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https://issues.apache.org/jira/browse/SPARK-22978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiao Li updated SPARK-22978:
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Description:
Capable of registering vectorized UDFs and then use it in SQL statement.
For example,
{noformat}
>>> import random
>>> from pyspark.sql.types import IntegerType
>>> from pyspark.sql.functions import pandas_udf
>>> random_pandas_udf = pandas_udf(
... lambda x: random.randint(0, 100) + x, IntegerType())
... .asNondeterministic() # doctest: +SKIP
>>> _ = spark.catalog.registerFunction(
... "random_pandas_udf", random_pandas_udf, IntegerType()) # doctest: +SKIP
>>> spark.sql("SELECT random_pandas_udf(2)").collect() # doctest: +SKIP
[Row(random_pandas_udf(2)=84)]
{noformat}
was:Capable of registering vectorized UDFs and then use it in SQL statement
> Register Vectorized UDFs for SQL Statement
> ------------------------------------------
>
> Key: SPARK-22978
> URL: https://issues.apache.org/jira/browse/SPARK-22978
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 2.3.0
> Reporter: Xiao Li
> Assignee: Xiao Li
>
> Capable of registering vectorized UDFs and then use it in SQL statement.
> For example,
> {noformat}
> >>> import random
> >>> from pyspark.sql.types import IntegerType
> >>> from pyspark.sql.functions import pandas_udf
> >>> random_pandas_udf = pandas_udf(
> ... lambda x: random.randint(0, 100) + x, IntegerType())
> ... .asNondeterministic() # doctest: +SKIP
> >>> _ = spark.catalog.registerFunction(
> ... "random_pandas_udf", random_pandas_udf, IntegerType()) # doctest:
> +SKIP
> >>> spark.sql("SELECT random_pandas_udf(2)").collect() # doctest: +SKIP
> [Row(random_pandas_udf(2)=84)]
> {noformat}
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