Скачать или смотреть How to Use a Pyspark UDF to Analyze DataFrames Efficiently

Learn how to use a Pyspark UDF to extract part numbers from a DataFrame by using broadcast variables, ensuring optimal performance and streamlined code.
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This video is based on the question https://stackoverflow.com/q/76954432/ asked by the user 'Alberto Tienda' ( https://stackoverflow.com/u/22103432/ ) and on the answer https://stackoverflow.com/a/76954470/ provided by the user 'Aymen Azoui' ( https://stackoverflow.com/u/10514682/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Efficiently Analyzing Pyspark DataFrames with UDFs

In the world of data analysis, especially when working with large datasets in Pyspark, we often face challenges in manipulating data efficiently. One common scenario arises when you need to access data from one DataFrame while operating on another using User Defined Functions (UDFs). This can be tricky, as UDFs cannot directly access Pyspark DataFrames.

The Problem

Imagine you have two Pyspark DataFrames: qnotes_df with a LONG_TEXT column and part_numbers_df with a list of part numbers. You want to analyze the LONG_TEXT to extract any part numbers mentioned and return them in a new column called REPLACEMENTS.

Here’s a snapshot of the initial attempt:

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However, trying to access part_numbers_df within the UDF will not work due to the restrictions of UDFs in Pyspark.

The Solution: Broadcasting Variables

The best way to tackle this issue is to use broadcast variables. Broadcasting allows you to efficiently share data across all nodes in the cluster, and it ensures that the data can be accessed within the UDF.

Step-by-step Implementation

Convert part_numbers_df to a Set:
First, we convert the part_numbers_df to a set to improve lookup times.

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Broadcast the Set:
Next, we broadcast this set to make it available to all executors.

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Tokenization and Filtering:
Define the UDF to tokenize the text and filter out the part numbers by checking against the broadcast variable.

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Register and Apply the UDF:
Finally, register the UDF with the return type as an array of strings and apply it to the LONG_TEXT column.

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Display Results:
Show the resulting DataFrame that now includes the REPLACEMENTS column.

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Final Code

Here’s the complete implementation for clarity:

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Conclusion

By using broadcast variables, we can efficiently access needed data from other DataFrames inside UDFs. This not only enhances performance but also allows you to maintain clean and organized code while working with Pyspark. Now, you can confidently analyze your DataFrames without running into the limitations of accessing multiple DataFrames within UDFs.