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https://issues.apache.org/jira/browse/SPARK-23026?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-23026:
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Assignee: Apache Spark (was: Xiao Li)
> Add RegisterUDF to PySpark
> --------------------------
>
> Key: SPARK-23026
> URL: https://issues.apache.org/jira/browse/SPARK-23026
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Xiao Li
> Assignee: Apache Spark
>
> Add a new API for registering row-at-a-time or scalar vectorized UDFs. The
> registered UDFs can be used in the SQL statement.
> {noformat}
> >>> from pyspark.sql.types import IntegerType
> >>> from pyspark.sql.functions import udf
> >>> slen = udf(lambda s: len(s), IntegerType())
> >>> _ = spark.udf.registerUDF("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
> >>> random_udf = udf(lambda: random.randint(0, 100),
> >>> IntegerType()).asNondeterministic()
> >>> newRandom_udf = spark.catalog.registerUDF("random_udf", random_udf)
> >>> spark.sql("SELECT random_udf()").collect()
> [Row(random_udf()=82)]
> >>> spark.range(1).select(newRandom_udf()).collect()
> [Row(random_udf()=62)]
> >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
> >>> @pandas_udf("integer", PandasUDFType.SCALAR)
> ... def add_one(x):
> ... return x + 1
> ...
> >>> _ = spark.udf.registerUDF("add_one", add_one)
> >>> spark.sql("SELECT add_one(id) FROM range(10)").collect()
> {noformat}
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