How to Discard Older Rows in Amazon Redshift Based on update_time

Описание: Learn how to efficiently remove duplicate rows in Amazon Redshift while retaining only the latest records based on `update_time`.
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This video is based on the question https://stackoverflow.com/q/64698526/ asked by the user 'Craig' ( https://stackoverflow.com/u/722950/ ) and on the answer https://stackoverflow.com/a/64698564/ 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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Discarding Older Rows in Amazon Redshift Based on update_time

Amazon Redshift is an incredibly powerful data warehousing solution that allows users to handle vast amounts of data efficiently. However, as your data grows, you may encounter certain common challenges, such as dealing with duplicate records. This post addresses a specific case where you have multiple rows with the same ID in a table and want to keep only the latest row based on the update_time value while discarding the older entries. Let's dig in!

Understanding the Problem

Imagine you have a table named my_table that contains the following data:

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In this table, the ID abc appears twice. Your goal is to retain only the latest entry, which in this case is associated with an update_time of 2019-11-11 15:15:15 and discard the older row.

The Solution

To effectively remove older rows while retaining only the latest records, you can employ a combination of SQL window functions and subqueries. Below is a step-by-step breakdown of how to achieve this.

Step 1: Identify the Latest Rows

First, you need to identify which rows are the most recent for each ID using the ROW_NUMBER() window function. Here's how you can do that:

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Explanation:

This SQL command creates a temporary table called foo.

Inside the subquery, it selects data from my_table and assigns a row number to each row based on the update_time sorted in descending order for each id.

Finally, it filters to retain only those rows where row_number equals 1, meaning only the latest rows for each ID are kept in foo.

Step 2: Deleting Older Rows

Once you have identified the latest entries, the next step is to delete the older records from the original my_table. You can use the following SQL DELETE statement to accomplish this:

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Explanation:

The USING clause defines a subquery that groups records in my_table by id and finds the maximum update_time for each ID.

The main DELETE statement removes rows from my_table where the update_time is less than the maximum update time found in the subquery, effectively keeping only the latest entries.

Conclusion

By using the combination of ROW_NUMBER(), subqueries, and a well-structured DELETE statement, you can effectively manage and clean your data in Amazon Redshift. This method not only simplifies your data management tasks but also helps maintain the integrity and relevance of your dataset.

Remember, keeping your tables organized is crucial as it directly impacts the performance and usability of your data warehouse. By applying these techniques, you can ensure that your data remains concise and actionable.

Now You Try It!

Are you facing similar issues with duplicate data in your database? Give this method a try in your Amazon Redshift environment, and see how it enhances your data handling capabilities!