Скачать или смотреть How to Filter Elements in Each Row of a List[StringType] Column in a Spark DataFrame

Learn how to effectively filter elements within rows of an ArrayType column in a Spark DataFrame using UDFs.
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Filtering Elements in Each Row of a List[StringType] Column in a Spark DataFrame

When working with Spark DataFrames, you may find yourself in a situation where you need to filter elements within each row of a column that consists of an array of strings. This challenge can arise when you want to retain only specific elements based on a given dictionary.

Let’s dive into the problem and examine how to approach it step-by-step.

The Problem

You have a Spark DataFrame that contains an ArrayType column, and you'd like to filter the elements of each array based on a list of allowed values (a dictionary). For instance, given the following DataFrame:

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This DataFrame looks like:

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And you want to filter this based on the following dictionary_list:

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The desired output should be:

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The Solution

To achieve this filtering, we'll utilize a User Defined Function (UDF) in Spark. Here’s how to implement the solution:

Step 1: Define the UDF

The first step is to create a UDF that takes an input sequence of strings and returns a filtered sequence containing only the elements present in your dictionary list. Here's how you can define it:

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Step 2: Use the UDF in the DataFrame Operation

Now that we have our UDF defined and registered, we can apply it to our DataFrame using the select operation. Remember that we need to use select instead of where, as we want to modify the contents of each row rather than filter out entire rows:

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Expected Output

When you run the code above, you should get the filtered DataFrame as follows:

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Conclusion

By utilizing a UDF, you've successfully filtered elements within each row of the ArrayType column in your Spark DataFrame. This approach provides a flexible and efficient way to handle complex array manipulations in Apache Spark.

With this technique, you can apply similar logic to various data processing tasks, making your data analysis more powerful and tailored to your needs.

Now you can easily filter elements in your DataFrame's rows based on your specified criteria!