there's no misunderstanding about what the weight value represents. It directly matches the requirement to "print cat name, color, and the minimum weight of cats with that color," making it clear that the weight is a property of the group, not necessarily of the individual cat listed.

Solution 2 indeed has advantages in terms of efficiency, primarily due to its use of a window function. Window functions can be more performant than equivalent operations involving joins, especially as the dataset grows in size. Here's why Solution 2 could be seen as more efficient:

Efficiency and Performance

• Window Functions: Solution 2 leverages FIRST_VALUE as a window function, which allows for a more straightforward and potentially faster computation. Window functions operate over a specified "window" of rows related to the current row, in this case, partitioned by color and ordered by weight. This means for each cat, the database engine computes the lowest weight within its color group without having to perform a separate aggregation and join operation.
• Avoids Joins: By not requiring a separate join between a CTE or subquery result and the original table, Solution 2 avoids the overhead associated with join operations, which can be significant depending on the join algorithm used by the database and the size of the dataset.
• Simpler Execution Plan: The execution plan for a window function can be simpler than that for a query involving joins. Database engines are generally very good at optimizing window functions, especially for partitioning and ordering operations, which can lead to faster query execution times.

Use Case Consideration

While Solution 2 is more efficient, it's essential to consider the use case. Solution 2 assigns the minimum weight of the color group to every cat within that color, which might be misinterpreted as the actual weight of each cat rather than the group's minimum weight. For clarity, the requirements should explicitly allow this interpretation. If the goal is to efficiently identify the minimum weight per color group and associate every cat within that group with this minimum weight, Solution 2 is indeed more efficient and directly achieves this goal.

If the primary concern is computational efficiency and the output's interpretation aligns with the query's intent, Solution 2 is a more efficient choice due to its use of window functions and avoidance of additional join operations.

Summary:

Using window functions to solve the given task involves leveraging the efficiency and simplicity of SQL's window functions to obtain the desired result.

Using Window Function approach ensures that each cat is associated with the lowest weight found within its color group, adhering to the requirements with increased efficiency and reduced computational overhead compared to a solution that uses joins.

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• SQL  
• Window Functions