pyspark.sql.functions.try_reflect#

pyspark.sql.functions.try_reflect(*cols)[source]#

This is a special version of reflect that performs the same operation, but returns a NULL value instead of raising an error if the invoke method thrown exception.

New in version 4.0.0.

Parameters

colsColumn or column name: the first element should be a Column representing literal string for the class name, and the second element should be a Column representing literal string for the method name, and the remaining are input arguments (Columns or column names) to the Java method.

See also

pyspark.sql.functions.reflect()
pyspark.sql.functions.java_method()

Examples

Example 1: Reflecting a method call with arguments

>>> from pyspark.sql import functions as sf
>>> df = spark.createDataFrame([("a5cf6c42-0c85-418f-af6c-3e4e5b1328f2",)], ["a"])
>>> df.select(
...     sf.try_reflect(sf.lit("java.util.UUID"), sf.lit("fromString"), "a")
... ).show(truncate=False)
+------------------------------------------+
|try_reflect(java.util.UUID, fromString, a)|
+------------------------------------------+
|a5cf6c42-0c85-418f-af6c-3e4e5b1328f2      |
+------------------------------------------+

Example 2: Exception in the reflection call, resulting in null

>>> from pyspark.sql import functions as sf
>>> spark.range(1).select(
...     sf.try_reflect(sf.lit("scala.Predef"), sf.lit("require"), sf.lit(False))
... ).show(truncate=False)
+-----------------------------------------+
|try_reflect(scala.Predef, require, false)|
+-----------------------------------------+
|NULL                                     |
+-----------------------------------------+