Simplifying T-SQL Queries with Conditional Aggregation

Introduction to Conditional Aggregation

Conditional aggregation is a powerful technique in SQL that allows us to simplify complex queries by grouping data based on conditions. In this article, we’ll explore how to use conditional aggregation to simplify the given T-SQL query.

Understanding the Original Query

The original query consists of multiple SELECT COUNT(*) statements with varying conditions. These conditions are used to filter rows from a table named Table. The main issue with this query is that it’s repetitive and difficult to maintain, especially when adding new conditions or modifying existing ones.

Using Conditional Aggregation

To simplify the query, we can use conditional aggregation by grouping data based on conditions. This approach allows us to avoid repeating the same column selection for each condition.

One of the key benefits of conditional aggregation is that it enables us to group data based on multiple conditions using a single CASE statement. In the original query, the same column selection (columnA, columnB) was repeated multiple times, which made the query cumbersome to maintain.

Breaking Down the Solution

To simplify the query, we’ll follow these steps:

1. Identify Conditions: Identify the unique conditions for each group of rows.
2. Grouping Data: Group data based on these conditions using conditional aggregation.
3. Joining Tables: Join tables to filter rows based on conditions.

Step 1: Identifying Conditions

The original query has multiple conditions, some of which are used in conjunction with others. To simplify the query, we’ll identify unique conditions for each group of rows:

• Condition 1: `columnA = ‘I’ AND columnB = ‘I’ AND columnC =’’
• Condition 2: `columnA = ‘I’ AND columnB = ‘I’ AND columnC <> '’
• Condition 3: columnA IN ('A','R') AND columnB = 'I' AND columnD IN (SELECT columnE FROM Table2)
• Condition 4: columnA IN ('B') AND columnB = 'I'
• Condition 5: columnA IN ('R') AND columnB = 'S'
• Condition 6: columnA IN ('A') AND columnB = 'S'

Step 2: Grouping Data Using Conditional Aggregation

We’ll use a single CASE statement to group data based on the identified conditions:

{< highlight sql >}
SELECT 
    SUM(CASE WHEN STATUS = 'I' AND TRX_STATE = 'I' AND ASSIGN_TO = '' THEN 1 ELSE 0 END) AS COUNT_1,
    SUM(CASE WHEN STATUS = 'I' AND TRX_STATE = 'I' AND ASSIGN_TO <>'' THEN 1 ELSE 0 END) AS COUNT_2,
    SUM(CASE WHEN STATUS IN ('A','R') AND TRX_STATE = 'I' AND s.BI_BRH IS NOT NULL THEN 1 ELSE 0 END) AS COUNT_3,
    SUM(CASE WHEN STATUS IN ('B') AND TRX_STATE = 'I' THEN 1 ELSE 0 END) AS COUNT_4,
    SUM(CASE WHEN STATUS IN ('R') AND TRX_STATE = 'S' THEN 1 ELSE 0 END) AS COUNT_5,
    SUM(CASE WHEN STATUS IN ('A') AND TRX_STATE = 'S' THEN 1 ELSE 0 END) AS COUNT_6
FROM 
    ICSCHQINFO i
LEFT JOIN 
    STDHOST s
ON 
    s.BI_BRH = i.BRH_ASSIGN_TO;
{< /highlight >}

Step 3: Joining Tables

We’ll join tables to filter rows based on conditions. In this case, we’re joining ICSCHQINFO with STDHOST.

{< highlight sql >}
SELECT 
    SUM(CASE WHEN STATUS = 'I' AND TRX_STATE = 'I' AND ASSIGN_TO = '' THEN 1 ELSE 0 END) AS COUNT_1,
    SUM(CASE WHEN STATUS = 'I' AND TRX_STATE = 'I' AND ASSIGN_TO <>'' THEN 1 ELSE 0 END) AS COUNT_2,
    SUM(CASE WHEN STATUS IN ('A','R') AND TRX_STATE = 'I' AND s.BI_BRH IS NOT NULL THEN 1 ELSE 0 END) AS COUNT_3,
    SUM(CASE WHEN STATUS IN ('B') AND TRX_STATE = 'I' THEN 1 ELSE 0 END) AS COUNT_4,
    SUM(CASE WHEN STATUS IN ('R') AND TRX_STATE = 'S' THEN 1 ELSE 0 END) AS COUNT_5,
    SUM(CASE WHEN STATUS IN ('A') AND TRX_STATE = 'S' THEN 1 ELSE 0 END) AS COUNT_6
FROM 
    ICSCHQINFO i
LEFT JOIN 
    STDHOST s
ON 
    s.BI_BRH = i.BRH_ASSIGN_TO;
{< /highlight >}

Benefits of Conditional Aggregation

Conditional aggregation offers several benefits over traditional approaches to querying data:

• Reduced Code Duplication: By using conditional aggregation, you can avoid duplicating code for repetitive conditions.
• Improved Maintainability: The query is easier to maintain and update when using conditional aggregation.
• Better Data Filtering: Conditional aggregation allows for more precise filtering of data based on complex conditions.

Conclusion

In this article, we explored how to simplify T-SQL queries using conditional aggregation. By identifying unique conditions for each group of rows and grouping data accordingly, you can create more efficient and maintainable queries.

Last modified on 2024-12-12