Jakub created SPARK-23177: ----------------------------- Summary: PySpark parameter-less UDFs raise exception if applied after distinct Key: SPARK-23177 URL: https://issues.apache.org/jira/browse/SPARK-23177 Project: Spark Issue Type: Bug Components: PySpark Affects Versions: 2.2.1, 2.2.0, 2.1.2 Reporter: Jakub
It seems there is an issue with UDFs that take no arguments, but only if UDF is applied after {{distinct()}} operation. Here is the short example, that allows reproduce an issue in PySpark shell: {code:java} import pyspark.sql.functions as f import uuid df = spark.createDataFrame([(1,2), (3,4)]) f_udf = f.udf(lambda: str(uuid.uuid4())) df.distinct().withColumn("a", f_udf()).show() {code} and it raises the following exception: {noformat} Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/opt/spark/python/pyspark/sql/dataframe.py", line 336, in show print(self._jdf.showString(n, 20)) File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__ File "/opt/spark/python/pyspark/sql/utils.py", line 63, in deco return f(*a, **kw) File "/opt/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value py4j.protocol.Py4JJavaError: An error occurred while calling o54.showString. : org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#16 at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306) at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187) at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87) at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:475) at org.apache.spark.sql.execution.aggregate.HashAggregateExec$$anonfun$33.apply(HashAggregateExec.scala:474) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.AbstractTraversable.map(Traversable.scala:104) at org.apache.spark.sql.execution.aggregate.HashAggregateExec.generateResultCode(HashAggregateExec.scala:474) at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduceWithKeys(HashAggregateExec.scala:612) at org.apache.spark.sql.execution.aggregate.HashAggregateExec.doProduce(HashAggregateExec.scala:148) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:85) at org.apache.spark.sql.execution.CodegenSupport$$anonfun$produce$1.apply(WholeStageCodegenExec.scala:80) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135) at org.apache.spark.sql.execution.CodegenSupport$class.produce(WholeStageCodegenExec.scala:80) at org.apache.spark.sql.execution.aggregate.HashAggregateExec.produce(HashAggregateExec.scala:38) at org.apache.spark.sql.execution.WholeStageCodegenExec.doCodeGen(WholeStageCodegenExec.scala:331) at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:372) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:117) at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:138) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:135) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:116) at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:228) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:311) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2861) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2150) at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2842) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2841) at org.apache.spark.sql.Dataset.head(Dataset.scala:2150) at org.apache.spark.sql.Dataset.take(Dataset.scala:2363) at org.apache.spark.sql.Dataset.showString(Dataset.scala:241) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) at py4j.Gateway.invoke(Gateway.java:280) at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) at py4j.commands.CallCommand.execute(CallCommand.java:79) at py4j.GatewayConnection.run(GatewayConnection.java:214) at java.lang.Thread.run(Thread.java:748) Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#16 in [_1#0L,_2#1L] at scala.sys.package$.error(package.scala:27) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94) at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88) at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52) ... 63 more {noformat} It is also worth to mention, that the same code without {{distinct}} does not cause an error. Furthermore, if the UDF takes at least one argument then an exception is not raised as well. -- This message was sent by Atlassian JIRA (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org