How to Delete Duplicates Using Spark SQL

Описание: Learn how to efficiently delete duplicate records in Spark SQL with an alternative method when using Databricks.
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This video is based on the question https://stackoverflow.com/q/66673230/ asked by the user 'Nat' ( https://stackoverflow.com/u/15256165/ ) and on the answer https://stackoverflow.com/a/66673261/ provided by the user 'Gordon Linoff' ( https://stackoverflow.com/u/1144035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Deleting Duplicate Records in Spark SQL

When working with large datasets, it's common to encounter duplicate records. Handling these duplicates efficiently is crucial for maintaining data integrity and accuracy. If you're using Spark SQL, particularly in Databricks, you may run into challenges when trying to delete duplicates using traditional SQL syntax. This post will guide you through an effective alternative to delete duplicates using Spark SQL.

The Challenge

You might have tried using a common table expression (CTE) as follows:

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However, running this code could lead to errors like:

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The problem arises because Spark SQL does not support deleting from a CTE directly, unlike some SQL Server implementations.

The Solution

To effectively delete duplicates in Spark SQL within Databricks, you need to use an alternative approach. If the Name field is unique and not NULL, you can employ the following SQL workflow.

Step 1: Identify Duplicates

Instead of deleting directly from the CTE, first, identify the duplicates. To do this, you can execute a SELECT query with a subquery:

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How It Works

Subquery Selection: The inner query fetches the minimum name of the duplicates for each id.

Deletion Logic: The outer DELETE statement compares each employee's name with the minimum name for their respective IDs.

Eliminating Duplicates: This logic effectively ensures that only the duplicate records, and not the original, are deleted from the Emp table.

Step 2: Consider Primary Keys

If your dataset is structured differently, you may also consider using the table's primary key for comparisons. This method ensures that you're accurately identifying duplicates based on unique identifiers.

Example with Primary Key

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This query keeps the record with the highest id (which we assume is unique) and removes the rest.

Conclusion

Deleting duplicates in Spark SQL can be tricky, especially when coming from a SQL Server background. However, by utilizing subqueries and focusing on unique constraints within your data, you can efficiently manage duplicates. Always remember to back up your data before performing delete operations to prevent unintended data loss.

By following these guidelines, you will keep your datasets clean and your queries efficient.