Why are Python UDFs slower due to serialization overhead and lack of JVM optimization?

Python UDFs run in a separate Python process while Spark itself runs on the JVM. Every row that a UDF touches must be serialized from the JVM into a byte stream, sent over the network to the Python worker, deserialized back into Python objects, processed, then serialized again and sent back. This round‑trip adds significant latency, especially for large datasets. Additionally, Python code is interpreted and does not benefit from the JVM’s Just‑In‑Time (JIT) compilation, which optimizes bytecode at runtime. Built‑in Spark functions are compiled in Java/Scala and can be executed directly on the JVM, avoiding the serialization overhead and leveraging JIT optimizations. To mitigate the performance hit, one can use Pandas UDFs (vectorized) or rewrite the logic using Spark SQL or native functions.