Showing Duplicates in Multiple Columns in UNION Query

When working with data from multiple tables in a UNION query, it’s often necessary to identify duplicates based on specific columns. In this article, we’ll explore how to show duplicates in multiple columns using the UNION operator and window functions.

Understanding the Problem

The problem at hand is to take two tables, ORIN and OINV, both with an open status ('O'), and use a UNION query to combine their data. We want to identify any duplicate rows based on specific columns: CardCode and DocTotal. The desired output should include only the rows where these values appear more than once.

Initial Query

To start, let’s examine the initial query that combines the data from both tables:

SELECT 
    T0.[CardCode], 
    T0.[CardName],
    T0.[DocNum],  
T0.[DocTotal] 

FROM 
ORIN T0 

WHERE 
T0.[DocStatus] = 'O'

UNION ALL

SELECT 
T0.[CardCode], 
T0.[CardName],
T0.[DocNum],  
T0.[DocTotal] 

FROM 
OINV T0 

WHERE 
T0.[DocStatus] = 'O'

This query uses the UNION operator to combine the results from both tables. However, it doesn’t provide any way to identify duplicate rows based on CardCode and DocTotal.

Solution for RDBMS Platforms Supporting QUALIFY Clause

For RDBMS platforms that support the QUALIFY clause (Teradata, Snowflake, Databricks), we can add a simple condition to filter out duplicate rows:

SELECT 
    T0.[CardCode], 
    T0.[CardName],
    T0.[DocNum],  
T0.[DocTotal] 

FROM 
ORIN T0 

WHERE 
T0.[DocStatus] = 'O'

UNION ALL

SELECT 
T0.[CardCode], 
T0.[CardName],
T0.[DocNum],  
T0.[DocTotal] 

FROM 
OINV T0 

WHERE 
T0.[DocStatus] = 'O'

QUALIFY COUNT(*) OVER (PARTITION BY CardCode, DocTotal) > 1

In this example, the QUALIFY clause is used to filter out rows where the count of duplicates for CardCode and DocTotal is greater than 1.

Solution for Other RDBMS Platforms

For other RDBMS platforms like sql-server, mysql, and postgres, we need to use a different approach. We’ll wrap the initial query in a subquery and add a window function to count duplicates:

SELECT CardCode,
    CardName,
    DocTotal,
    COUNT(*) OVER (PARTITION BY CardCode, DocTotal) as duplicates

FROM 
    (
        SELECT CardCode,
            CardName,
            DocTotal,
            T0.[DocNum]
        FROM ORIN T0
        WHERE T0.[DocStatus] = 'O'
        UNION ALL
        SELECT T0.[CardCode],
            T0.[CardName],
            T0.[DocNum],
            T0.[DocTotal]
        FROM OINV T0
        WHERE T0.[DocStatus] = 'O'
    ) as T1

WHERE duplicates > 1

In this example, the subquery first combines the data from both tables using a UNION query. The outer query then uses a window function to count the number of duplicates for each row based on CardCode and DocTotal. Finally, we filter out rows where the duplicate count is greater than 1.

Understanding Window Functions

Window functions are used in SQL to perform calculations across a set of rows that are related to the current row. In this case, the window function COUNT(*) OVER (PARTITION BY CardCode, DocTotal) counts the number of rows with the same values for CardCode and DocTotal.

The PARTITION BY clause divides the result set into partitions based on the specified columns (CardCode and DocTotal). The COUNT(*) aggregation function then counts the number of rows within each partition.

Additional Considerations

When working with window functions, it’s essential to consider how they affect query performance. In general, using window functions can lead to increased CPU usage and slower query execution times.

To mitigate this impact, you can use various optimization techniques, such as:

• Indexing columns used in the PARTITION BY clause
• Limiting the number of partitions created by the window function
• Using partition elimination or range aggregation instead of window functions

Conclusion

In conclusion, showing duplicates in multiple columns in a UNION query can be achieved using window functions. By understanding how to use the QUALIFY clause and window functions, you can effectively identify duplicate rows and improve data analysis and reporting.

When working with complex queries, consider optimizing your approach by indexing columns used in the PARTITION BY clause or exploring alternative methods like partition elimination or range aggregation.

Last modified on 2023-09-15