5 Top Reasons Why Python UDFs Slow in PySpark

PySpark UDFs (User Defined Functions) can be slow for several reasons. These include the overhead of serialization, Lack of optimization, style of processing, and less effectiveness in resource use, etc.

Serialization Overhead

When you use UDFs, PySpark needs to serialize the data to pass it between the Python and JVM (Java Virtual Machine) processes. This serialization overhead can be significant, especially when dealing with large volumes of data.

Python Process Overhead

PySpark runs on the JVM, while UDFs execute in the Python interpreter. It requires data to be serialized and transferred between the JVM and Python processes, which can introduce additional overhead.

Lack of Optimization

Python UDFs may not benefit from the same optimizations as built-in PySpark functions (written in Scala or Java). PySpark’s built-in functions are often highly optimized and leverage Spark’s internal optimizations, while UDFs rely on Python’s interpreter.

Spark’s Catalyst optimizer cannot optimize UDFs written in Python. Catalyst can only optimize operations written in DataFrame or SQL API. When you use UDF, Catalyst treats it as a black box. So, it cannot apply the same optimization, leading to less efficient execution plans.

Row-at-a-time Processing

PySpark UDFs operate on rows of data, which can lead to inefficient row-at-a-time processing. In contrast, built-in PySpark functions often operate on entire data in the partition at once, which can be more efficient.

Resource Utilization

UDFs may not fully utilize the distributed computing capabilities of Spark. They may perform computation locally on the Executor rather than leveraging Spark’s distributed processing capabilities beneficially.

Tips to mitigate the Udf’s slow performance

• Use Spark SQL Functions: Whenever possible, use built-in Spark SQL functions which are highly optimized and avoid the overhead of Python UDFs.
• Pandas UDFs (Vectorized UDFs): Use Pandas UDFs, which are optimized and can operate on a batch of rows, reducing the serialization and deserialization overhead.
• Broadcast Variables: Use broadcast variables for read-only data to minimize the data transfer between the Python and JVM environments.
• Optimize UDF Logic: Ensure that the logic within the UDF is optimized and minimizes expensive operations.