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Posted to issues@spark.apache.org by "Wenchen Fan (JIRA)" <ji...@apache.org> on 2018/08/28 02:59:00 UTC
[jira] [Assigned] (SPARK-24721) Failed to use PythonUDF with
literal inputs in filter with data sources
[ https://issues.apache.org/jira/browse/SPARK-24721?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wenchen Fan reassigned SPARK-24721:
-----------------------------------
Assignee: Li Jin
> Failed to use PythonUDF with literal inputs in filter with data sources
> -----------------------------------------------------------------------
>
> Key: SPARK-24721
> URL: https://issues.apache.org/jira/browse/SPARK-24721
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Affects Versions: 2.3.1
> Reporter: Xiao Li
> Assignee: Li Jin
> Priority: Major
> Fix For: 2.4.0
>
>
> {code}
> import random
> from pyspark.sql.functions import *
> from pyspark.sql.types import *
> def random_probability(label):
> if label == 1.0:
> return random.uniform(0.5, 1.0)
> else:
> return random.uniform(0.0, 0.4999)
> def randomize_label(ratio):
>
> if random.random() >= ratio:
> return 1.0
> else:
> return 0.0
> random_probability = udf(random_probability, DoubleType())
> randomize_label = udf(randomize_label, DoubleType())
> spark.range(10).write.mode("overwrite").format('csv').save("/tmp/tab3")
> babydf = spark.read.csv("/tmp/tab3")
> data_modified_label = babydf.withColumn(
> 'random_label', randomize_label(lit(1 - 0.1))
> )
> data_modified_random = data_modified_label.withColumn(
> 'random_probability',
> random_probability(col('random_label'))
> )
> data_modified_label.filter(col('random_label') == 0).show()
> {code}
> The above code will generate the following exception:
> {code}
> Py4JJavaError: An error occurred while calling o446.showString.
> : java.lang.RuntimeException: Invalid PythonUDF randomize_label(0.9), requires attributes from more than one child.
> at scala.sys.package$.error(package.scala:27)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:166)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract$2.apply(ExtractPythonUDFs.scala:165)
> at scala.collection.immutable.Stream.foreach(Stream.scala:594)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:165)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:116)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:112)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:77)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:327)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:325)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:327)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:325)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:327)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:325)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:327)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:208)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:325)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:112)
> at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:92)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:119)
> at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:119)
> at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
> at scala.collection.immutable.List.foldLeft(List.scala:84)
> at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:119)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:109)
> at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:109)
> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3016)
> at org.apache.spark.sql.Dataset.head(Dataset.scala:2216)
> at org.apache.spark.sql.Dataset.take(Dataset.scala:2429)
> at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:380)
> at py4j.Gateway.invoke(Gateway.java:293)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:226)
> at java.lang.Thread.run(Thread.java:748)
> {code}
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