Retiago Drago

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# Queries Quality and Percentage | LeetCode | MSSQL

## The Problem

We are given a table called `Queries` with the following structure:

Column Name Type
query_name varchar
result varchar
position int
rating int

This table contains information collected from some queries on a database. The position column ranges from 1 to 500, and the rating column ranges from 1 to 5. Queries with a rating less than 3 are considered as poor.

We define two metrics:

• Query quality: the average ratio of query rating to its position.
• Poor query percentage: the percentage of all queries with a rating less than 3.

The task is to write an SQL query that calculates these metrics for each `query_name`. Both should be rounded to 2 decimal places.

Example:

Input:

Queries table:

query_name result position rating
Dog Golden Retriever 1 5
Dog German Shepherd 2 5
Dog Mule 200 1
Cat Shirazi 5 2
Cat Siamese 3 3
Cat Sphynx 7 4

Output:

query_name quality poor_query_percentage
Dog 2.50 33.33
Cat 0.66 33.33

## The Solution

We'll discuss four SQL solutions with varying degrees of complexity. The key difference among these solutions is the SQL features they rely on, such as window functions, conditional aggregates, and distinct values.

### Source Code 1: Simple Aggregation

This approach uses simple SQL aggregates and a conditional count to calculate the metrics.

``````SELECT
query_name,
ROUND(SUM(rating * 1.0 / position) / COUNT(query_name), 2) AS quality,
ROUND(
SUM(
CASE
WHEN rating < 3 THEN 1
ELSE 0
END
) * 100.0 / COUNT(query_name)
, 2) AS poor_query_percentage
FROM Queries
GROUP BY query_name
``````

Runtime: 4383ms, beats 5.50% of submissions on LeetCode.

### Source Code 2: Window Functions

This solution utilizes window functions, which allow calculations across a set of rows related to the current row. This makes the solution more flexible and potentially more efficient.

``````SELECT DISTINCT
query_name,
ROUND(
SUM(rating * 1.0 / position) OVER (PARTITION BY query_name)
/ COUNT(query_name) OVER (PARTITION BY query_name)
, 2) AS quality,
ROUND(
SUM(
CASE
WHEN rating < 3 THEN 1
ELSE 0
END
)  OVER (PARTITION BY query_name)
* 100.0 / COUNT(query_name)  OVER (PARTITION BY query_name)
,

2) AS poor_query_percentage
FROM Queries
``````

Runtime: 3440ms, beats 30.10% of submissions on LeetCode.

### Source Code 3: Window Functions with Average

This approach also uses window functions but incorporates the AVG function for a more straightforward calculation of the quality metric.

``````SELECT DISTINCT
query_name,
ROUND(AVG(rating * 1.0 / position) OVER (PARTITION BY query_name), 2) AS quality,
ROUND(
SUM(
CASE
WHEN rating < 3 THEN 1
ELSE 0
END
)  OVER (PARTITION BY query_name)
* 100.0 / COUNT(query_name)  OVER (PARTITION BY query_name)
, 2) AS poor_query_percentage
FROM Queries
``````

Runtime: 4158ms, beats 8.48% of submissions on LeetCode.

### Source Code 4: Aggregation with Average

This solution resembles the first one, but it replaces the complex sum expression with the AVG function for the quality calculation, improving readability.

``````SELECT
query_name,
ROUND(AVG(rating * 1.0 / position), 2) AS quality,
ROUND(
SUM(
CASE
WHEN rating < 3 THEN 1
ELSE 0
END
) * 100.0 / COUNT(query_name)
, 2) AS poor_query_percentage
FROM Queries
GROUP BY query_name
``````

Runtime: 4284ms, beats 6.76% of submissions on LeetCode.

## Conclusion

Each solution offers unique insights into SQL coding strategies, showcasing the trade-offs between readability and performance. Among the four, Source Code 2 provides the best performance in the LeetCode environment. However, please note that actual performance may vary in a real-world RDBMS due to factors like data distribution and indexing.

Ranked from the best to worst based on the LeetCode benchmark:

1. Source Code 2
2. Source Code 3
3. Source Code 4
4. Source Code 1

You can find the original problem at LeetCode.

For more insightful solutions and tech-related content, feel free to connect with me on my Beacons page.

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