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Скачать или смотреть How to Zip Rows into One in Apache Spark with PySpark

  • vlogize
  • 2025-09-15
  • 0
How to Zip Rows into One in Apache Spark with PySpark
How can I zip rows into one?apache sparkpysparkapache spark sql
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Описание к видео How to Zip Rows into One in Apache Spark with PySpark

Learn how to efficiently merge rows with the same ID and date in Apache Spark using PySpark. Get practical solutions and code examples.
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This video is based on the question https://stackoverflow.com/q/62526057/ asked by the user 'David' ( https://stackoverflow.com/u/11287858/ ) and on the answer https://stackoverflow.com/a/62526217/ provided by the user 'notNull' ( https://stackoverflow.com/u/7632695/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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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.

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How to Zip Rows into One in Apache Spark with PySpark

Merging rows in a dataset where multiple entries share the same identifier can be a common requirement, especially in data processing tasks. In this article, we will dive into how to effectively zip rows into one in Apache Spark using PySpark. We will use a practical example, including a sample CSV file and detailed code snippets for better understanding.

The Problem

Suppose we have a CSV file named test.csv that contains the following data:

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

From this dataset, the goal is to consolidate the rows with the same id and date into a single row as shown below:

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

As you can see, we need to merge the values of item1, item2, and item3 into one structured row per unique combination of id and date.

The Solution

Here's how you can achieve this in PySpark. We will explore three effective methods for merging rows.

Method 1: Using flatten and array

Read the CSV file:

We start by reading the CSV file into a DataFrame.

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

Group and Aggregate:

We group the DataFrame by id and date while collecting the items into an array and flattening them into a single array.

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

Output:

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

Method 2: Using groupBy with first

Another approach is to leverage the first function to get the first non-null value for each item.

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

This will give us the same output as before, merging the rows effectively.

Method 3: Using SQL Queries

If you prefer using SQL syntax, you can register the DataFrame as a temporary view and run SQL queries to get the desired results.

Create a Temporary View:

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

Run SQL Queries:

You can use either first or max to merge the rows.

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

Dynamic Solutions

If your CSV file has an arbitrary number of item columns, consider using a dynamic approach to handle different numbers of items.

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

Conclusion

Merging rows with the same identifier in PySpark is straightforward once you know the right methods. Using flatten, first, or SQL queries can help you efficiently consolidate data in your DataFrame.

Experiment with these methods in your own environment, and tailor them to fit your specific datasets and requirements.

Happy coding with Apache Spark!

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