Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/20171#discussion_r161592401
--- Diff: python/pyspark/sql/catalog.py ---
@@ -256,27 +258,58 @@ def registerFunction(self, name, f,
returnType=StringType()):
>>> spark.sql("SELECT stringLengthInt('test')").collect()
[Row(stringLengthInt(test)=4)]
+ >>> from pyspark.sql.types import IntegerType
+ >>> from pyspark.sql.functions import udf
+ >>> slen = udf(lambda s: len(s), IntegerType())
+ >>> _ = spark.udf.register("slen", slen)
+ >>> spark.sql("SELECT slen('test')").collect()
+ [Row(slen(test)=4)]
+
>>> import random
>>> from pyspark.sql.functions import udf
- >>> from pyspark.sql.types import IntegerType, StringType
+ >>> from pyspark.sql.types import IntegerType
>>> random_udf = udf(lambda: random.randint(0, 100),
IntegerType()).asNondeterministic()
- >>> newRandom_udf = spark.catalog.registerFunction("random_udf",
random_udf, StringType())
+ >>> newRandom_udf = spark.udf.register("random_udf", random_udf)
>>> spark.sql("SELECT random_udf()").collect() # doctest: +SKIP
- [Row(random_udf()=u'82')]
+ [Row(random_udf()=82)]
>>> spark.range(1).select(newRandom_udf()).collect() # doctest:
+SKIP
- [Row(random_udf()=u'62')]
+ [Row(<lambda>()=26)]
+
+ >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
+ >>> @pandas_udf("integer", PandasUDFType.SCALAR) # doctest: +SKIP
+ ... def add_one(x):
+ ... return x + 1
+ ...
+ >>> _ = spark.udf.register("add_one", add_one) # doctest: +SKIP
--- End diff --
This is to avoid generating the random hex value returned by PySpark. You
can try `spark.udf.register("add_one", add_one)`
With the underscore placeholder, we can remove `# doctest: +SKIP`
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