UDF
UDF stands for user-defined function, registered in Spark SQL or DataFrame to extend built-in function capabilities. It is typically written in Python, Scala, or Java, returns an output value for each row of input, but cannot be deeply optimized by the Catalyst optimizer.
Timeline (last 90 days)
Overhead outside the UDF can eat up the GPU speedup gains: transferring data back and forth between CPU and GPU is expensive, and if a GPU UDF is sandwiched between operators that frequently fall back to the CPU, the whole thing may actually become slower, so migration should be evaluated in units of an entire operator chain rather than as single-point replacements.
UDFs not suitable for GPU migration include: those with lots of branch jumps, those relying on row-by-row operations on Python objects, or those requiring external network calls; moving such logic to the GPU often yields limited benefit or even introduces extra copy overhead.
Even if the CPU and GPU implementations are logically consistent, they may differ in floating-point accumulation order, null value (NaN/Null) propagation, integer overflow, and time zone conversion, so a candidate implementation generated by an Agent must be verified by comparing it with the original UDF on samples, and acceptance should use relative or absolute error thresholds rather than strict equality.
A Spark UDF (User Defined Function) is a custom function registered by a user in Spark SQL or the DataFrame API, usually written in Python (PySpark) or Scala/Java, that returns one output value for each row of input.
Spark's Catalyst optimizer cannot see inside UDFs to do operator pushdown or vectorization, and Python UDFs require serialized parameter passing between the JVM and the Python process, making per-row invocation overhead significant.
When the RAPIDS Accelerator for Apache Spark encounters a custom UDF, it cannot translate it automatically, so a developer or Agent must provide an equivalent implementation executable on the GPU.
Typical forms of UDFs suitable for GPU migration include: vectorized numerical computation, string matching and regular expressions, timestamp operations, and scalar functions that can be executed in batches over columns.
The UDF approach exhibits significant performance bottlenecks when joining millions of log records against thousands of CIDR rules, because every row incurs the invocation overhead of the JVM or Python interpreter.
In the past, common approaches to analyzing IP data in SQL included using regular expressions, writing custom UDFs, or relying on bitwise operations—solutions that are difficult to maintain and error-prone.
All Facts (9)
Overhead outside the UDF can eat up the GPU speedup gains: transferring data back and forth between CPU and GPU is expensive, and if a GPU UDF is sandwiched between operators that frequently fall back to the CPU, the whole thing may actually become slower, so migration should be evaluated in units of an entire operator chain rather than as single-point replacements.
50%UnverifiedUDFs not suitable for GPU migration include: those with lots of branch jumps, those relying on row-by-row operations on Python objects, or those requiring external network calls; moving such logic to the GPU often yields limited benefit or even introduces extra copy overhead.
50%UnverifiedEven if the CPU and GPU implementations are logically consistent, they may differ in floating-point accumulation order, null value (NaN/Null) propagation, integer overflow, and time zone conversion, so a candidate implementation generated by an Agent must be verified by comparing it with the original UDF on samples, and acceptance should use relative or absolute error thresholds rather than strict equality.
50%UnverifiedA Spark UDF (User Defined Function) is a custom function registered by a user in Spark SQL or the DataFrame API, usually written in Python (PySpark) or Scala/Java, that returns one output value for each row of input.
50%UnverifiedSpark's Catalyst optimizer cannot see inside UDFs to do operator pushdown or vectorization, and Python UDFs require serialized parameter passing between the JVM and the Python process, making per-row invocation overhead significant.
50%UnverifiedWhen the RAPIDS Accelerator for Apache Spark encounters a custom UDF, it cannot translate it automatically, so a developer or Agent must provide an equivalent implementation executable on the GPU.
50%UnverifiedTypical forms of UDFs suitable for GPU migration include: vectorized numerical computation, string matching and regular expressions, timestamp operations, and scalar functions that can be executed in batches over columns.
50%UnverifiedThe UDF approach exhibits significant performance bottlenecks when joining millions of log records against thousands of CIDR rules, because every row incurs the invocation overhead of the JVM or Python interpreter.
50%UnverifiedIn the past, common approaches to analyzing IP data in SQL included using regular expressions, writing custom UDFs, or relying on bitwise operations—solutions that are difficult to maintain and error-prone.
50%