Uncovering the Hidden Values: A Deep Dive into SQL Query Optimization

As a technical blogger, I’ve encountered numerous questions on Stack Overflow that showcase the complexities of SQL queries. Recently, a user posed an intriguing question about retrieving non-common values from two different columns of two different tables. In this article, we’ll delve into the query optimization process and explore ways to achieve the desired outcome.

Understanding the Problem Statement

The original query involves joining two tables: vw_summary (alias zone1) and vw_advice (alias zone2). The user wants to retrieve non-common values from columns val1 and val2 in both tables. To be specific, they’re looking for the following records:

The user’s query uses the LISTAGG function to concatenate values in staff_ids column from zone2. However, they’re struggling to get the desired outcome.

Initial Query Analysis

Let’s examine the initial query:

SELECT zone1.Id, zone1.VAL1, zone1.Org, zone2.staff_ids
FROM (
  SELECT ta1.Id, ta1.VAL1, ta1.P_DATE, ta1.Org 
  FROM vw_summary ta1 
  LEFT JOIN tbl_staff t3 ON t3.staff_id IN (ta1.Org) 
) zone1 
LEFT JOIN (
  SELECT tb1.advice_id, tb1.VAL1,
         LISTAGG(t1.ID, ',') WITHIN GROUP (ORDER BY tb1.VAL1) as staff_ids
  FROM vw_advice tb1 
  LEFT JOIN tbl_issue t1 ON tb1.VAL1 = t1.VAL1                 
  GROUP BY  tb1.advice_id, tb1.VAL1
) zone2 ON zone1.VAL1 = zone2.VAL1
WHERE P_DATE LIKE '%-22%'   
GROUP BY  zone1.Id, zone1.VAL1, zone1.Org, zone2.VAL1, zone2.staff_ids
ORDER BY  zone1.VAL1 ASC;

This query joins vw_summary and vw_advice on the VAL1 column. The subquery uses LISTAGG to concatenate values in staff_ids, which is then joined with the outer query.

Identifying Issues

There are several issues with the initial query:

1. Incorrect join condition: The join condition between zone1 and zone2 is based on VAL1, but it should be based on both VAL1 and VAL2.
2. Missing conditions: The query doesn’t account for cases where values are missing in either table.
3. Incorrect grouping: The query groups by multiple columns, which can lead to incorrect results.

Optimization Strategies

To overcome these issues, we’ll employ the following strategies:

1. Use proper join conditions: Update the join condition to include both VAL1 and VAL2.
2. Incorporate value checking: Add checks for missing values in either table.
3. Refine grouping: Simplify the grouping process to ensure accurate results.

Updated Query

Here’s the updated query that incorporates these strategies:

SELECT zone1.Id, zone1.VAL1, zone1 VAL2, zone2.staff_ids
FROM (
  SELECT ta1.Id, ta1.VAL1, ta1.P_DATE, ta1.Org 
  FROM vw_summary ta1 
  LEFT JOIN tbl_staff t3 ON t3.staff_id IN (ta1.Org) 
) zone1 
LEFT JOIN (
  SELECT tb1.advice_id, tb1.VAL1,
         CASE WHEN tb1.VAL2 IS NULL THEN tb1.VAL1 ELSE NULL END AS VAL2
  FROM vw_advice tb1 
  LEFT JOIN tbl_issue t1 ON tb1.VAL1 = t1.VAL1                 
  GROUP BY  tb1.advice_id, tb1.VAL1
) zone2 ON zone1.VAL1 = zone2.VAL1 AND zone1.VAL2 = zone2.VAL2
WHERE P_DATE LIKE '%-22%'   
GROUP BY  zone1.Id, zone1.VAL1, zone1.VAL2, zone2.staff_ids
ORDER BY  zone1.VAL1 ASC;

Explanation

The updated query includes the following changes:

• We added a CASE statement to check for missing values in VAL2.
• We updated the join condition to include both VAL1 and VAL2.
• We simplified the grouping process by including all necessary columns.

Example Use Cases

This optimized query can be used to retrieve non-common values from two different tables, as demonstrated in the original question. The query’s flexibility allows it to handle various scenarios, such as:

• Retrieving specific records based on VAL1 and VAL2.
• Handling missing values in either table.
• Simplifying grouping processes for accurate results.

By applying these optimization strategies and understanding the intricacies of SQL queries, developers can create more efficient and effective solutions to complex problems.

Last modified on 2024-07-12