Understanding PySpark UDF Performance: Comparing Python UDF with Pandas UDF

Explore the performance differences between `Python UDF` and `Pandas UDF` in `PySpark`. Learn why results may vary and how setup overhead impacts execution time.
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Understanding PySpark UDF Performance: Comparing Python UDF with Pandas UDF

When working with large datasets in PySpark, selecting the right type of User Defined Function (UDF) can significantly impact performance. Among the popular types are Python UDF and Pandas UDF, each with its own characteristics.

Recently, a user raised an interesting question regarding the performance of these two UDF types, specifically noting a surprising anomaly in execution time. The user expected Pandas UDF to outperform Python UDF due to its use of Apache Arrow for reduced data serialization overhead and support for vectorized operations. However, the results of a code snippet indicated otherwise. Let’s explore this situation in detail.

The Issue Explained

The user executed the following snippet to compare the performance of both UDFs:

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

In this code, the user attempts to benchmark the execution time of both UDFs. One might expect Pandas UDF to be faster due to its optimization features mentioned earlier.

The Confusing Result

Despite these expectations, the performance results did not align. Following the execution of the UDFs, the user observed that the Python UDF performed better than the Pandas UDF. On analyzing the reason, it became clear that the default behavior of the show() function in PySpark was a contributing factor.

Key Insight

The show() function in PySpark only displays the first 20 rows of the DataFrame by default. Therefore, when the UDF is applied, only those 20 rows are actually processed using the UDF. This limited data sample means that the setup overhead time for the Pandas UDF, which is inherently higher than that of the Python UDF, dominates the execution time.

Addressing the Problem

To understand this behavior and achieve accurate performance comparisons, consider the following strategies:

Increase Output Rows: To test the performance more effectively, you could specify a larger number of rows to display when using the show() function, ensuring that more data is being processed.

Performance Metrics: Instead of using show(), you can measure the performance based on transformations that engage a larger number of rows, such as collect() or count(), to get a clearer picture of the UDF performance.

Example Code for Improved Benchmarking

Below is an example of how to adapt the code to measure performance more accurately:

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

Conclusion

In summary, while using UDFs in PySpark can be counterintuitive at times, understanding the underlying execution process helps in making better choices. The observed performance difference between Python UDF and Pandas UDF does not always reflect their theoretical performance potential. Always be mindful of how data is processed and displayed when benchmarking UDF performance in PySpark.

Being informed about these factors enables you to confidently select the right UDF for your applications, enhancing performance and efficiency.