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Li Jin commented on SPARK-24721: -------------------------------- I think the issue is the UDF is being pushed down to the PartitionFilter in FileScan physical node (this is the Spark plan before execution the ExtractPythonUDFs rule): {code:java} Project [_c0#17, <lambda>(1) AS v1#20] +- Filter (<lambda>(1) = 0) +- FileScan csv [_c0#17] Batched: false, Format: CSV, Location: InMemoryFileIndex[file:/tmp/tab3], PartitionFilters: [(<lambda>(1) = 0)], PushedFilters: [], ReadSchema: struct<_c0:string> {code} I am not familiar with how PartiionFilters pushdown is supposed to work. [~smilegator] and [~cloud_fan] could you guys maybe point me to the right direction? Should we not push down the filter <lambda(1) = 0> to FileScan? Or should we ignore it in the ExtractPythonUDFs rule? > Failed to call PythonUDF whose input is the output of another PythonUDF > ----------------------------------------------------------------------- > > Key: SPARK-24721 > URL: https://issues.apache.org/jira/browse/SPARK-24721 > Project: Spark > Issue Type: Sub-task > Components: PySpark > Affects Versions: 2.3.1 > Reporter: Xiao Li > Priority: Major > > {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} -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: 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