Alternative Approach for COUNT(1)

When working with databases, especially those that use SQL as a query language, it’s not uncommon to encounter situations where a seemingly straightforward query takes an excessively long time to execute. The question presented here revolves around optimizing a query that aims to count the total number of cargodetails on the selected row if it has a matching reference or booking.

Understanding the Original Query

The original query is as follows:

SELECT Column_Names
WHEN department = 'e'
THEN 
(
  SELECT COUNT(1) FROM AMAMDBECUGROUPAGE.dbo.cargodetail WITH (nolock)
  WHERE AMAMDBECUGROUPAGE.dbo.cargodetail.inactive = 0 
  AND CAST(AMAMDBECUGROUPAGE.dbo.cargodetail.cargonrbooking AS varchar) = CAST(dbo.StockCargo.reference AS varchar)
)
ELSE ''
END AS [#Cargos]
FROM dbo.StockCargo WITH (nolock)

This query, while functional, exhibits performance issues that lead to execution times exceeding 10 minutes. Despite the presence of indexes, no improvement is seen in the query’s performance.

The Problem with INNER JOIN

The provided alternative suggests converting the WHERE clause into an INNER JOIN. This approach leads to a new query:

SELECT COUNT(1) FROM AMAMDBECUGROUPAGE.dbo.cargodetail WITH (nolock) 
INNER JOIN dbo.StockCargo WITH (nolock) ON 
CAST(AMAMDBECUGROUPAGE.dbo.cargodetail.cargonrbooking AS varchar) = CAST(dbo.StockCargo.reference AS varchar)
WHERE AMAMDBECUGROUPAGE.dbo.cargodetail.inactive = 0

Although this approach offers a significant improvement in execution time (from over 10 minutes to mere seconds), the results are not as expected. This discrepancy highlights that there is more to query optimization than just faster execution times.

A Deeper Look into LEFT JOIN

The alternative solution proposed by one of the answerers makes use of a LEFT JOIN sub-query:

SELECT Column_Names
      ,CASE 
            WHEN department = 'e'
            THEN counts
            ELSE ''
       END AS [#Cargos]
FROM dbo.StockCargo ST WITH (nolock)
LEFT JOIN
(
    SELECT cargonrbooking
          ,COUNT(1)
    FROM AMAMDBECUGROUPAGE.dbo.cargodetail
    WHERE inactive = 0 
    GROUP BY cargonrbooking
) DS (cargonrbooking, counts)
    ON CAST(DS.cargonrbooking AS varchar) = CAST(ST.reference AS varchar)

The LEFT JOIN here plays a crucial role in achieving the desired result. Instead of joining on the exact matching value between ST.reference and DS.cargonrbooking, we join them based on their equality after casting to strings.

The Power of Left Join

A key benefit of using LEFT JOIN is that it allows for handling rows from both tables, even if there’s no match in either. However, when combined with string matching via CAST() AS varchar, this setup can help avoid the performance issues encountered by the original query and improve the accuracy of the results.

Temporary Tables: An Alternative Approach

Another answer suggests using temporary tables to further optimize the query:

SELECT Column_Names
      ,CASE 
            WHEN department = 'e'
            THEN DS.counts
            ELSE ''
       END AS [#Cargos]
FROM dbo.StockCargo ST WITH (nolock)
LEFT JOIN
(
    SELECT cargonrbooking
          ,COUNT(1) as counts
    FROM AMAMDBECUGROUPAGE.dbo.cargodetail
    WHERE inactive = 0 
    GROUP BY cargonrbooking
) DS (cargonrbooking, counts)
    ON CAST(DS.cargonrbooking AS varchar) = CAST(ST.reference AS varchar)

Here, instead of using a sub-query to get the count, we’re storing this result in a temporary table, DS, and joining it with our original query. This reduces the number of iterations through the data and could provide further performance benefits.

Conclusion

In conclusion, optimizing database queries often requires creative thinking about how to restructure or manipulate your SQL code to achieve better performance. By shifting from traditional approaches like INNER JOIN or even the original sub-query solution, using tools like temporary tables can lead to more efficient query execution times while maintaining the accuracy of results.

This example illustrates the importance of understanding the underlying mechanics of database queries and how creative problem-solving can be used to address seemingly intractable performance issues.

Last modified on 2025-02-23