Optimize the pyarrow function

Oct 13, 2025 by leolavanderia.com.br 23 views

Introduction

In this article, we will explore the issue of running a pyarrow function on a pyspark dataframe resulting in a non-vectorized execution. We will examine the code provided, understand the problem, and discuss possible solutions.

What happened?

When running a pyarrow function on a pyspark dataframe, the execution is not vectorized. This means that instead of performing the operation on the entire array at once, the function is called for each individual element.

Sample Code

The sample code provided is as follows:

from pyspark.sql import SparkSession
import ibis
import pyarrow.compute as pc

def main():
    session = SparkSession.builder.getOrCreate()
    con = ibis.pyspark.connect(session=session)
    p = con.read_csv("./penguins.csv.gz")
    con.create_view("penguins",
                    p,
                    # database=db,
                    overwrite=True
                    )
    udf = ibis.udf.scalar.pyarrow(somma)
    t = con.table("penguins")
    print((t.filter(t.species == "Adelie")
     .mutate(weird_col=udf(t.year, t.body_mass_g))
     .select("year", "body_mass_g", "weird_col")).execute())


def somma(x: int, y: int) -> int:
    # I can't add correct type hints (pyarrow.lib.Int64Array) to this function otherwise
    # I get `dtype`: <class 'pyarrow.lib.Int64Array'> is not either None or coercible to a DataType` error
    # the types are those though and work across spark and polars
    return pc.add(x, y)


if __name__ == "__main__":
    main()

Understanding the Issue

The issue arises when the somma function is called for each individual element in the dataframe. This is because the pyarrow function is not vectorized, meaning it cannot operate on the entire array at once.

Debugging the Issue

To debug the issue, we can patch the compute.py file to log the arguments of the call. This will help us understand what is happening under the hood.

        def wrapper(*args, memory_pool=None):
            print("****")
            print(type(args[1]))
            print(args[1])
            print("****")

Output

The output of the debugged code is as follows:

****
<class 'float'>
3700.0
****
****
<class 'float'>
4000.0
****

Comparison with Polars

When running the same code with Polars, we get a different output:

****
<class 'pyarrow.lib.Int64Array'>
[
  3750,
  3800,
  3250,
...

Expected Behavior

The expected behavior is that the pyarrow function should be vectorized, meaning it should operate on the entire array at once.

Version of Ibis

The version of Ibis being used is 10.2.0.

Backend(s)

The backend being used is PySpark.

Relevant Log Output

There is no relevant log output provided.

Conclusion

In conclusion, running a pyarrow function on a pyspark dataframe results in a non-vectorized execution. This is because the pyarrow function is not vectorized, meaning it cannot operate on the entire array at once. To debug the issue, we can patch the compute.py file to log the arguments of the call. The expected behavior is that the pyarrow function should be vectorized, meaning it should operate on the entire array at once.

Possible Solutions

One possible solution is to use a different backend, such as Polars, which is known to be vectorized.

Code of Conduct

I agree to follow this project's Code of Conduct.

What can be done to improve the situation?

Use a different backend: Consider using a different backend, such as Polars, which is known to be vectorized.
Optimize the pyarrow function: Optimize the pyarrow function to make it more efficient and vectorized.
Use a different library: Consider using a different library, such as Dask, which is designed for parallel computing and may be more suitable for this use case.

What are the implications of this issue?

Performance: The issue may result in poor performance, as the function is called for each individual element in the dataframe.
Scalability: The issue may make it difficult to scale the code, as the function is not vectorized and may not be able to operate on large datasets.
Maintainability: The issue may make the code more difficult to maintain, as the function is not vectorized and may require additional optimization.

How can this issue be avoided in the future?

Choose a different backend: Consider using a different backend, such as Polars, which is known to be vectorized.
Optimize the code: Optimize the code to make it more efficient and vectorized.
Use a different library: Consider using a different library, such as Dask, which is designed for parallel computing and may be more suitable for this use case.
Q&A: Running a pyarrow function on pyspark dataframe results in a non-vectorized execution =====================================================================================

Q: What is the issue with running a pyarrow function on a pyspark dataframe?

A: The issue is that the pyarrow function is not vectorized, meaning it cannot operate on the entire array at once. This results in a non-vectorized execution, which can lead to poor performance and scalability issues.

Q: What is the expected behavior of a pyarrow function on a pyspark dataframe?

A: The expected behavior is that the pyarrow function should be vectorized, meaning it should operate on the entire array at once.

Q: Why is the pyarrow function not vectorized?

A: The pyarrow function is not vectorized because it is not designed to operate on the entire array at once. It is a scalar function, meaning it operates on individual elements, rather than the entire array.

Q: How can I debug the issue?

A: You can debug the issue by patching the compute.py file to log the arguments of the call. This will help you understand what is happening under the hood.

Q: What are the implications of this issue?

A: The implications of this issue are:

Performance: The issue may result in poor performance, as the function is called for each individual element in the dataframe.
Scalability: The issue may make it difficult to scale the code, as the function is not vectorized and may not be able to operate on large datasets.
Maintainability: The issue may make the code more difficult to maintain, as the function is not vectorized and may require additional optimization.

Q: How can I avoid this issue in the future?

A: You can avoid this issue in the future by:

Choosing a different backend: Consider using a different backend, such as Polars, which is known to be vectorized.
Optimizing the code: Optimize the code to make it more efficient and vectorized.
Using a different library: Consider using a different library, such as Dask, which is designed for parallel computing and may be more suitable for this use case.

Q: What are some possible solutions to this issue?

A: Some possible solutions to this issue are:

Using a different backend: Consider using a different backend, such as Polars, which is known to be vectorized.
Optimizing the pyarrow function: Optimize the pyarrow function to make it more efficient and vectorized.
Using a different library: Consider using a different library, such as Dask, which is designed for parallel computing and may be more suitable for this use case.

Q: What are the benefits of using a vectorized function?

A: The benefits of using a vectorized function are:

Improved performance: Vectorized functions can operate on the entire array at once, resulting in improved performance.
Increased scalability: Vectorized functions can handle large datasets, making them more suitable for big data applications.
Simplified code: Vectorized functions can simplify the code, making it easier to maintain and debug.

Q: How can I determine if a function is vectorized?

A: You can determine if a function is vectorized by checking its documentation or by using a tool such as pyarrow.compute to inspect the function's behavior.