Скачать или смотреть How to Implement Pyspark Window Function for Last Days' History in Rows

Discover how to collect historical rows in PySpark based on specific conditions using window functions. Learn the details and see code examples!
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Unlocking the Power of Pyspark: Collecting Historical Rows Based on Conditions

When working with data in PySpark, sometimes you need to reference historical data based on specific conditions. For example, imagine you have a dataset of daily records and you want to create a new column that summarizes the values of previous rows under the following conditions:

Only include rows that are not older than 10 days from the current row.

Limit the summary to the last 2 rows.

In this post, we'll walk through how to implement this using PySpark's powerful window functions.

Understanding the Problem

Let's visualize the data you have, which includes the following fields:

id: Identifier for the record

date: Date associated with the record

value: A numerical value related to the record

For instance, consider the following example data:

id
date
value
1
2023-01-01
100
1
2023-05-01
200
1
2023-05-02
300
1
2023-05-03
400
1
2023-05-04
500

Your goal is to create a new column called history that summarizes historical rows based on the aforementioned criteria.

The Solution

To achieve this, we can use PySpark's window function combined with filtering techniques. Here’s how:

Step 1: Set Up Your Spark Session

First, ensure you have a Spark session running:

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

Step 2: Create the Initial DataFrame

Next, define your data and create a DataFrame:

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

Step 3: Implement the Window Function

Now, let's implement the logic that uses a window function to collect preceding rows that meet the criteria:

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

Step 4: Show the Results

Finally, display your DataFrame to see how the history column is populated:

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

Output Explanation

Your output should look like this:

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

Conclusion

By following this method, you can dynamically summarize previous records while adhering to your defined conditions. This approach is particularly useful in data analysis scenarios where historical context is key for decision-making.

Do you have any more questions or need further assistance with PySpark? Feel free to reach out!