@johnb1 nystateofhealth wrote:
I would like to understand how
(1) SQL UDFs compare to Python UDFs
(2) SQL UDFs compare to Pandas UDFs
Especially in terms of performance.
I cannot find any documentation on the topics, also not in the official Databricks documentation (which unfortunately is kind of a pattern).
I do not need information regarding the comparison between Python UDFs and Pandas UDFs though.
Thanks in advance!
Letโs break down the comparisons between SQL UDFs, Python UDFs, and Pandas UDFs, especially focusing on performance.
1. SQL UDFs vs. Python UDFs
SQL UDFs (User Defined Functions):
Execution Context: They run directly in the SQL engine.
Performance: Generally faster than Python UDFs because they execute in the same engine as the SQL queries, leveraging optimizations like vectorized execution.
Use Cases: Best for simple logic and computations that can be expressed in SQL. Ideal for operations like aggregations, conditional logic, and string manipulations.
Type Safety: SQL UDFs are typically more type-safe due to their strict typing system.
Python UDFs:
Execution Context: Run in a separate Python environment, which introduces overhead.
Performance: Slower than SQL UDFs, especially for large datasets, because of serialization/deserialization costs when data is moved between the Spark engine and the Python process.
Use Cases: Useful for complex logic, machine learning models, or when leveraging Python libraries. However, they should be used sparingly for performance-sensitive tasks.
Flexibility: Greater flexibility and ease of use for complex data manipulations that can't be easily expressed in SQL.
2. SQL UDFs vs. Pandas UDFs
Pandas UDFs:
Execution Context: They run in a Python environment but are optimized for Apache Arrow, which allows for efficient data transfer between Spark and Pandas.
Performance: Faster than traditional Python UDFs due to vectorization and reduced serialization overhead, making them more suitable for processing larger datasets.
Use Cases: Ideal for applying complex operations that benefit from the flexibility of Pandas while still leveraging the distributed nature of Spark. Suitable for batch processing and transforming data.
Scalability: Can handle larger data volumes better than standard Python UDFs due to Arrow's optimized performance.
Summary of Performance Considerations
SQL UDFs are typically the fastest option for straightforward operations and should be the first choice when possible.
Pandas UDFs offer a good balance between performance and flexibility, especially for data manipulation that leverages Pandasโ capabilities.
Python UDFs should be used when necessary, but they come with performance trade-offs, especially for large datasets, due to their overhead.
