Скачать или смотреть How to Remove Duplicate Results in SQL Based on an Aggregate Field

Learn how to efficiently remove duplicate results in SQL using CTE and Row_number() based on an aggregate field.
---
This video is based on the question https://stackoverflow.com/q/74049467/ asked by the user 'Armine' ( https://stackoverflow.com/u/12983438/ ) and on the answer https://stackoverflow.com/a/74051857/ provided by the user 'Data_Enginner_Suraj' ( https://stackoverflow.com/u/13299323/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to remove duplicate results based on an aggregate field?

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Remove Duplicate Results in SQL Based on an Aggregate Field

In the world of data management, encountering duplicate records can be a frequent headache, especially when you're working with aggregate fields. Whether you’re trying to analyze sales data or survey responses, duplicates can skew your results and lead to misleading insights. In today's guide, we will address a common SQL problem: how to remove duplicate results based on an aggregate field.

The Problem

Imagine you have created an aggregate field by combining two existing fields, fieldA and fieldB, into a new field called new_field. However, after running your query, you realize you are still encountering duplicate rows even when applying the min() function. This situation can be quite frustrating, as duplicates can make it difficult to analyze your data accurately.

For example, starting with the following SQL query:

[[See Video to Reveal this Text or Code Snippet]]

This query has not effectively filtered out duplicate entries based on the new field new_field.

The Solution

Using Common Table Expressions (CTE) and Row_Number()

To effectively remove the duplicate records based on fieldA and fieldB, we can employ a combination of Common Table Expressions (CTE) and the ROW_NUMBER() window function. This method allows us to assign a unique sequential integer to rows within a partition of a result set. Here’s how it can be accomplished:

Step-by-Step Breakdown

Create a CTE: We will first create a CTE that selects records from your table and calculates the row number for each partition based on the combination of fieldA and fieldB.

Use the ROW_NUMBER() Window Function: This function will assign a unique number to each row based on the ordering defined (in our case, ordering by fieldA and fieldB).

Delete Duplicates: Finally, we will delete the records that have a row number greater than 1, which indicates that they are duplicates.

Here’s the SQL implementation:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

CTE: The Common Table Expression identifies the records to process. It includes all fields from tableA and computes a new temporary column called RN which will contain the row number for each group based on the aggregated values.

PARTITION BY Clause: This defines the groups of duplicates by determining how the data is grouped for the row numbering. In our example, it is based on new_field.

Deleting Duplicates: The WHERE RN > 1 clause identifies rows beyond the first of their respective groups, effectively selecting duplicates for deletion.

Conclusion

Handling duplicates in SQL can sometimes seem daunting, but with the right approach, it can be done efficiently. By utilizing CTEs and the ROW_NUMBER() function, you can easily identify and remove duplicate records based on your aggregate field. This not only cleans up your dataset but also ensures your analyses are based on accurate, unique entries.

Do you have further questions on SQL or need more clarification on this process? Feel free to reach out in the comments below!