Comparing Vectorized UDFs and Built-in Functions in Databricks (pyspark)
In this tutorial, we explore the performance differences between implementing a vectorized User-Defined Function (UDF) and using a built-in function.
We will focus on calculating a moving average for a sample dataset and compare the execution times and physical execution plans.
what is the vectorized User-Defined Function ?
In Databricks, a vectorized User-Defined Function (UDF) refers to a user-defined function that operates on entire columns of data at once, rather than processing one row at a time. This is in contrast to traditional row-wise UDFs, which operate on a single row of data at a time. Vectorized UDFs are designed to take advantage of the underlying distributed computing framework in Databricks and can significantly improve the performance
of data processing tasks.
The term "vectorized" is borrowed from linear algebra,
where operations are performed on entire vectors or matrices,
rather than individual elements. Similarly, in the context of Databricks,
vectorized UDFs operate on entire columns of data as a single unit,
which can result in more efficient processing compared to row-wise UDFs.
Vectorized UDFs are typically implemented using programming languages that support vectorized operations, such as Apache Spark's built-in functions or libraries like Apache Arrow.
These UDFs can be written in languages such as Python, Scala, or SQL,
depending on the Databricks environment and the specific use case.
The benefits of using vectorized UDFs include:
Performance Improvement:
Vectorized UDFs can take advantage of optimizations in the underlying processing engine, leading to improved performance compared to row-wise UDFs.
Parallel Execution:
The vectorized approach allows for parallel execution of operations on different partitions of the data, leveraging the distributed nature of Databricks.
Code Simplicity:
Vectorized UDFs often result in simpler and more concise code,
as they abstract away the need for explicit looping over rows.
Keep in mind that the exact implementation details and syntax for vectorized UDFs may vary depending on the version of Databricks and the programming language used.
Always refer to the official documentation for the specific version you are working with for accurate and up-to-date information.